Ubuntu 22.04 Server Benchmarks

AMD EPYC 7713 64-Core testing with a AMD DAYTONA_X (RYM1009B BIOS) and ASPEED on Ubuntu 22.04 via the Phoronix Test Suite.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2209132-NE-UBUNTU22004
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EPYC 7713 2P
September 08 2022
  1 Day, 15 Hours, 49 Minutes
EPYC 7713
September 11 2022
  1 Day, 16 Hours, 26 Minutes
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  1 Day, 16 Hours, 7 Minutes
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EPYC 7713 2P

Processor: 2 x AMD EPYC 7713 64-Core @ 2.00GHz (128 Cores / 256 Threads), Motherboard: AMD DAYTONA_X (RYM1009B BIOS), Chipset: AMD Starship/Matisse, Memory: 512GB, Disk: 3841GB Micron_9300_MTFDHAL3T8TDP, Graphics: ASPEED, Monitor: VE228, Network: 2 x Mellanox MT27710

OS: Ubuntu 22.04, Kernel: 5.15.0-47-generic (x86_64), Desktop: GNOME Shell 42.4, Display Server: X Server 1.21.1.3, Vulkan: 1.2.204, Compiler: GCC 11.2.0, File-System: ext4, Screen Resolution: 1920x1080

Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-11-gBFGDP/gcc-11-11.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-gBFGDP/gcc-11-11.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-build-config=bootstrap-lto-lean --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v
Processor Notes: Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xa001173
Java Notes: OpenJDK Runtime Environment (build 11.0.16+8-post-Ubuntu-0ubuntu122.04)
Python Notes: Python 3.10.4
Security Notes: itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl and seccomp + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional IBRS_FW STIBP: always-on RSB filling + srbds: Not affected + tsx_async_abort: Not affected

EPYC 7713

Changed Processor to AMD EPYC 7713 64-Core @ 2.00GHz (64 Cores / 128 Threads).

Changed Memory to 256GB.

Ubuntu 22.04 Server BenchmarksProcessorMotherboardChipsetMemoryDiskGraphicsMonitorNetworkOSKernelDesktopDisplay ServerVulkanCompilerFile-SystemScreen ResolutionEPYC 7713 2PEPYC 77132 x AMD EPYC 7713 64-Core @ 2.00GHz (128 Cores / 256 Threads)AMD DAYTONA_X (RYM1009B BIOS)AMD Starship/Matisse512GB3841GB Micron_9300_MTFDHAL3T8TDPASPEEDVE2282 x Mellanox MT27710Ubuntu 22.045.15.0-47-generic (x86_64)GNOME Shell 42.4X Server 1.21.1.31.2.204GCC 11.2.0ext41920x1080AMD EPYC 7713 64-Core @ 2.00GHz (64 Cores / 128 Threads)256GBOpenBenchmarking.orgKernel Details- Transparent Huge Pages: madviseCompiler Details- --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-11-gBFGDP/gcc-11-11.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-gBFGDP/gcc-11-11.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-build-config=bootstrap-lto-lean --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v Processor Details- Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xa001173Java Details- OpenJDK Runtime Environment (build 11.0.16+8-post-Ubuntu-0ubuntu122.04)Python Details- Python 3.10.4Security Details- itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl and seccomp + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional IBRS_FW STIBP: always-on RSB filling + srbds: Not affected + tsx_async_abort: Not affected

EPYC 7713 2P vs. EPYC 7713 ComparisonPhoronix Test SuiteBaseline+99.2%+99.2%+198.4%+198.4%+297.6%+297.6%+396.8%+396.8%396.9%366.9%347.8%343.4%342.1%339.8%339.3%310%215.8%155.9%152%150.4%138.9%131%124.3%120.9%119.7%116.7%116.7%113.2%112.8%110%107.7%106.3%100%99.1%92.6%92.1%92.1%91.6%91.5%91.1%91.1%89.4%88.7%88.1%85.6%76.4%75.4%69.3%62.9%62%61.1%56.8%53.1%45.8%45.1%37.6%31.5%30.8%30.5%29.9%28.8%28.4%26.4%26%24.4%24.3%23.8%22.5%21.9%19.4%19.2%18.9%18.8%17.7%17.6%14.4%14.3%14.3%13.9%12%9.1%9%9%8.2%8%7.7%7.3%6.7%6.6%6.4%6%4.8%4.6%3.8%3.8%3.8%3.8%3.4%3.4%3.2%3%2.9%2.9%2.9%2.7%2.6%2.6%2.5%2.5%2.5%2.3%2.2%2.2%2.1%2.1%2%50 - 1:550 - 1:1V.P.M364.5%50 - 5:1100 - 250 - Read Write - Average Latency100 - 250 - Read Write100 - 500 - Read Write - Average Latency100 - 500 - Read Write2048SpaceshipR.N.N.T - f32 - CPUR.N.N.T - u8s8f32 - CPUR.N.N.T - bf16bf16bf16 - CPUM.M.B.S.T - u8s8f32 - CPUR.N.N.I - bf16bf16bf16 - CPUd.S.M.S - Execution Time125%SP.C124.4%R.N.N.I - f32 - CPUMaterial TesterR.N.N.I - u8s8f32 - CPURANGE - 500 - 100 - Average LatencyPUT - 500 - 100 - Average LatencyRANGE - 500 - 100RANGE - 100 - 100d.L.M.S - Execution Time112.3%PUT - 500 - 100RANGE - 100 - 100 - Average LatencyPUT - 100 - 100X.b.i.i102.8%sedovbig101.3%CoreMark Size 666 - I.P.S100.7%PUT - 100 - 100 - Average LatencytConvolve MPI - Gridding99.3%A.G.R.R.0.F.I - CPURand Read98.4%RSA409698.3%RSA409698.3%CPU98.1%SHA25697.7%Exhaustive97.5%tConvolve MPI - Degridding96.2%F.D.F - CPU96.1%conus 2.5km96%EP.D95.1%14 digit94.8%94.2%Basic - CPU93.6%256 - 256 - 5793.4%5001e1392.4%FT.C92.4%P.D.F - CPU92.2%RANGE - 500 - 1000P.D.F - CPU92.1%PUT - 500 - 1000Thorough92%Time To Solve91.9%PUT - 500 - 1000 - Average LatencyRANGE - 500 - 1000 - Average LatencyLU.C91.2%V.D.F - CPU91.1%PUT - 100 - 1000 - Average LatencyPUT - 100 - 10001 - 4K - 32 - Path Tracer90.4%90%3 - 4K - 32 - Path Tracer89.5%RANGE - 100 - 10002 - 4K - 32 - Path Tracer89.4%CG.C89.3%EP.C89.2%88.8%RANGE - 100 - 1000 - Average LatencyClassroom - CPU-Only88.7%P.V.B.D.F - CPU87.9%128 - 256 - 5787.9%M.T.E.T.D.F - CPU87.5%Total Time87.1%F.D.F.I - CPU86.4%V.D.F.I - CPU85.7%1000W.P.D.F.I - CPU85.2%W.P.D.F - CPU84.9%BT.C84.2%83.9%1.H.M.2.D79.3%D.R78.8%Pabellon Barcelona - CPU-Only78.7%1 - 4K - 1 - Path Tracer78.6%1 - 4K - 16 - Path Tracer78.6%3 - 4K - 16 - Path Tracer77.8%3 - 4K - 1 - Path Tracer77.5%Monero - 1M77.4%IS.D77.1%BMW27 - CPU-Only77%40962 - 4K - 16 - Path Tracer76.1%MG.C75.7%ArcFace ResNet-100 - CPU - Standard2 - 4K - 1 - Path Tracer75.3%Fishy Cat - CPU-Only74.6%Barbershop - CPU-Only74.5%Pathtracer ISPC - Crown74.4%OpenMP LavaMD74.1%Pathtracer - Crown72.9%leblancbig72.4%ATPase Simulation - 327,506 Atoms70.2%allmodconfig69.9%Q.1.C.E.5Carbon Nanotube63.4%H.C.OL.E.HRT.hdr_alb_nrm.3840x216061.4%A.G.R.R.0.F - CPUMedium60.9%RT.ldr_alb_nrm.3840x216060.3%MPI CPU - water_GMX50_bare60.1%Read While Writing58.6%SP.B58.6%F.H.RS.F.P.R53.6%100 - 250 - Read Only - Average Latency53.2%Savina Reactors.IO100 - 250 - Read Only52.8%Sharpen52.4%1 - Bosphorus 4K52.2%Ninja51.9%Orange Juice - CPU49%Bosphorus 4K - Very Fast47.8%100 - 500 - Read Only - Average Latency47.2%Compression Rating47.2%DLSC - CPU46.8%100 - 500 - Read Only46.8%C.B.S.A - f32 - CPU45.9%GET - 1000super-resolution-10 - CPU - StandardTime To Compile44.5%Enhanced42.8%Trace Time40.7%RAM / Memorydefconfig36.3%UASTC Level 334.9%3 - D.SALS Movie Lens9 - D.SA.S.PQ.9.C.E.729.2%1000Allfcn-resnet101-11 - CPU - Standard28.1%Time To Compile27.2%yolov4 - CPU - Standard500R.C.a.P - CPUBosphorus 4KS.C.c.j64 - 256 - 5723.8%I.M.D.SUnix Makefiles22.3%A.G.R.R.0.F - CPU22.2%R.R.W.RDisney Material21.4%UASTC Level 221%Danish Mood - CPU21%2620.6%WritesHWB Color SpaceGPT-2 - CPU - Standard2618.8%19, Long Mode - Compression SpeedPathtracer ISPC - Asian Dragon18.2%Speed 9 Realtime - Bosphorus 4KUpdate RandA.G.R.R.0.F.I - CPU16.2%Wownero - 1M15.1%Q.7.C.E.714.6%d.L.M.S - Mesh Time14.5%Speed 5 - Bosphorus 4K10 - Bosphorus 4KBLAS14.3%TradebeansC240 Buckyball14.1%EmilyTime To Compile13.9%Eigen13.8%Small12.8%Speed 10 Realtime - Bosphorus 4Kd.S.M.S - Mesh Time11.9%LuxCore Benchmark - CPU11.5%Time To Compile11%Time To Compile10.8%Pathtracer - Asian Dragon9.4%scikit_qdaOpenMP LeukocyteApache Spark BayesOpenMP CFD Solver8.9%W.P.D.F - CPUW.P.D.F.I - CPUDefault7.8%V.D.F.I - CPUF.D.F.I - CPUbertsquad-12 - CPU - StandardM.T.E.T.D.F - CPUP.V.B.D.F - CPUPreset 10 - Bosphorus 4KTime To Compile5.3%7 - Bosphorus 4K5.1%OFDM_TestV.D.F - CPUP.D.F - CPUP.P.BJPEG - 7P.D.F - CPUTime To Compile3.7%Bosphorus 4K - Ultra Fast3.5%PNG - 7OPTIONS, StatefulG.A.U.J.F263.2%10, LosslessPreset 8 - Bosphorus 4K6Preset 12 - Bosphorus 4K262.7%9 - Compression SpeedCPU - MobileNet v2UASTC Level 0scikit_icaC75523 - Compression SpeedS.C.m.j2.3%RotateLion19 - D.S2.1%Time To Compile6, LosslessDefault2.1%PNG - 8DragonflydbDragonflydbBRL-CADDragonflydbPostgreSQL pgbenchPostgreSQL pgbenchPostgreSQL pgbenchPostgreSQL pgbenchMariaDBNatrononeDNNoneDNNoneDNNoneDNNoneDNNOpenFOAMNAS Parallel BenchmarksoneDNNAppleseedoneDNNetcdetcdetcdetcdOpenFOAMetcdetcdetcdXcompact3d Incompact3dPennantCoremarketcdASKAPOpenVINOFacebook RocksDBOpenSSLOpenSSLSysbenchOpenSSLASTC EncoderASKAPOpenVINOWRFNAS Parallel BenchmarksHelsingHigh Performance Conjugate GradientRELIONLiquid-DSPnginxPrimesieveNAS Parallel BenchmarksOpenVINOetcdOpenVINOetcdASTC Encoderm-queensetcdetcdNAS Parallel BenchmarksOpenVINOetcdetcdOSPRay StudioAlgebraic Multi-Grid BenchmarkOSPRay StudioetcdOSPRay StudioNAS Parallel BenchmarksNAS Parallel BenchmarksLULESHetcdBlenderKripkeOpenVINOLiquid-DSPOpenVINOStockfishOpenVINOOpenVINOnginxOpenVINOOpenVINONAS Parallel BenchmarksebizzyasmFish7-Zip CompressionBlenderOSPRay StudioOSPRay StudioOSPRay StudioOSPRay StudioXmrigNAS Parallel BenchmarksBlenderMariaDBOSPRay StudioNAS Parallel BenchmarksONNX RuntimeOSPRay StudioBlenderBlenderEmbreeRodiniaEmbreePennantNAMDTimed Linux Kernel CompilationWebP2 Image EncodeGPAWASKAPCloverLeafIntel Open Image DenoiseOpenVINOASTC EncoderIntel Open Image DenoiseGROMACSFacebook RocksDBNAS Parallel BenchmarksRenaissanceACES DGEMMPostgreSQL pgbenchRenaissancePostgreSQL pgbenchGraphicsMagickSVT-HEVCTimed LLVM CompilationLuxCoreRenderKvazaarPostgreSQL pgbench7-Zip CompressionLuxCoreRenderPostgreSQL pgbenchoneDNNRedisONNX RuntimeTimed Node.js CompilationGraphicsMagickPOV-RaySysbenchTimed Linux Kernel CompilationBasis UniversalLZ4 CompressionRenaissanceLZ4 CompressionRenaissanceWebP2 Image EncodeApache HTTP ServerJPEG XL Decoding libjxlONNX RuntimeTimed FFmpeg CompilationONNX RuntimeApache HTTP ServerLuxCoreRenderx265SPECjbb 2015Liquid-DSPRenaissanceTimed LLVM CompilationOpenVINOFacebook RocksDBAppleseedBasis UniversalLuxCoreRenderGraph500Apache CassandraGraphicsMagickONNX RuntimeGraph500Zstd CompressionEmbreeAOM AV1Facebook RocksDBOpenVINOXmrigWebP2 Image EncodeOpenFOAMVP9 libvpx EncodingSVT-HEVCLeelaChessZeroDaCapo BenchmarkNWChemAppleseedTimed Mesa CompilationLeelaChessZerominiFEAOM AV1OpenFOAMLuxCoreRenderTimed Gem5 CompilationBuild2EmbreeMlpack BenchmarkRodiniaRenaissanceRodiniaOpenVINOOpenVINOWebP2 Image EncodeOpenVINOOpenVINOONNX RuntimeOpenVINOOpenVINOSVT-AV1Timed Godot Game Engine CompilationSVT-HEVCsrsRANOpenVINOOpenVINOLibRawJPEG XL libjxlOpenVINOTimed PHP CompilationKvazaarJPEG XL libjxlPJSIPRenaissanceGraph500libavif avifencSVT-AV1libavif avifencSVT-AV1Node.js Express HTTP Load TestGraph500LZ4 CompressionTNNBasis UniversalMlpack BenchmarkNgspiceLZ4 CompressionSPECjbb 2015GraphicsMagickGoogle DracoZstd CompressionTimed Apache Compilationlibavif avifencTimed CPython CompilationJPEG XL libjxlEPYC 7713 2PEPYC 7713

Ubuntu 22.04 Server Benchmarkswrf: conus 2.5kmopenfoam: drivaerFastback, Large Mesh Size - Execution Timeopenfoam: drivaerFastback, Large Mesh Size - Mesh Timespec-jbb2015: SPECjbb2015-Composite critical-jOPSspec-jbb2015: SPECjbb2015-Composite max-jOPSmysqlslap: 4096mysqlslap: 2048nwchem: C240 Buckyballrenaissance: ALS Movie Lensrelion: Basic - CPUbrl-cad: VGR Performance Metricincompact3d: X3D-benchmarking input.i3drodinia: OpenMP HotSpot3Dstockfish: Total Timelczero: Eigenlczero: BLASrenaissance: Savina Reactors.IOqe: AUSURF112onednn: Recurrent Neural Network Inference - bf16bf16bf16 - CPUpgbench: 100 - 250 - Read Write - Average Latencypgbench: 100 - 250 - Read Writeasmfish: 1024 Hash Memory, 26 Depthpgbench: 100 - 500 - Read Write - Average Latencypgbench: 100 - 500 - Read Writerenaissance: Apache Spark PageRankgraph500: 26graph500: 26graph500: 26graph500: 26onnx: ArcFace ResNet-100 - CPU - Standardonnx: fcn-resnet101-11 - CPU - Standardonnx: yolov4 - CPU - Standardonnx: super-resolution-10 - CPU - Standardjpegxl: PNG - 8securemark: SecureMark-TLShpcg: luaradio: Complex Phaseluaradio: Hilbert Transformluaradio: FM Deemphasis Filterluaradio: Five Back to Back FIR Filtersblender: Barbershop - CPU-Onlyonednn: Recurrent Neural Network Inference - u8s8f32 - CPUmlpack: scikit_qdabuild-linux-kernel: allmodconfigtnn: CPU - DenseNetopenfoam: drivaerFastback, Small Mesh Size - Execution Timeopenfoam: drivaerFastback, Small Mesh Size - Mesh Timebuild-llvm: Unix Makefilesnumpy: luxcorerender: Danish Mood - CPUluxcorerender: LuxCore Benchmark - CPUcompress-lz4: 3 - Decompression Speedcompress-lz4: 3 - Compression Speedopenssl: SHA256build-gem5: Time To Compileappleseed: Material Testerwebp2: Quality 95, Compression Effort 7ospray-studio: 3 - 4K - 32 - Path Tracerbuild-nodejs: Time To Compilepgbench: 100 - 500 - Read Only - Average Latencypgbench: 100 - 500 - Read Onlypgbench: 100 - 250 - Read Only - Average Latencypgbench: 100 - 250 - Read Onlyospray-studio: 3 - 4K - 16 - Path Tracerngspice: C2670build-llvm: Ninjavpxenc: Speed 5 - Bosphorus 4Kluxcorerender: Orange Juice - CPUcassandra: Writesclickhouse: 100M Rows Web Analytics Dataset, Third Runclickhouse: 100M Rows Web Analytics Dataset, Second Runclickhouse: 100M Rows Web Analytics Dataset, First Run / Cold Cacheospray-studio: 3 - 4K - 1 - Path Tracerospray-studio: 2 - 4K - 32 - Path Traceronnx: GPT-2 - CPU - Standardonnx: bertsquad-12 - CPU - Standardospray-studio: 1 - 4K - 32 - Path Tracerospray-studio: 2 - 4K - 1 - Path Tracerospray-studio: 2 - 4K - 16 - Path Tracerospray-studio: 1 - 4K - 1 - Path Tracerospray-studio: 1 - 4K - 16 - Path Tracerrenaissance: Genetic Algorithm Using Jenetics + Futuresnpb: EP.Drenaissance: In-Memory Database Shootoutrenaissance: Finagle HTTP Requestsngspice: C7552etcd: RANGE - 100 - 100 - Average Latencyetcd: RANGE - 100 - 100onednn: Recurrent Neural Network Training - bf16bf16bf16 - CPUjpegxl: PNG - 7appleseed: Emilyopenvino: Face Detection FP16 - CPUopenvino: Face Detection FP16 - CPUsimdjson: DistinctUserIDsimdjson: TopTweetapache: 1000helsing: 14 digitsimdjson: PartialTweetsapache: 500nginx: 500nginx: 1000sysbench: CPUbuild-python: Released Build, PGO + LTO Optimizedetcd: PUT - 100 - 100 - Average Latencyetcd: PUT - 100 - 100onednn: Recurrent Neural Network Training - f32 - CPUonednn: Recurrent Neural Network Training - u8s8f32 - CPUetcd: RANGE - 500 - 100 - Average Latencyetcd: RANGE - 500 - 100etcd: PUT - 500 - 100 - Average Latencyetcd: PUT - 500 - 100etcd: PUT - 100 - 1000 - Average Latencyetcd: PUT - 100 - 1000etcd: RANGE - 100 - 1000 - Average Latencyetcd: RANGE - 100 - 1000ebizzy: node-web-tooling: onednn: Recurrent Neural Network Inference - f32 - CPUetcd: PUT - 500 - 1000 - Average Latencyetcd: PUT - 500 - 1000etcd: RANGE - 500 - 1000 - Average Latencyetcd: RANGE - 500 - 1000blender: Pabellon Barcelona - CPU-Onlyopenvino: Person Detection FP16 - CPUopenvino: Person Detection FP16 - CPUcompress-lz4: 9 - Decompression Speedcompress-lz4: 9 - Compression Speedopenvino: Person Detection FP32 - CPUopenvino: Person Detection FP32 - CPUdragonflydb: 50 - 1:5dragonflydb: 50 - 1:1dragonflydb: 50 - 5:1openvino: Face Detection FP16-INT8 - CPUopenvino: Face Detection FP16-INT8 - CPUpyperformance: python_startuppjsip: INVITEpjsip: OPTIONS, Statefulopenvino: Machine Translation EN To DE FP16 - CPUopenvino: Machine Translation EN To DE FP16 - CPUopenvino: Person Vehicle Bike Detection FP16 - CPUopenvino: Person Vehicle Bike Detection FP16 - CPUluxcorerender: DLSC - CPUopenvino: Vehicle Detection FP16-INT8 - CPUopenvino: Vehicle Detection FP16-INT8 - CPUopenvino: Vehicle Detection FP16 - CPUopenvino: Vehicle Detection FP16 - CPUopenvino: Age Gender Recognition Retail 0013 FP16-INT8 - CPUopenvino: Age Gender Recognition Retail 0013 FP16-INT8 - CPUopenvino: Weld Porosity Detection FP16-INT8 - CPUopenvino: Weld Porosity Detection FP16-INT8 - CPUopenvino: Age Gender Recognition Retail 0013 FP16 - CPUopenvino: Age Gender Recognition Retail 0013 FP16 - CPUopenvino: Weld Porosity Detection FP16 - CPUopenvino: Weld Porosity Detection FP16 - CPUrocksdb: Update Randrocksdb: Read Rand Write Randrocksdb: Read While Writinggraphics-magick: Sharpenopenssl: RSA4096openssl: RSA4096graphics-magick: Enhancedrocksdb: Rand Readgraphics-magick: Rotategraphics-magick: HWB Color Spaceetcpak: Single-Threaded - ETC2coremark: CoreMark Size 666 - Iterations Per Secondblender: Classroom - CPU-Onlygpaw: Carbon Nanotubecloverleaf: Lagrangian-Eulerian Hydrodynamicssimdjson: Kostyabuild2: Time To Compileaom-av1: Speed 10 Realtime - Bosphorus 4Knatron: Spaceshiprodinia: OpenMP Leukocytesvt-hevc: 1 - Bosphorus 4Kbuild-wasmer: Time To Compilesimdjson: LargeRandsrsran: 4G PHY_DL_Test 100 PRB SISO 64-QAMsrsran: 4G PHY_DL_Test 100 PRB SISO 64-QAMminife: Smallluxcorerender: Rainbow Colors and Prism - CPUmlpack: scikit_icaaskap: tConvolve MPI - Griddingaskap: tConvolve MPI - Degriddingwebp2: Quality 75, Compression Effort 7compress-zstd: 19 - Decompression Speedcompress-zstd: 19 - Compression Speedcompress-zstd: 19, Long Mode - Decompression Speedcompress-zstd: 19, Long Mode - Compression Speedbuild-linux-kernel: defconfigcompress-7zip: Decompression Ratingcompress-7zip: Compression Ratingkripke: jpegxl-decode: 1webp: Quality 100, Lossless, Highest Compressionbuild-godot: Time To Compileprimesieve: 1e13srsran: 4G PHY_DL_Test 100 PRB MIMO 256-QAMsrsran: 4G PHY_DL_Test 100 PRB MIMO 256-QAMavifenc: 2pyperformance: chaosgromacs: MPI CPU - water_GMX50_barebuild-php: Time To Compileredis: GET - 1000amg: appleseed: Disney Materialkvazaar: Bosphorus 4K - Ultra Fastrodinia: OpenMP LavaMDsrsran: OFDM_Testsrsran: 4G PHY_DL_Test 100 PRB MIMO 64-QAMsrsran: 4G PHY_DL_Test 100 PRB MIMO 64-QAMembree: Pathtracer - Asian Dragonembree: Pathtracer ISPC - Asian Dragononednn: Matrix Multiply Batch Shapes Transformer - u8s8f32 - CPUpyperformance: regex_compilepjsip: OPTIONS, Statelesslibraw: Post-Processing Benchmarkblender: Fishy Cat - CPU-Onlynpb: IS.Drenaissance: Apache Spark Bayesaircrack-ng: encode-flac: WAV To FLACx265: Bosphorus 4Ksynthmark: VoiceMark_100quantlib: xmrig: Monero - 1Mnamd: ATPase Simulation - 327,506 Atomspennant: leblancbigphpbench: PHP Benchmark Suitejpegxl: JPEG - 7xmrig: Wownero - 1Mmlpack: scikit_svmdacapobench: Tradebeansblender: BMW27 - CPU-Onlylulesh: pyperformance: pickle_pure_pythontnn: CPU - MobileNet v2cython-bench: N-Queensnpb: SP.Cpybench: Total For Average Test Timesnode-express-loadtest: srsran: 5G PHY_DL_NR Test 52 PRB SISO 64-QAMsrsran: 5G PHY_DL_NR Test 52 PRB SISO 64-QAMoidn: RT.hdr_alb_nrm.3840x2160npb: BT.Csvt-hevc: 10 - Bosphorus 4Koidn: RT.ldr_alb_nrm.3840x2160build-apache: Time To Compileaskap: Hogbom Clean OpenMPliquid-dsp: 256 - 256 - 57kvazaar: Bosphorus 4K - Very Fastliquid-dsp: 128 - 256 - 57liquid-dsp: 64 - 256 - 57liquid-dsp: 32 - 256 - 57npb: SP.Bsrsran: 4G PHY_DL_Test 100 PRB SISO 256-QAMsrsran: 4G PHY_DL_Test 100 PRB SISO 256-QAMbuild-mesa: Time To Compiletnn: CPU - SqueezeNet v1.1build-ffmpeg: Time To Compilejpegxl-decode: Allbasis: UASTC Level 3sysbench: RAM / Memorynpb: LU.Csvt-av1: Preset 12 - Bosphorus 4Kaom-av1: Speed 9 Realtime - Bosphorus 4Ksvt-av1: Preset 8 - Bosphorus 4Kastcenc: Exhaustivewebp: Quality 100, Losslessembree: Pathtracer ISPC - Crownjpegxl: JPEG - 8povray: Trace Timemt-dgemm: Sustained Floating-Point Ratebasis: UASTC Level 2embree: Pathtracer - Crownastcenc: Thoroughencode-mp3: WAV To MP3draco: Church Facadenpb: CG.Conednn: Convolution Batch Shapes Auto - f32 - CPUpennant: sedovbigm-queens: Time To Solveavifenc: 6, Losslessdraco: Lionwebp: Quality 100, Highest Compressionrodinia: OpenMP CFD Solversvt-av1: Preset 10 - Bosphorus 4Knpb: FT.Cbasis: UASTC Level 0webp2: Defaultoctave-benchmark: dacapobench: Jythonsvt-hevc: 7 - Bosphorus 4Kavifenc: 10, Losslessastcenc: Mediumtnn: CPU - SqueezeNet v2npb: EP.Cetcpak: Multi-Threaded - ETC2avifenc: 6npb: MG.Cwebp2: Quality 100, Compression Effort 5webp: Quality 100webp: Defaultbuild-python: Defaultctx-clock: Context Switch Timeblake2: EPYC 7713 2PEPYC 77138650.8397052.62776.38689461308991401502183.624613.1290.9613240515300.69114289.2802794869424093444912254.2399.922840.0515.0761663324887051935.094142613985.739037700030233800065946700064251600087723733045691.0024958237.1011623.898.4364.31211.6171.522754.4531.84160.4633065.920281.7124.96178.675469.387.507.6310743.254.18134629156527159.015336.5831190.3152785113.4660.24820127220.126198315526510137.362105.03213.8618.60209921396.82387.01377.521646454607878668446241424227421379221582314.99109.246136.410535.4105.9512.737230.32737473.9110.74151.5310713597.4717.594.424.3984767.7362.1293.8491255.7990312.0094018.12500125.79261.3372.638209.42027531.127435.342.638506.60512.638992.815823.741860.482523.442305.618345325810.502840.3322.843696.761822.644040.713553.884777.1513.1010851.452.514774.3713.09724442.87724466.22724635.931388.4145.847.7146928819292.82218.1322.162884.4412.2320.383136.1435.751788.342.1157763.8928.054559.423.2737561.0733.551905.513046542922167145646737791638050.525009.613444801753327301093229.6374105747.13977240.8943.85719.442.953.23257.041.947.28415.0851.8691143.6392.324664.816.9946.4043735.839742.00.553473.084.73485.839.921.58463408651661714395026766.930.5641.16728.718141.0426.141.21395.88.21538.3381364479.13192330666750.46425858.5226.740130333333133.6394.168.875967.173630.01161556761737.9622.254690.06638.1149778.79718.13321.48748.0572808.850749.60.267123.556570726003100.7741982.921.45456817.1936456.229385341.28123.312116527.63953619862.2127.92.26235808.54195.622.2620.521319.756535090000054.24508506666731915333331614266667142675.55149.6427.418.100273.84814.271569.9711.4027260.66259899.49179.55055.6268.7976.44991.4282.926328.927.59032.0712278.77389.600658.47447.490752545587.100.6408335.6758956.3767.33457353.476.283123.806116679.266.3799.706.5454106144.705.558380.646765.8948338.276678.5653.957100740.896.4110.6516.7515.391203.3916959.84814972.97888.82853891279572476152491.118814.1563.271697698609.67747088.019149383689359838938004.9397.641229.563.400735271387761027.980626543067.3323593000254477000642278000622681000153818541766311.0224774719.1088628.998.6365.41189.0299.301253.5029.19272.6093107.102633.93139.89218.445464.566.206.8414123.355.4368101809853176.430152.3942040.24100051164.0010.36513709350.193129768447134136.020159.57515.8612.48250596394.98392.92378.442922860819368713849682496400582463395682243.24668.965009.26720.8103.3871.379219.65092984.4711.11133.043473555.788.974.404.39109150.30120.9993.86115006.99173899.19174524.79252440.57261.3651.378829.58882943.082950.431.282098.41781.281895.962312.479978.190512.480131.795824649410.601266.2011.983927.655811.884602.780396.304601.786.8214156.753.894600.946.813599739.573382696.793244708.361293.5124.597.6446909115274.57116.3320.821535.088.3318.931689.1834.17935.581.0649691.0325.972462.402.0330742.1731.021030.7635833635618419182167511825904.112613.19412420747987461303230.4322045229.64589677.1471.65812.002.958.95663.906.043.3929.9151.9651143.6396.021875.121.1345.2721943.020252.10.483401.684.93460.347.429.40935460835107727072252767.620.5643.35455.255141.4431.840.76696.55.13039.7631990067.33101205166761.26563756.5346.547136633333133.7394.462.981456.809812.56081566660239.4038.852648.96585.6149666.94318.12926.69747.3082795.328612.40.454576.133060733332104.5836467.221.33399830.4319305.656387332.61123.38451921.71959636762.4128.91.40127998.61223.661.4120.100520.840276716666736.7127069000002577666667160556666789950.76149.6430.520.610273.48118.146731.8815.3859990.12135917.43184.71165.4970.7833.26591.4047.558329.2410.68020.88027110.61551.833430.45547.472741124085.730.93470611.4272312.2387.18456133.486.845131.27060659.256.2219.006.5824033137.745.398236.602065.6174406.026702.5183.84657329.7010.8510.6916.7815.7081203.39OpenBenchmarking.org

WRF

WRF, the Weather Research and Forecasting Model, is a "next-generation mesoscale numerical weather prediction system designed for both atmospheric research and operational forecasting applications. It features two dynamical cores, a data assimilation system, and a software architecture supporting parallel computation and system extensibility." Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterWRF 4.2.2Input: conus 2.5kmEPYC 7713 2PEPYC 77134K8K12K16K20K8650.8416959.851. (F9X) gfortran options: -O2 -ftree-vectorize -funroll-loops -ffree-form -fconvert=big-endian -frecord-marker=4 -fallow-invalid-boz -lesmf_time -lwrfio_nf -lnetcdff -lnetcdf -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz
OpenBenchmarking.orgWatts, Fewer Is BetterWRF 4.2.2CPU Power Consumption MonitorEPYC 7713EPYC 7713 2P80160240320400Min: 37.97 / Avg: 213.89 / Max: 233.05Min: 80.92 / Avg: 444.81 / Max: 460.67

OpenFOAM

OpenFOAM is the leading free, open-source software for computational fluid dynamics (CFD). This test profile currently uses the drivaerFastback test case for analyzing automotive aerodynamics or alternatively the older motorBike input. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenFOAM 9Input: drivaerFastback, Large Mesh Size - Execution TimeEPYC 7713 2PEPYC 77133K6K9K12K15K7052.6214972.971. (CXX) g++ options: -std=c++14 -m64 -O3 -ftemplate-depth-100 -fPIC -fuse-ld=bfd -Xlinker --add-needed --no-as-needed -ldynamicMesh -ldecompose -lgenericPatchFields -lmetisDecomp -lscotchDecomp -llagrangian -lregionModels -lOpenFOAM -ldl -lm
OpenBenchmarking.orgWatts, Fewer Is BetterOpenFOAM 9CPU Power Consumption MonitorEPYC 7713EPYC 7713 2P80160240320400Min: 38.09 / Avg: 213.33 / Max: 230.31Min: 80.96 / Avg: 441.98 / Max: 460.58

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenFOAM 9Input: drivaerFastback, Large Mesh Size - Mesh TimeEPYC 7713 2PEPYC 77132004006008001000776.38888.821. (CXX) g++ options: -std=c++14 -m64 -O3 -ftemplate-depth-100 -fPIC -fuse-ld=bfd -Xlinker --add-needed --no-as-needed -ldynamicMesh -ldecompose -lgenericPatchFields -lmetisDecomp -lscotchDecomp -llagrangian -lregionModels -lOpenFOAM -ldl -lm

SPECjbb 2015

This is a benchmark of SPECjbb 2015. For this test profile to work, you must have a valid license/copy of the SPECjbb 2015 ISO (SPECjbb2015-1.02.iso) in your Phoronix Test Suite download cache. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgjOPS, More Is BetterSPECjbb 2015SPECjbb2015-Composite critical-jOPSEPYC 7713EPYC 7713 2P20K40K60K80K100K8538968946
OpenBenchmarking.orgjOPS Per Watt, More Is BetterSPECjbb 2015SPECjbb2015-Composite critical-jOPSEPYC 7713EPYC 7713 2P110220330440550498.68196.90
OpenBenchmarking.orgWatts, Fewer Is BetterSPECjbb 2015CPU Power Consumption MonitorEPYC 7713EPYC 7713 2P80160240320400Min: 37.88 / Avg: 171.23 / Max: 224.72Min: 77.79 / Avg: 350.16 / Max: 445.79

OpenBenchmarking.orgjOPS, More Is BetterSPECjbb 2015SPECjbb2015-Composite max-jOPSEPYC 7713 2PEPYC 771330K60K90K120K150K130899127957

MariaDB

This is a MariaDB MySQL database server benchmark making use of mysqlslap. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgQueries Per Second, More Is BetterMariaDB 10.8.2Clients: 4096EPYC 7713EPYC 7713 2P50100150200250SE +/- 0.15, N = 3SE +/- 0.87, N = 32471401. (CXX) g++ options: -pie -fPIC -fstack-protector -O3 -lnuma -lpcre2-8 -lcrypt -laio -lz -lm -lssl -lcrypto -lpthread -ldl
OpenBenchmarking.orgQueries Per Second Per Watt, More Is BetterMariaDB 10.8.2Clients: 4096EPYC 7713EPYC 7713 2P0.37510.75021.12531.50041.87551.6670.464
OpenBenchmarking.orgWatts, Fewer Is BetterMariaDB 10.8.2CPU Power Consumption MonitorEPYC 7713EPYC 7713 2P70140210280350Min: 37.46 / Avg: 148.13 / Max: 220.4Min: 81.11 / Avg: 301.96 / Max: 383.631. EPYC 7713: Approximate power consumption of 124186 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 440655 Joules per run.

OpenBenchmarking.orgQueries Per Second, More Is BetterMariaDB 10.8.2Clients: 2048EPYC 7713EPYC 7713 2P130260390520650SE +/- 4.69, N = 3SE +/- 0.88, N = 36151501. (CXX) g++ options: -pie -fPIC -fstack-protector -O3 -lnuma -lpcre2-8 -lcrypt -laio -lz -lm -lssl -lcrypto -lpthread -ldl
OpenBenchmarking.orgQueries Per Second Per Watt, More Is BetterMariaDB 10.8.2Clients: 2048EPYC 7713EPYC 7713 2P1.03212.06423.09634.12845.16054.5870.498
MinAvgMaxEPYC 771337.2134.1189.6EPYC 7713 2P80.8300.9371.3OpenBenchmarking.orgWatts, Fewer Is BetterMariaDB 10.8.2CPU Power Consumption Monitor1002003004005001. EPYC 7713: Approximate power consumption of 47013 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 409046 Joules per run.

NWChem

NWChem is an open-source high performance computational chemistry package. Per NWChem's documentation, "NWChem aims to provide its users with computational chemistry tools that are scalable both in their ability to treat large scientific computational chemistry problems efficiently, and in their use of available parallel computing resources from high-performance parallel supercomputers to conventional workstation clusters." Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterNWChem 7.0.2Input: C240 BuckyballEPYC 7713 2PEPYC 771350010001500200025002183.62491.11. (F9X) gfortran options: -lnwctask -lccsd -lmcscf -lselci -lmp2 -lmoints -lstepper -ldriver -loptim -lnwdft -lgradients -lcphf -lesp -lddscf -ldangchang -lguess -lhessian -lvib -lnwcutil -lrimp2 -lproperty -lsolvation -lnwints -lprepar -lnwmd -lnwpw -lofpw -lpaw -lpspw -lband -lnwpwlib -lcafe -lspace -lanalyze -lqhop -lpfft -ldplot -ldrdy -lvscf -lqmmm -lqmd -letrans -ltce -lbq -lmm -lcons -lperfm -ldntmc -lccca -ldimqm -lga -larmci -lpeigs -l64to32 -lopenblas -lpthread -lrt -llapack -lnwcblas -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz -lcomex -m64 -ffast-math -std=legacy -fdefault-integer-8 -finline-functions -O2
OpenBenchmarking.orgWatts, Fewer Is BetterNWChem 7.0.2CPU Power Consumption MonitorEPYC 7713EPYC 7713 2P80160240320400Min: 37.45 / Avg: 228.16 / Max: 230.32Min: 81.09 / Avg: 458.34 / Max: 461.01

Renaissance

Renaissance is a suite of benchmarks designed to test the Java JVM from Apache Spark to a Twitter-like service to Scala and other features. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterRenaissance 0.14Test: ALS Movie LensEPYC 7713EPYC 7713 2P5K10K15K20K25KSE +/- 25.65, N = 3SE +/- 323.63, N = 418814.124613.1MIN: 18778.84 / MAX: 20660.87MIN: 20413.78 / MAX: 30021.19
OpenBenchmarking.orgWatts, Fewer Is BetterRenaissance 0.14CPU Power Consumption MonitorEPYC 7713EPYC 7713 2P50100150200250Min: 37.3 / Avg: 94.8 / Max: 146.06Min: 81.42 / Avg: 198.22 / Max: 255.041. EPYC 7713: Approximate power consumption of 39628 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 113281 Joules per run.

RELION

RELION - REgularised LIkelihood OptimisatioN - is a stand-alone computer program for Maximum A Posteriori refinement of (multiple) 3D reconstructions or 2D class averages in cryo-electron microscopy (cryo-EM). It is developed in the research group of Sjors Scheres at the MRC Laboratory of Molecular Biology. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterRELION 3.1.1Test: Basic - Device: CPUEPYC 7713 2PEPYC 7713120240360480600SE +/- 3.51, N = 4SE +/- 3.66, N = 3290.96563.271. (CXX) g++ options: -fopenmp -std=c++0x -O3 -rdynamic -ldl -ltiff -lfftw3f -lfftw3 -lpng -lmpi_cxx -lmpi
OpenBenchmarking.orgWatts, Fewer Is BetterRELION 3.1.1CPU Power Consumption MonitorEPYC 7713EPYC 7713 2P80160240320400Min: 38.17 / Avg: 220.64 / Max: 230.23Min: 80.71 / Avg: 443.79 / Max: 460.841. EPYC 7713: Approximate power consumption of 124664 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 129808 Joules per run.

BRL-CAD

BRL-CAD is a cross-platform, open-source solid modeling system with built-in benchmark mode. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgVGR Performance Metric, More Is BetterBRL-CAD 7.32.6VGR Performance MetricEPYC 7713 2PEPYC 7713700K1400K2100K2800K3500K32405156976981. (CXX) g++ options: -std=c++11 -pipe -fvisibility=hidden -fno-strict-aliasing -fno-common -fexceptions -ftemplate-depth-128 -m64 -ggdb3 -O3 -fipa-pta -fstrength-reduce -finline-functions -flto -pedantic -ldl -lm
OpenBenchmarking.orgVGR Performance Metric Per Watt, More Is BetterBRL-CAD 7.32.6VGR Performance MetricEPYC 7713 2PEPYC 77134K8K12K16K20K17175.933133.83
OpenBenchmarking.orgWatts, Fewer Is BetterBRL-CAD 7.32.6CPU Power Consumption MonitorEPYC 7713 2PEPYC 771360120180240300Min: 78.76 / Avg: 188.67 / Max: 340.41Min: 37.39 / Avg: 222.63 / Max: 230.51

Xcompact3d Incompact3d

Xcompact3d Incompact3d is a Fortran-MPI based, finite difference high-performance code for solving the incompressible Navier-Stokes equation and as many as you need scalar transport equations. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterXcompact3d Incompact3d 2021-03-11Input: X3D-benchmarking input.i3dEPYC 7713 2PEPYC 7713130260390520650SE +/- 0.29, N = 3SE +/- 1.64, N = 3300.69609.681. (F9X) gfortran options: -cpp -O2 -funroll-loops -floop-optimize -fcray-pointer -fbacktrace -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz
OpenBenchmarking.orgWatts, Fewer Is BetterXcompact3d Incompact3d 2021-03-11CPU Power Consumption MonitorEPYC 7713EPYC 7713 2P80160240320400Min: 37.94 / Avg: 210.48 / Max: 220.44Min: 81.73 / Avg: 436.12 / Max: 448.761. EPYC 7713: Approximate power consumption of 129584 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 133015 Joules per run.

Rodinia

Rodinia is a suite focused upon accelerating compute-intensive applications with accelerators. CUDA, OpenMP, and OpenCL parallel models are supported by the included applications. This profile utilizes select OpenCL, NVIDIA CUDA and OpenMP test binaries at the moment. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterRodinia 3.1Test: OpenMP HotSpot3DEPYC 7713EPYC 7713 2P20406080100SE +/- 1.40, N = 15SE +/- 1.37, N = 1588.0289.281. (CXX) g++ options: -O2 -lOpenCL
OpenBenchmarking.orgWatts, Fewer Is BetterRodinia 3.1CPU Power Consumption MonitorEPYC 7713EPYC 7713 2P60120180240300Min: 37.31 / Avg: 95.61 / Max: 178.35Min: 76.91 / Avg: 184.96 / Max: 331.471. EPYC 7713: Approximate power consumption of 8784 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 17201 Joules per run.

Stockfish

This is a test of Stockfish, an advanced open-source C++11 chess benchmark that can scale up to 512 CPU threads. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgNodes Per Second, More Is BetterStockfish 15Total TimeEPYC 7713 2PEPYC 771360M120M180M240M300MSE +/- 4884547.18, N = 15SE +/- 3372481.12, N = 122794869421493836891. (CXX) g++ options: -lgcov -m64 -lpthread -fno-exceptions -std=c++17 -fno-peel-loops -fno-tracer -pedantic -O3 -msse -msse3 -mpopcnt -mavx2 -msse4.1 -mssse3 -msse2 -mbmi2 -flto -flto=jobserver
OpenBenchmarking.orgNodes Per Second Per Watt, More Is BetterStockfish 15Total TimeEPYC 7713EPYC 7713 2P150K300K450K600K750K678106.61637159.42
MinAvgMaxEPYC 771338.3220.3230.4EPYC 7713 2P80.2438.6460.8OpenBenchmarking.orgWatts, Fewer Is BetterStockfish 15CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 20671 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 47403 Joules per run.

LeelaChessZero

LeelaChessZero (lc0 / lczero) is a chess engine automated vian neural networks. This test profile can be used for OpenCL, CUDA + cuDNN, and BLAS (CPU-based) benchmarking. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgNodes Per Second, More Is BetterLeelaChessZero 0.28Backend: EigenEPYC 7713 2PEPYC 77139001800270036004500SE +/- 42.95, N = 4SE +/- 38.17, N = 3409335981. (CXX) g++ options: -flto -pthread
OpenBenchmarking.orgNodes Per Second Per Watt, More Is BetterLeelaChessZero 0.28Backend: EigenEPYC 7713EPYC 7713 2P4812162017.3412.41
MinAvgMaxEPYC 771337.9207.5230.7EPYC 7713 2P81.1329.8460.4OpenBenchmarking.orgWatts, Fewer Is BetterLeelaChessZero 0.28CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 77273 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 122341 Joules per run.

OpenBenchmarking.orgNodes Per Second, More Is BetterLeelaChessZero 0.28Backend: BLASEPYC 7713 2PEPYC 771310002000300040005000SE +/- 49.21, N = 4SE +/- 30.99, N = 3444938931. (CXX) g++ options: -flto -pthread
OpenBenchmarking.orgNodes Per Second Per Watt, More Is BetterLeelaChessZero 0.28Backend: BLASEPYC 7713EPYC 7713 2P51015202519.0713.53
MinAvgMaxEPYC 771337.6204.2230.7EPYC 7713 2P81.0328.9460.6OpenBenchmarking.orgWatts, Fewer Is BetterLeelaChessZero 0.28CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 75337 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 121775 Joules per run.

Renaissance

Renaissance is a suite of benchmarks designed to test the Java JVM from Apache Spark to a Twitter-like service to Scala and other features. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterRenaissance 0.14Test: Savina Reactors.IOEPYC 7713EPYC 7713 2P3K6K9K12K15KSE +/- 89.73, N = 3SE +/- 134.48, N = 128004.912254.2MIN: 7833.11 / MAX: 12272.32MIN: 11483.79 / MAX: 29956.39
MinAvgMaxEPYC 771337.489.9155.4EPYC 7713 2P80.3189.0356.9OpenBenchmarking.orgWatts, Fewer Is BetterRenaissance 0.14CPU Power Consumption Monitor1002003004005001. EPYC 7713: Approximate power consumption of 10220 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 33749 Joules per run.

Quantum ESPRESSO

Quantum ESPRESSO is an integrated suite of Open-Source computer codes for electronic-structure calculations and materials modeling at the nanoscale. It is based on density-functional theory, plane waves, and pseudopotentials. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterQuantum ESPRESSO 7.0Input: AUSURF112EPYC 7713EPYC 7713 2P90180270360450SE +/- 0.15, N = 3SE +/- 0.17, N = 3397.64399.921. (F9X) gfortran options: -pthread -fopenmp -ldevXlib -lopenblas -lFoX_dom -lFoX_sax -lFoX_wxml -lFoX_common -lFoX_utils -lFoX_fsys -lfftw3_omp -lfftw3 -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz
OpenBenchmarking.orgWatts, Fewer Is BetterQuantum ESPRESSO 7.0CPU Power Consumption MonitorEPYC 7713EPYC 7713 2P80160240320400Min: 38.14 / Avg: 226.3 / Max: 231.31Min: 80.14 / Avg: 453.36 / Max: 465.831. EPYC 7713: Approximate power consumption of 90748 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 182402 Joules per run.

oneDNN

This is a test of the Intel oneDNN as an Intel-optimized library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Intel oneDNN was formerly known as DNNL (Deep Neural Network Library) and MKL-DNN before being rebranded as part of Intel oneAPI. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 2.6Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPUEPYC 7713EPYC 7713 2P6001200180024003000SE +/- 16.05, N = 15SE +/- 49.56, N = 151229.562840.05MIN: 1044.76MIN: 2016.641. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -std=c++11 -pie -ldl -lpthread
MinAvgMaxEPYC 771337.7179.1224.2EPYC 7713 2P80.7328.4424.1OpenBenchmarking.orgWatts, Fewer Is BetteroneDNN 2.6CPU Power Consumption Monitor1102203304405501. EPYC 7713: Approximate power consumption of 14317 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 27303 Joules per run.

PostgreSQL pgbench

This is a benchmark of PostgreSQL using pgbench for facilitating the database benchmarks. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterPostgreSQL pgbench 14.0Scaling Factor: 100 - Clients: 250 - Mode: Read Write - Average LatencyEPYC 7713EPYC 7713 2P48121620SE +/- 0.005, N = 3SE +/- 0.235, N = 123.40015.0761. (CC) gcc options: -fno-strict-aliasing -fwrapv -O2 -lpgcommon -lpgport -lpq -lm
MinAvgMaxEPYC 771337.3190.3221.5EPYC 7713 2P80.4260.6353.8OpenBenchmarking.orgWatts, Fewer Is BetterPostgreSQL pgbench 14.0CPU Power Consumption Monitor1002003004005001. EPYC 7713: Approximate power consumption of 27082 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 37113 Joules per run.

OpenBenchmarking.orgTPS, More Is BetterPostgreSQL pgbench 14.0Scaling Factor: 100 - Clients: 250 - Mode: Read WriteEPYC 7713EPYC 7713 2P16K32K48K64K80KSE +/- 106.66, N = 3SE +/- 294.20, N = 1273527166331. (CC) gcc options: -fno-strict-aliasing -fwrapv -O2 -lpgcommon -lpgport -lpq -lm

asmFish

This is a test of asmFish, an advanced chess benchmark written in Assembly. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgNodes/second, More Is BetterasmFish 2018-07-231024 Hash Memory, 26 DepthEPYC 7713 2PEPYC 771350M100M150M200M250MSE +/- 2119737.62, N = 12SE +/- 277360.08, N = 3248870519138776102
OpenBenchmarking.orgNodes/second Per Watt, More Is BetterasmFish 2018-07-231024 Hash Memory, 26 DepthEPYC 7713EPYC 7713 2P130K260K390K520K650K626117.05560328.94
MinAvgMaxEPYC 771337.7221.6230.2EPYC 7713 2P80.3444.2461.1OpenBenchmarking.orgWatts, Fewer Is BetterasmFish 2018-07-23CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 32212 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 62699 Joules per run.

PostgreSQL pgbench

This is a benchmark of PostgreSQL using pgbench for facilitating the database benchmarks. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterPostgreSQL pgbench 14.0Scaling Factor: 100 - Clients: 500 - Mode: Read Write - Average LatencyEPYC 7713EPYC 7713 2P816243240SE +/- 0.022, N = 3SE +/- 0.318, N = 127.98035.0941. (CC) gcc options: -fno-strict-aliasing -fwrapv -O2 -lpgcommon -lpgport -lpq -lm
MinAvgMaxEPYC 771337.6190.9220.5EPYC 7713 2P80.4262.2360.1OpenBenchmarking.orgWatts, Fewer Is BetterPostgreSQL pgbench 14.0CPU Power Consumption Monitor1002003004005001. EPYC 7713: Approximate power consumption of 27107 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 37291 Joules per run.

OpenBenchmarking.orgTPS, More Is BetterPostgreSQL pgbench 14.0Scaling Factor: 100 - Clients: 500 - Mode: Read WriteEPYC 7713EPYC 7713 2P13K26K39K52K65KSE +/- 172.39, N = 3SE +/- 133.03, N = 1262654142611. (CC) gcc options: -fno-strict-aliasing -fwrapv -O2 -lpgcommon -lpgport -lpq -lm

Renaissance

Renaissance is a suite of benchmarks designed to test the Java JVM from Apache Spark to a Twitter-like service to Scala and other features. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterRenaissance 0.14Test: Apache Spark PageRankEPYC 7713EPYC 7713 2P9001800270036004500SE +/- 36.17, N = 4SE +/- 42.53, N = 123067.33985.7MIN: 2704.9 / MAX: 3282.25MIN: 3177.8 / MAX: 5051.59
MinAvgMaxEPYC 771337.497.0174.5EPYC 7713 2P79.9205.5333.4OpenBenchmarking.orgWatts, Fewer Is BetterRenaissance 0.14CPU Power Consumption Monitor801602403204001. EPYC 7713: Approximate power consumption of 10554 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 28536 Joules per run.

Graph500

This is a benchmark of the reference implementation of Graph500, an HPC benchmark focused on data intensive loads and commonly tested on supercomputers for complex data problems. Graph500 primarily stresses the communication subsystem of the hardware under test. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgsssp max_TEPS, More Is BetterGraph500 3.0Scale: 26EPYC 7713 2PEPYC 771380M160M240M320M400M3903770003235930001. (CC) gcc options: -fcommon -O3 -lpthread -lm -lmpi
OpenBenchmarking.orgsssp max_TEPS Per Watt, More Is BetterGraph500 3.0Scale: 26EPYC 7713EPYC 7713 2P300K600K900K1200K1500K1477511.39880495.41
MinAvgMaxEPYC 771337.7219.0227.4EPYC 7713 2P80.8443.4460.3OpenBenchmarking.orgWatts, Fewer Is BetterGraph500 3.0CPU Power Consumption Monitor120240360480600

OpenBenchmarking.orgsssp median_TEPS, More Is BetterGraph500 3.0Scale: 26EPYC 7713 2PEPYC 771360M120M180M240M300M3023380002544770001. (CC) gcc options: -fcommon -O3 -lpthread -lm -lmpi

OpenBenchmarking.orgbfs max_TEPS, More Is BetterGraph500 3.0Scale: 26EPYC 7713 2PEPYC 7713140M280M420M560M700M6594670006422780001. (CC) gcc options: -fcommon -O3 -lpthread -lm -lmpi

OpenBenchmarking.orgbfs median_TEPS, More Is BetterGraph500 3.0Scale: 26EPYC 7713 2PEPYC 7713140M280M420M560M700M6425160006226810001. (CC) gcc options: -fcommon -O3 -lpthread -lm -lmpi

ONNX Runtime

ONNX Runtime is developed by Microsoft and partners as a open-source, cross-platform, high performance machine learning inferencing and training accelerator. This test profile runs the ONNX Runtime with various models available from the ONNX Zoo. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgInferences Per Minute, More Is BetterONNX Runtime 1.11Model: ArcFace ResNet-100 - Device: CPU - Executor: StandardEPYC 7713EPYC 7713 2P30060090012001500SE +/- 5.25, N = 3SE +/- 14.15, N = 1215388771. (CXX) g++ options: -ffunction-sections -fdata-sections -march=native -mtune=native -O3 -flto -fno-fat-lto-objects -ldl -lrt
OpenBenchmarking.orgInferences Per Minute Per Watt, More Is BetterONNX Runtime 1.11Model: ArcFace ResNet-100 - Device: CPU - Executor: StandardEPYC 7713EPYC 7713 2P2468107.3812.228
MinAvgMaxEPYC 771337.4208.4220.6EPYC 7713 2P81.3393.7432.4OpenBenchmarking.orgWatts, Fewer Is BetterONNX Runtime 1.11CPU Power Consumption Monitor1102203304405501. EPYC 7713: Approximate power consumption of 26048 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 49011 Joules per run.

OpenBenchmarking.orgInferences Per Minute, More Is BetterONNX Runtime 1.11Model: fcn-resnet101-11 - Device: CPU - Executor: StandardEPYC 7713 2PEPYC 771350100150200250SE +/- 2.05, N = 3SE +/- 3.69, N = 122371851. (CXX) g++ options: -ffunction-sections -fdata-sections -march=native -mtune=native -O3 -flto -fno-fat-lto-objects -ldl -lrt
OpenBenchmarking.orgInferences Per Minute Per Watt, More Is BetterONNX Runtime 1.11Model: fcn-resnet101-11 - Device: CPU - Executor: StandardEPYC 7713EPYC 7713 2P0.20480.40960.61440.81921.0240.910.55
OpenBenchmarking.orgWatts, Fewer Is BetterONNX Runtime 1.11CPU Power Consumption MonitorEPYC 7713EPYC 7713 2P80160240320400Min: 37.78 / Avg: 203.21 / Max: 225.65Min: 81.6 / Avg: 430.98 / Max: 454.191. EPYC 7713: Approximate power consumption of 25367 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 53585 Joules per run.

OpenBenchmarking.orgInferences Per Minute, More Is BetterONNX Runtime 1.11Model: yolov4 - Device: CPU - Executor: StandardEPYC 7713EPYC 7713 2P90180270360450SE +/- 1.15, N = 3SE +/- 5.46, N = 124173301. (CXX) g++ options: -ffunction-sections -fdata-sections -march=native -mtune=native -O3 -flto -fno-fat-lto-objects -ldl -lrt
OpenBenchmarking.orgInferences Per Minute Per Watt, More Is BetterONNX Runtime 1.11Model: yolov4 - Device: CPU - Executor: StandardEPYC 7713EPYC 7713 2P0.47050.9411.41151.8822.35252.0910.813
MinAvgMaxEPYC 771338.2199.5207.6EPYC 7713 2P81.1405.9431.2OpenBenchmarking.orgWatts, Fewer Is BetterONNX Runtime 1.11CPU Power Consumption Monitor1102203304405501. EPYC 7713: Approximate power consumption of 24933 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 50538 Joules per run.

OpenBenchmarking.orgInferences Per Minute, More Is BetterONNX Runtime 1.11Model: super-resolution-10 - Device: CPU - Executor: StandardEPYC 7713EPYC 7713 2P14002800420056007000SE +/- 6.02, N = 3SE +/- 45.38, N = 12663145691. (CXX) g++ options: -ffunction-sections -fdata-sections -march=native -mtune=native -O3 -flto -fno-fat-lto-objects -ldl -lrt
OpenBenchmarking.orgInferences Per Minute Per Watt, More Is BetterONNX Runtime 1.11Model: super-resolution-10 - Device: CPU - Executor: StandardEPYC 7713EPYC 7713 2P112233445547.9119.08
MinAvgMaxEPYC 771337.6138.4144.1EPYC 7713 2P81.0239.5266.4OpenBenchmarking.orgWatts, Fewer Is BetterONNX Runtime 1.11CPU Power Consumption Monitor701402102803501. EPYC 7713: Approximate power consumption of 17118 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 29619 Joules per run.

JPEG XL libjxl

The JPEG XL Image Coding System is designed to provide next-generation JPEG image capabilities with JPEG XL offering better image quality and compression over legacy JPEG. This test profile is currently focused on the multi-threaded JPEG XL image encode performance using the reference libjxl library. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMP/s, More Is BetterJPEG XL libjxl 0.6.1Input: PNG - Encode Speed: 8EPYC 7713EPYC 7713 2P0.22950.4590.68850.9181.1475SE +/- 0.00, N = 3SE +/- 0.00, N = 31.021.001. (CXX) g++ options: -funwind-tables -O3 -O2 -fPIE -pie
OpenBenchmarking.orgMP/s Per Watt, More Is BetterJPEG XL libjxl 0.6.1Input: PNG - Encode Speed: 8EPYC 7713EPYC 7713 2P0.00270.00540.00810.01080.01350.0120.006
MinAvgMaxEPYC 771337.282.599.6EPYC 7713 2P80.9162.3195.2OpenBenchmarking.orgWatts, Fewer Is BetterJPEG XL libjxl 0.6.1CPU Power Consumption Monitor501001502002501. EPYC 7713: Approximate power consumption of 23681 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 47974 Joules per run.

SecureMark

SecureMark is an objective, standardized benchmarking framework for measuring the efficiency of cryptographic processing solutions developed by EEMBC. SecureMark-TLS is benchmarking Transport Layer Security performance with a focus on IoT/edge computing. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgmarks, More Is BetterSecureMark 1.0.4Benchmark: SecureMark-TLSEPYC 7713 2PEPYC 771350K100K150K200K250KSE +/- 512.51, N = 3SE +/- 2502.24, N = 32495822477471. (CC) gcc options: -pedantic -O3
OpenBenchmarking.orgmarks Per Watt, More Is BetterSecureMark 1.0.4Benchmark: SecureMark-TLSEPYC 7713EPYC 7713 2P70014002100280035003330.251670.40
MinAvgMaxEPYC 771337.374.475.4EPYC 7713 2P79.8149.4154.8OpenBenchmarking.orgWatts, Fewer Is BetterSecureMark 1.0.4CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 21152 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 42483 Joules per run.

High Performance Conjugate Gradient

HPCG is the High Performance Conjugate Gradient and is a new scientific benchmark from Sandia National Lans focused for super-computer testing with modern real-world workloads compared to HPCC. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgGFLOP/s, More Is BetterHigh Performance Conjugate Gradient 3.1EPYC 7713 2PEPYC 7713918273645SE +/- 0.11, N = 3SE +/- 0.01, N = 337.1019.111. (CXX) g++ options: -O3 -ffast-math -ftree-vectorize -lmpi_cxx -lmpi
OpenBenchmarking.orgGFLOP/s Per Watt, More Is BetterHigh Performance Conjugate Gradient 3.1EPYC 7713EPYC 7713 2P0.02070.04140.06210.08280.10350.0920.086
MinAvgMaxEPYC 771337.9208.1224.5EPYC 7713 2P80.3433.3453.1OpenBenchmarking.orgWatts, Fewer Is BetterHigh Performance Conjugate Gradient 3.1CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 51465 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 110771 Joules per run.

LuaRadio

LuaRadio is a lightweight software-defined radio (SDR) framework built atop LuaJIT. LuaRadio provides a suite of source, sink, and processing blocks, with a simple API for defining flow graphs, running flow graphs, creating blocks, and creating data types. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMiB/s, More Is BetterLuaRadio 0.9.1Test: Complex PhaseEPYC 7713EPYC 7713 2P140280420560700SE +/- 0.40, N = 3SE +/- 0.67, N = 3628.9623.8
OpenBenchmarking.orgMiB/s Per Watt, More Is BetterLuaRadio 0.9.1Test: Complex PhaseEPYC 7713EPYC 7713 2P2468107.5583.937
MinAvgMaxEPYC 771337.483.2114.5EPYC 7713 2P74.5158.5199.2OpenBenchmarking.orgWatts, Fewer Is BetterLuaRadio 0.9.1CPU Power Consumption Monitor501001502002501. EPYC 7713: Approximate power consumption of 20304 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 38663 Joules per run.

OpenBenchmarking.orgMiB/s, More Is BetterLuaRadio 0.9.1Test: Hilbert TransformEPYC 7713EPYC 7713 2P20406080100SE +/- 0.12, N = 3SE +/- 0.06, N = 398.698.4

OpenBenchmarking.orgMiB/s, More Is BetterLuaRadio 0.9.1Test: FM Deemphasis FilterEPYC 7713EPYC 7713 2P80160240320400SE +/- 0.12, N = 3SE +/- 0.33, N = 3365.4364.3

OpenBenchmarking.orgMiB/s, More Is BetterLuaRadio 0.9.1Test: Five Back to Back FIR FiltersEPYC 7713 2PEPYC 771330060090012001500SE +/- 4.33, N = 3SE +/- 13.28, N = 31211.61189.0

Blender

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.3Blend File: Barbershop - Compute: CPU-OnlyEPYC 7713 2PEPYC 771370140210280350SE +/- 0.28, N = 3SE +/- 0.18, N = 3171.52299.30
MinAvgMaxEPYC 771338.0224.7231.2EPYC 7713 2P80.3436.3461.0OpenBenchmarking.orgWatts, Fewer Is BetterBlender 3.3CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 68228 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 77370 Joules per run.

oneDNN

This is a test of the Intel oneDNN as an Intel-optimized library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Intel oneDNN was formerly known as DNNL (Deep Neural Network Library) and MKL-DNN before being rebranded as part of Intel oneAPI. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 2.6Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPUEPYC 7713EPYC 7713 2P6001200180024003000SE +/- 2.41, N = 3SE +/- 32.96, N = 151253.502754.45MIN: 1197.7MIN: 2158.821. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -std=c++11 -pie -ldl -lpthread
MinAvgMaxEPYC 771337.7177.8222.4EPYC 7713 2P80.5327.1426.0OpenBenchmarking.orgWatts, Fewer Is BetteroneDNN 2.6CPU Power Consumption Monitor1102203304405501. EPYC 7713: Approximate power consumption of 14281 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 27063 Joules per run.

Mlpack Benchmark

Mlpack benchmark scripts for machine learning libraries Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_qdaEPYC 7713EPYC 7713 2P714212835SE +/- 0.09, N = 3SE +/- 0.28, N = 1529.1931.84
MinAvgMaxEPYC 771337.2129.3225.9EPYC 7713 2P80.3243.7428.2OpenBenchmarking.orgWatts, Fewer Is BetterMlpack BenchmarkCPU Power Consumption Monitor1102203304405501. EPYC 7713: Approximate power consumption of 9609 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 18926 Joules per run.

Timed Linux Kernel Compilation

This test times how long it takes to build the Linux kernel in a default configuration (defconfig) for the architecture being tested or alternatively an allmodconfig for building all possible kernel modules for the build. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed Linux Kernel Compilation 5.18Build: allmodconfigEPYC 7713 2PEPYC 771360120180240300SE +/- 0.47, N = 3SE +/- 0.54, N = 3160.46272.61
MinAvgMaxEPYC 771337.5203.2229.9EPYC 7713 2P81.3375.6461.9OpenBenchmarking.orgWatts, Fewer Is BetterTimed Linux Kernel Compilation 5.18CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 57718 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 64845 Joules per run.

TNN

TNN is an open-source deep learning reasoning framework developed by Tencent. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterTNN 0.3Target: CPU - Model: DenseNetEPYC 7713 2PEPYC 77137001400210028003500SE +/- 3.94, N = 3SE +/- 0.99, N = 33065.923107.10MIN: 3032.01 / MAX: 3618.4MIN: 3090.23 / MAX: 3134.071. (CXX) g++ options: -fopenmp -pthread -fvisibility=hidden -fvisibility=default -O3 -rdynamic -ldl
MinAvgMaxEPYC 771337.595.1101.9EPYC 7713 2P80.3200.2232.1OpenBenchmarking.orgWatts, Fewer Is BetterTNN 0.3CPU Power Consumption Monitor601201802403001. EPYC 7713: Approximate power consumption of 21051 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 43634 Joules per run.

OpenFOAM

OpenFOAM is the leading free, open-source software for computational fluid dynamics (CFD). This test profile currently uses the drivaerFastback test case for analyzing automotive aerodynamics or alternatively the older motorBike input. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenFOAM 9Input: drivaerFastback, Small Mesh Size - Execution TimeEPYC 7713 2PEPYC 7713140280420560700281.70633.931. (CXX) g++ options: -std=c++14 -m64 -O3 -ftemplate-depth-100 -fPIC -fuse-ld=bfd -Xlinker --add-needed --no-as-needed -ldynamicMesh -ldecompose -lgenericPatchFields -lmetisDecomp -lscotchDecomp -llagrangian -lregionModels -lOpenFOAM -ldl -lm
MinAvgMaxEPYC 771337.2217.7230.3EPYC 7713 2P81.3443.3460.6OpenBenchmarking.orgWatts, Fewer Is BetterOpenFOAM 9CPU Power Consumption Monitor120240360480600

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenFOAM 9Input: drivaerFastback, Small Mesh Size - Mesh TimeEPYC 7713 2PEPYC 7713306090120150124.96139.891. (CXX) g++ options: -std=c++14 -m64 -O3 -ftemplate-depth-100 -fPIC -fuse-ld=bfd -Xlinker --add-needed --no-as-needed -ldynamicMesh -ldecompose -lgenericPatchFields -lmetisDecomp -lscotchDecomp -llagrangian -lregionModels -lOpenFOAM -ldl -lm

Timed LLVM Compilation

This test times how long it takes to build the LLVM compiler stack. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed LLVM Compilation 13.0Build System: Unix MakefilesEPYC 7713 2PEPYC 771350100150200250SE +/- 0.32, N = 3SE +/- 0.36, N = 3178.68218.45
MinAvgMaxEPYC 771337.9167.6230.8EPYC 7713 2P80.3287.6461.5OpenBenchmarking.orgWatts, Fewer Is BetterTimed LLVM Compilation 13.0CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 39767 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 57041 Joules per run.

Numpy Benchmark

This is a test to obtain the general Numpy performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgScore, More Is BetterNumpy BenchmarkEPYC 7713 2PEPYC 7713100200300400500SE +/- 0.78, N = 3SE +/- 0.32, N = 3469.38464.56
OpenBenchmarking.orgScore Per Watt, More Is BetterNumpy BenchmarkEPYC 7713EPYC 7713 2P2468106.1133.119
MinAvgMaxEPYC 771337.176.097.5EPYC 7713 2P80.3150.5172.6OpenBenchmarking.orgWatts, Fewer Is BetterNumpy BenchmarkCPU Power Consumption Monitor501001502002501. EPYC 7713: Approximate power consumption of 14870 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 29294 Joules per run.

LuxCoreRender

LuxCoreRender is an open-source 3D physically based renderer formerly known as LuxRender. LuxCoreRender supports CPU-based rendering as well as GPU acceleration via OpenCL, NVIDIA CUDA, and NVIDIA OptiX interfaces. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgM samples/sec, More Is BetterLuxCoreRender 2.6Scene: Danish Mood - Acceleration: CPUEPYC 7713 2PEPYC 7713246810SE +/- 0.10, N = 15SE +/- 0.07, N = 37.506.20MIN: 2.67 / MAX: 10.06MIN: 2.45 / MAX: 7.36
OpenBenchmarking.orgM samples/sec Per Watt, More Is BetterLuxCoreRender 2.6Scene: Danish Mood - Acceleration: CPUEPYC 7713EPYC 7713 2P0.00680.01360.02040.02720.0340.0300.019
MinAvgMaxEPYC 771338.1205.6227.0EPYC 7713 2P80.8403.9458.8OpenBenchmarking.orgWatts, Fewer Is BetterLuxCoreRender 2.6CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 13706 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 26951 Joules per run.

OpenBenchmarking.orgM samples/sec, More Is BetterLuxCoreRender 2.6Scene: LuxCore Benchmark - Acceleration: CPUEPYC 7713 2PEPYC 7713246810SE +/- 0.12, N = 15SE +/- 0.06, N = 37.636.84MIN: 2.51 / MAX: 10.1MIN: 2.61 / MAX: 8.19
OpenBenchmarking.orgM samples/sec Per Watt, More Is BetterLuxCoreRender 2.6Scene: LuxCore Benchmark - Acceleration: CPUEPYC 7713EPYC 7713 2P0.00740.01480.02220.02960.0370.0330.019
MinAvgMaxEPYC 771337.6205.8230.1EPYC 7713 2P80.9403.7460.2OpenBenchmarking.orgWatts, Fewer Is BetterLuxCoreRender 2.6CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 13583 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 26752 Joules per run.

LZ4 Compression

This test measures the time needed to compress/decompress a sample file (an Ubuntu ISO) using LZ4 compression. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMB/s, More Is BetterLZ4 Compression 1.9.3Compression Level: 3 - Decompression SpeedEPYC 7713EPYC 7713 2P3K6K9K12K15KSE +/- 23.01, N = 15SE +/- 16.62, N = 314123.310743.21. (CC) gcc options: -O3
OpenBenchmarking.orgMB/s Per Watt, More Is BetterLZ4 Compression 1.9.3Compression Level: 3 - Decompression SpeedEPYC 7713EPYC 7713 2P4080120160200178.0166.58
MinAvgMaxEPYC 771337.479.3105.1EPYC 7713 2P80.7161.4219.0OpenBenchmarking.orgWatts, Fewer Is BetterLZ4 Compression 1.9.3CPU Power Consumption Monitor601201802403001. EPYC 7713: Approximate power consumption of 5083 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 10811 Joules per run.

OpenBenchmarking.orgMB/s, More Is BetterLZ4 Compression 1.9.3Compression Level: 3 - Compression SpeedEPYC 7713EPYC 7713 2P1224364860SE +/- 0.38, N = 15SE +/- 0.40, N = 355.4354.181. (CC) gcc options: -O3

OpenSSL

OpenSSL is an open-source toolkit that implements SSL (Secure Sockets Layer) and TLS (Transport Layer Security) protocols. This test profile makes use of the built-in "openssl speed" benchmarking capabilities. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgbyte/s, More Is BetterOpenSSL 3.0Algorithm: SHA256EPYC 7713 2PEPYC 771330000M60000M90000M120000M150000MSE +/- 153008615.66, N = 3SE +/- 67558357.93, N = 3134629156527681018098531. (CC) gcc options: -pthread -m64 -O3 -lssl -lcrypto -ldl
OpenBenchmarking.orgbyte/s Per Watt, More Is BetterOpenSSL 3.0Algorithm: SHA256EPYC 7713EPYC 7713 2P60M120M180M240M300M303100481.37298000025.39
MinAvgMaxEPYC 771338.2224.7230.7EPYC 7713 2P80.6451.8460.6OpenBenchmarking.orgWatts, Fewer Is BetterOpenSSL 3.0CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 41267 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 82826 Joules per run.

Timed Gem5 Compilation

This test times how long it takes to compile Gem5. Gem5 is a simulator for computer system architecture research. Gem5 is widely used for computer architecture research within the industry, academia, and more. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed Gem5 Compilation 21.2Time To CompileEPYC 7713 2PEPYC 77134080120160200SE +/- 1.20, N = 3SE +/- 1.96, N = 3159.02176.43
MinAvgMaxEPYC 771337.5137.4229.6EPYC 7713 2P80.3235.4456.8OpenBenchmarking.orgWatts, Fewer Is BetterTimed Gem5 Compilation 21.2CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 24923 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 38534 Joules per run.

Appleseed

Appleseed is an open-source production renderer focused on physically-based global illumination rendering engine primarily designed for animation and visual effects. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterAppleseed 2.0 BetaScene: Material TesterEPYC 7713EPYC 7713 2P70140210280350152.39336.58
MinAvgMaxEPYC 771337.7189.2207.8EPYC 7713 2P80.9276.8313.6OpenBenchmarking.orgWatts, Fewer Is BetterAppleseed 2.0 BetaCPU Power Consumption Monitor80160240320400

WebP2 Image Encode

This is a test of Google's libwebp2 library with the WebP2 image encode utility and using a sample 6000x4000 pixel JPEG image as the input, similar to the WebP/libwebp test profile. WebP2 is currently experimental and under heavy development as ultimately the successor to WebP. WebP2 supports 10-bit HDR, more efficienct lossy compression, improved lossless compression, animation support, and full multi-threading support compared to WebP. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMP/s, More Is BetterWebP2 Image Encode 20220823Encode Settings: Quality 95, Compression Effort 7EPYC 7713 2PEPYC 77130.06980.13960.20940.27920.349SE +/- 0.00, N = 7SE +/- 0.00, N = 30.310.241. (CXX) g++ options: -msse4.2 -fno-rtti -O3 -ldl
OpenBenchmarking.orgMP/s Per Watt, More Is BetterWebP2 Image Encode 20220823Encode Settings: Quality 95, Compression Effort 7EPYC 7713EPYC 7713 2P0.00020.00040.00060.00080.0010.0010.001
MinAvgMaxEPYC 771337.6203.9230.3EPYC 7713 2P79.8345.9460.6OpenBenchmarking.orgWatts, Fewer Is BetterWebP2 Image Encode 20220823CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 21411 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 28467 Joules per run.

OSPRay Studio

Intel OSPRay Studio is an open-source, interactive visualization and ray-tracing software package. OSPRay Studio makes use of Intel OSPRay, a portable ray-tracing engine for high-performance, high-fidelity visualizations. OSPRay builds off Intel's Embree and Intel SPMD Program Compiler (ISPC) components as part of the oneAPI rendering toolkit. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterOSPRay Studio 0.11Camera: 3 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path TracerEPYC 7713 2PEPYC 771320K40K60K80K100KSE +/- 265.11, N = 3SE +/- 269.96, N = 3527851000511. (CXX) g++ options: -O3 -ldl
MinAvgMaxEPYC 771338.3205.1230.3EPYC 7713 2P80.1424.8460.5OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay Studio 0.11CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 22902 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 74903 Joules per run.

Timed Node.js Compilation

This test profile times how long it takes to build/compile Node.js itself from source. Node.js is a JavaScript run-time built from the Chrome V8 JavaScript engine while itself is written in C/C++. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed Node.js Compilation 18.8Time To CompileEPYC 7713 2PEPYC 77134080120160200SE +/- 0.71, N = 3SE +/- 0.14, N = 3113.47164.00
MinAvgMaxEPYC 771337.9182.4229.9EPYC 7713 2P80.8320.4461.3OpenBenchmarking.orgWatts, Fewer Is BetterTimed Node.js Compilation 18.8CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 31244 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 38872 Joules per run.

PostgreSQL pgbench

This is a benchmark of PostgreSQL using pgbench for facilitating the database benchmarks. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterPostgreSQL pgbench 14.0Scaling Factor: 100 - Clients: 500 - Mode: Read Only - Average LatencyEPYC 7713 2PEPYC 77130.08210.16420.24630.32840.4105SE +/- 0.002, N = 3SE +/- 0.004, N = 30.2480.3651. (CC) gcc options: -fno-strict-aliasing -fwrapv -O2 -lpgcommon -lpgport -lpq -lm
MinAvgMaxEPYC 771337.6200.5231.1EPYC 7713 2P80.5401.3457.5OpenBenchmarking.orgWatts, Fewer Is BetterPostgreSQL pgbench 14.0CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 28475 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 56979 Joules per run.

OpenBenchmarking.orgTPS, More Is BetterPostgreSQL pgbench 14.0Scaling Factor: 100 - Clients: 500 - Mode: Read OnlyEPYC 7713 2PEPYC 7713400K800K1200K1600K2000KSE +/- 13496.08, N = 3SE +/- 13290.57, N = 3201272213709351. (CC) gcc options: -fno-strict-aliasing -fwrapv -O2 -lpgcommon -lpgport -lpq -lm

OpenBenchmarking.orgms, Fewer Is BetterPostgreSQL pgbench 14.0Scaling Factor: 100 - Clients: 250 - Mode: Read Only - Average LatencyEPYC 7713 2PEPYC 77130.04340.08680.13020.17360.217SE +/- 0.001, N = 3SE +/- 0.000, N = 30.1260.1931. (CC) gcc options: -fno-strict-aliasing -fwrapv -O2 -lpgcommon -lpgport -lpq -lm
MinAvgMaxEPYC 771337.6200.0230.2EPYC 7713 2P80.9399.9461.4OpenBenchmarking.orgWatts, Fewer Is BetterPostgreSQL pgbench 14.0CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 28333 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 56921 Joules per run.

OpenBenchmarking.orgTPS, More Is BetterPostgreSQL pgbench 14.0Scaling Factor: 100 - Clients: 250 - Mode: Read OnlyEPYC 7713 2PEPYC 7713400K800K1200K1600K2000KSE +/- 12988.21, N = 3SE +/- 818.86, N = 3198315512976841. (CC) gcc options: -fno-strict-aliasing -fwrapv -O2 -lpgcommon -lpgport -lpq -lm

OSPRay Studio

Intel OSPRay Studio is an open-source, interactive visualization and ray-tracing software package. OSPRay Studio makes use of Intel OSPRay, a portable ray-tracing engine for high-performance, high-fidelity visualizations. OSPRay builds off Intel's Embree and Intel SPMD Program Compiler (ISPC) components as part of the oneAPI rendering toolkit. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterOSPRay Studio 0.11Camera: 3 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path TracerEPYC 7713 2PEPYC 771310K20K30K40K50KSE +/- 146.03, N = 3SE +/- 66.05, N = 326510471341. (CXX) g++ options: -O3 -ldl
MinAvgMaxEPYC 771337.9212.4231.2EPYC 7713 2P80.9410.5460.6OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay Studio 0.11CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 33625 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 50905 Joules per run.

Ngspice

Ngspice is an open-source SPICE circuit simulator. Ngspice was originally based on the Berkeley SPICE electronic circuit simulator. Ngspice supports basic threading using OpenMP. This test profile is making use of the ISCAS 85 benchmark circuits. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterNgspice 34Circuit: C2670EPYC 7713EPYC 7713 2P306090120150SE +/- 0.70, N = 3SE +/- 0.58, N = 3136.02137.361. (CC) gcc options: -O0 -fopenmp -lm -lstdc++ -lfftw3 -lXaw -lXmu -lXt -lXext -lX11 -lXft -lfontconfig -lXrender -lfreetype -lSM -lICE
MinAvgMaxEPYC 771337.380.990.0EPYC 7713 2P81.2162.3170.8OpenBenchmarking.orgWatts, Fewer Is BetterNgspice 34CPU Power Consumption Monitor501001502002501. EPYC 7713: Approximate power consumption of 11382 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 23101 Joules per run.

Timed LLVM Compilation

This test times how long it takes to build the LLVM compiler stack. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed LLVM Compilation 13.0Build System: NinjaEPYC 7713 2PEPYC 77134080120160200SE +/- 0.51, N = 3SE +/- 0.28, N = 3105.03159.58
MinAvgMaxEPYC 771337.8183.4230.3EPYC 7713 2P80.1319.2460.5OpenBenchmarking.orgWatts, Fewer Is BetterTimed LLVM Compilation 13.0CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 32406 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 39155 Joules per run.

VP9 libvpx Encoding

This is a standard video encoding performance test of Google's libvpx library and the vpxenc command for the VP9 video format. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterVP9 libvpx Encoding 1.10.0Speed: Speed 5 - Input: Bosphorus 4KEPYC 7713EPYC 7713 2P48121620SE +/- 0.08, N = 3SE +/- 0.37, N = 1515.8613.861. (CXX) g++ options: -m64 -lm -lpthread -O3 -fPIC -U_FORTIFY_SOURCE -std=gnu++11
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterVP9 libvpx Encoding 1.10.0Speed: Speed 5 - Input: Bosphorus 4KEPYC 7713EPYC 7713 2P0.03240.06480.09720.12960.1620.1440.071
MinAvgMaxEPYC 771337.4110.3122.8EPYC 7713 2P80.5196.6227.6OpenBenchmarking.orgWatts, Fewer Is BetterVP9 libvpx Encoding 1.10.0CPU Power Consumption Monitor601201802403001. EPYC 7713: Approximate power consumption of 4925 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 9541 Joules per run.

LuxCoreRender

LuxCoreRender is an open-source 3D physically based renderer formerly known as LuxRender. LuxCoreRender supports CPU-based rendering as well as GPU acceleration via OpenCL, NVIDIA CUDA, and NVIDIA OptiX interfaces. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgM samples/sec, More Is BetterLuxCoreRender 2.6Scene: Orange Juice - Acceleration: CPUEPYC 7713 2PEPYC 7713510152025SE +/- 0.15, N = 9SE +/- 0.01, N = 318.6012.48MIN: 15.92 / MAX: 24.08MIN: 10.85 / MAX: 14.14
OpenBenchmarking.orgM samples/sec Per Watt, More Is BetterLuxCoreRender 2.6Scene: Orange Juice - Acceleration: CPUEPYC 7713EPYC 7713 2P0.01330.02660.03990.05320.06650.0590.044
MinAvgMaxEPYC 771338.1211.4230.3EPYC 7713 2P80.0419.6464.6OpenBenchmarking.orgWatts, Fewer Is BetterLuxCoreRender 2.6CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 14304 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 28439 Joules per run.

Apache Cassandra

This is a benchmark of the Apache Cassandra NoSQL database management system making use of cassandra-stress. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgOp/s, More Is BetterApache Cassandra 4.0Test: WritesEPYC 7713EPYC 7713 2P50K100K150K200K250KSE +/- 786.81, N = 3SE +/- 1824.75, N = 3250596209921
OpenBenchmarking.orgOp/s Per Watt, More Is BetterApache Cassandra 4.0Test: WritesEPYC 7713EPYC 7713 2P300600900120015001328.21598.21
MinAvgMaxEPYC 771338.2188.7217.9EPYC 7713 2P81.1350.9403.8OpenBenchmarking.orgWatts, Fewer Is BetterApache Cassandra 4.0CPU Power Consumption Monitor1102203304405501. EPYC 7713: Approximate power consumption of 27232 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 50766 Joules per run.

ClickHouse

ClickHouse is an open-source, high performance OLAP data management system. This test profile uses ClickHouse's standard benchmark recommendations per https://clickhouse.com/docs/en/operations/performance-test/ with the 100 million rows web analytics dataset. The reported value is the query processing time using the geometric mean of all queries performed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgQueries Per Minute, Geo Mean, More Is BetterClickHouse 22.5.4.19100M Rows Web Analytics Dataset, Third RunEPYC 7713 2PEPYC 771390180270360450SE +/- 2.46, N = 3SE +/- 1.58, N = 15396.82394.98MIN: 60.67 / MAX: 20000MIN: 40.79 / MAX: 200001. ClickHouse server version 22.5.4.19 (official build).
OpenBenchmarking.orgQueries Per Minute, Geo Mean Per Watt, More Is BetterClickHouse 22.5.4.19100M Rows Web Analytics Dataset, Third RunEPYC 7713EPYC 7713 2P0.61741.23481.85222.46963.0872.7441.522
MinAvgMaxEPYC 771337.8144.0209.3EPYC 7713 2P80.8260.6374.0OpenBenchmarking.orgWatts, Fewer Is BetterClickHouse 22.5.4.19CPU Power Consumption Monitor1002003004005001. EPYC 7713: Approximate power consumption of 6565 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 11468 Joules per run.

OpenBenchmarking.orgQueries Per Minute, Geo Mean, More Is BetterClickHouse 22.5.4.19100M Rows Web Analytics Dataset, Second RunEPYC 7713EPYC 7713 2P90180270360450SE +/- 2.54, N = 15SE +/- 2.92, N = 3392.92387.01MIN: 41.75 / MAX: 30000MIN: 63.9 / MAX: 200001. ClickHouse server version 22.5.4.19 (official build).

OpenBenchmarking.orgQueries Per Minute, Geo Mean, More Is BetterClickHouse 22.5.4.19100M Rows Web Analytics Dataset, First Run / Cold CacheEPYC 7713EPYC 7713 2P80160240320400SE +/- 3.68, N = 15SE +/- 4.60, N = 3378.44377.52MIN: 37.15 / MAX: 20000MIN: 43.89 / MAX: 200001. ClickHouse server version 22.5.4.19 (official build).

OSPRay Studio

Intel OSPRay Studio is an open-source, interactive visualization and ray-tracing software package. OSPRay Studio makes use of Intel OSPRay, a portable ray-tracing engine for high-performance, high-fidelity visualizations. OSPRay builds off Intel's Embree and Intel SPMD Program Compiler (ISPC) components as part of the oneAPI rendering toolkit. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterOSPRay Studio 0.11Camera: 3 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path TracerEPYC 7713 2PEPYC 77136001200180024003000SE +/- 0.33, N = 3SE +/- 0.88, N = 3164629221. (CXX) g++ options: -O3 -ldl
MinAvgMaxEPYC 771337.9208.7230.3EPYC 7713 2P80.3409.5460.5OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay Studio 0.11CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 27895 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 49143 Joules per run.

OpenBenchmarking.orgms, Fewer Is BetterOSPRay Studio 0.11Camera: 2 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path TracerEPYC 7713 2PEPYC 771320K40K60K80K100KSE +/- 39.74, N = 3SE +/- 135.08, N = 345460860811. (CXX) g++ options: -O3 -ldl
MinAvgMaxEPYC 771337.8201.2230.3EPYC 7713 2P81.0419.8460.5OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay Studio 0.11CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 19651 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 64792 Joules per run.

ONNX Runtime

ONNX Runtime is developed by Microsoft and partners as a open-source, cross-platform, high performance machine learning inferencing and training accelerator. This test profile runs the ONNX Runtime with various models available from the ONNX Zoo. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgInferences Per Minute, More Is BetterONNX Runtime 1.11Model: GPT-2 - Device: CPU - Executor: StandardEPYC 7713EPYC 7713 2P2K4K6K8K10KSE +/- 26.19, N = 3SE +/- 48.25, N = 3936878781. (CXX) g++ options: -ffunction-sections -fdata-sections -march=native -mtune=native -O3 -flto -fno-fat-lto-objects -ldl -lrt
OpenBenchmarking.orgInferences Per Minute Per Watt, More Is BetterONNX Runtime 1.11Model: GPT-2 - Device: CPU - Executor: StandardEPYC 7713EPYC 7713 2P163248648069.9032.83
MinAvgMaxEPYC 771337.6134.0138.2EPYC 7713 2P81.2240.0259.0OpenBenchmarking.orgWatts, Fewer Is BetterONNX Runtime 1.11CPU Power Consumption Monitor701402102803501. EPYC 7713: Approximate power consumption of 16797 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 30078 Joules per run.

OpenBenchmarking.orgInferences Per Minute, More Is BetterONNX Runtime 1.11Model: bertsquad-12 - Device: CPU - Executor: StandardEPYC 7713EPYC 7713 2P150300450600750SE +/- 0.50, N = 3SE +/- 2.52, N = 37136681. (CXX) g++ options: -ffunction-sections -fdata-sections -march=native -mtune=native -O3 -flto -fno-fat-lto-objects -ldl -lrt
OpenBenchmarking.orgInferences Per Minute Per Watt, More Is BetterONNX Runtime 1.11Model: bertsquad-12 - Device: CPU - Executor: StandardEPYC 7713EPYC 7713 2P0.7721.5442.3163.0883.863.4311.776
MinAvgMaxEPYC 771337.4207.8217.7EPYC 7713 2P80.1376.1410.7OpenBenchmarking.orgWatts, Fewer Is BetterONNX Runtime 1.11CPU Power Consumption Monitor1102203304405501. EPYC 7713: Approximate power consumption of 25977 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 46886 Joules per run.

OSPRay Studio

Intel OSPRay Studio is an open-source, interactive visualization and ray-tracing software package. OSPRay Studio makes use of Intel OSPRay, a portable ray-tracing engine for high-performance, high-fidelity visualizations. OSPRay builds off Intel's Embree and Intel SPMD Program Compiler (ISPC) components as part of the oneAPI rendering toolkit. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterOSPRay Studio 0.11Camera: 1 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path TracerEPYC 7713 2PEPYC 771320K40K60K80K100KSE +/- 224.62, N = 3SE +/- 162.94, N = 344624849681. (CXX) g++ options: -O3 -ldl
MinAvgMaxEPYC 771337.8201.1230.3EPYC 7713 2P81.2418.9460.6OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay Studio 0.11CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 19508 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 63527 Joules per run.

OpenBenchmarking.orgms, Fewer Is BetterOSPRay Studio 0.11Camera: 2 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path TracerEPYC 7713 2PEPYC 77135001000150020002500SE +/- 0.33, N = 3SE +/- 1.73, N = 3142424961. (CXX) g++ options: -O3 -ldl
MinAvgMaxEPYC 771337.8208.6230.3EPYC 7713 2P80.0408.1460.3OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay Studio 0.11CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 26978 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 48154 Joules per run.

OpenBenchmarking.orgms, Fewer Is BetterOSPRay Studio 0.11Camera: 2 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path TracerEPYC 7713 2PEPYC 77139K18K27K36K45KSE +/- 44.67, N = 3SE +/- 10.17, N = 322742400581. (CXX) g++ options: -O3 -ldl
MinAvgMaxEPYC 771338.1209.6230.3EPYC 7713 2P80.8402.6460.5OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay Studio 0.11CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 28854 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 44156 Joules per run.

OpenBenchmarking.orgms, Fewer Is BetterOSPRay Studio 0.11Camera: 1 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path TracerEPYC 7713 2PEPYC 77135001000150020002500SE +/- 1.76, N = 3SE +/- 0.33, N = 3137924631. (CXX) g++ options: -O3 -ldl
MinAvgMaxEPYC 771337.6208.1230.5EPYC 7713 2P81.1408.6461.5OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay Studio 0.11CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 26707 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 48076 Joules per run.

OpenBenchmarking.orgms, Fewer Is BetterOSPRay Studio 0.11Camera: 1 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path TracerEPYC 7713 2PEPYC 77138K16K24K32K40KSE +/- 60.36, N = 3SE +/- 29.02, N = 322158395681. (CXX) g++ options: -O3 -ldl
MinAvgMaxEPYC 771338.0209.0230.3EPYC 7713 2P80.1402.3460.5OpenBenchmarking.orgWatts, Fewer Is BetterOSPRay Studio 0.11CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 28419 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 43176 Joules per run.

Renaissance

Renaissance is a suite of benchmarks designed to test the Java JVM from Apache Spark to a Twitter-like service to Scala and other features. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterRenaissance 0.14Test: Genetic Algorithm Using Jenetics + FuturesEPYC 7713EPYC 7713 2P5001000150020002500SE +/- 24.42, N = 3SE +/- 20.78, N = 32243.22314.9MIN: 2049.08 / MAX: 2446.27MIN: 2126.5 / MAX: 2897.34
MinAvgMaxEPYC 771337.683.498.2EPYC 7713 2P79.9162.6198.2OpenBenchmarking.orgWatts, Fewer Is BetterRenaissance 0.14CPU Power Consumption Monitor501001502002501. EPYC 7713: Approximate power consumption of 9897 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 19948 Joules per run.

NAS Parallel Benchmarks

NPB, NAS Parallel Benchmarks, is a benchmark developed by NASA for high-end computer systems. This test profile currently uses the MPI version of NPB. This test profile offers selecting the different NPB tests/problems and varying problem sizes. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: EP.DEPYC 7713 2PEPYC 77132K4K6K8K10KSE +/- 151.57, N = 15SE +/- 87.49, N = 159109.244668.961. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.2
OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: EP.DEPYC 7713 2PEPYC 771361218243024.3823.10
MinAvgMaxEPYC 771337.5202.1230.6EPYC 7713 2P80.8373.7460.6OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 6900 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 7449 Joules per run.

Renaissance

Renaissance is a suite of benchmarks designed to test the Java JVM from Apache Spark to a Twitter-like service to Scala and other features. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterRenaissance 0.14Test: In-Memory Database ShootoutEPYC 7713EPYC 7713 2P13002600390052006500SE +/- 58.97, N = 4SE +/- 42.85, N = 35009.26136.4MIN: 4716.84 / MAX: 5514.55MIN: 5231.89 / MAX: 7748.89
MinAvgMaxEPYC 771337.4129.2162.5EPYC 7713 2P80.9238.6386.5OpenBenchmarking.orgWatts, Fewer Is BetterRenaissance 0.14CPU Power Consumption Monitor1002003004005001. EPYC 7713: Approximate power consumption of 11890 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 27434 Joules per run.

OpenBenchmarking.orgms, Fewer Is BetterRenaissance 0.14Test: Finagle HTTP RequestsEPYC 7713EPYC 7713 2P2K4K6K8K10KSE +/- 23.48, N = 3SE +/- 107.40, N = 36720.810535.4MIN: 6349.06 / MAX: 6850.05MIN: 9747.42 / MAX: 10878.16
MinAvgMaxEPYC 771337.5207.7227.1EPYC 7713 2P80.9410.9452.4OpenBenchmarking.orgWatts, Fewer Is BetterRenaissance 0.14CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 18555 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 58342 Joules per run.

Ngspice

Ngspice is an open-source SPICE circuit simulator. Ngspice was originally based on the Berkeley SPICE electronic circuit simulator. Ngspice supports basic threading using OpenMP. This test profile is making use of the ISCAS 85 benchmark circuits. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterNgspice 34Circuit: C7552EPYC 7713EPYC 7713 2P20406080100SE +/- 0.58, N = 3SE +/- 0.54, N = 3103.39105.951. (CC) gcc options: -O0 -fopenmp -lm -lstdc++ -lfftw3 -lXaw -lXmu -lXt -lXext -lX11 -lXft -lfontconfig -lXrender -lfreetype -lSM -lICE
MinAvgMaxEPYC 771337.578.591.8EPYC 7713 2P80.7153.5168.7OpenBenchmarking.orgWatts, Fewer Is BetterNgspice 34CPU Power Consumption Monitor501001502002501. EPYC 7713: Approximate power consumption of 8794 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 17596 Joules per run.

etcd

Etcd is a distributed, reliable key-value store intended for critical data of a distributed system. Etcd is written in Golang and part of the Cloud Native Computing Foundation (CNCF) and used by Kubernetes, Rook, CoreDNS, and other open-source software. This test profile uses Etcd's built-in benchmark to stress the PUT and RANGE performance of a single node / local system. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is Betteretcd 3.5.4Test: RANGE - Connections: 100 - Clients: 100 - Average LatencyEPYC 7713EPYC 7713 2P0.60751.2151.82252.433.0375SE +/- 0.03, N = 3SE +/- 0.03, N = 41.32.7
MinAvgMaxEPYC 771337.6168.0202.7EPYC 7713 2P80.9303.2346.5OpenBenchmarking.orgWatts, Fewer Is Betteretcd 3.5.4CPU Power Consumption Monitor1002003004005001. EPYC 7713: Approximate power consumption of 10529 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 36156 Joules per run.

OpenBenchmarking.orgRequests/sec, More Is Betteretcd 3.5.4Test: RANGE - Connections: 100 - Clients: 100EPYC 7713EPYC 7713 2P20K40K60K80K100KSE +/- 329.90, N = 3SE +/- 394.99, N = 479219.6537230.33

oneDNN

This is a test of the Intel oneDNN as an Intel-optimized library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Intel oneDNN was formerly known as DNNL (Deep Neural Network Library) and MKL-DNN before being rebranded as part of Intel oneAPI. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 2.6Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPUEPYC 7713EPYC 7713 2P16003200480064008000SE +/- 21.84, N = 3SE +/- 85.87, N = 42984.477473.91MIN: 2892.79MIN: 6964.911. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -std=c++11 -pie -ldl -lpthread
MinAvgMaxEPYC 771337.8168.8226.3EPYC 7713 2P80.9315.7435.6OpenBenchmarking.orgWatts, Fewer Is BetteroneDNN 2.6CPU Power Consumption Monitor1102203304405501. EPYC 7713: Approximate power consumption of 14178 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 29909 Joules per run.

JPEG XL libjxl

The JPEG XL Image Coding System is designed to provide next-generation JPEG image capabilities with JPEG XL offering better image quality and compression over legacy JPEG. This test profile is currently focused on the multi-threaded JPEG XL image encode performance using the reference libjxl library. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMP/s, More Is BetterJPEG XL libjxl 0.6.1Input: PNG - Encode Speed: 7EPYC 7713EPYC 7713 2P3691215SE +/- 0.02, N = 3SE +/- 0.04, N = 311.1110.741. (CXX) g++ options: -funwind-tables -O3 -O2 -fPIE -pie
OpenBenchmarking.orgMP/s Per Watt, More Is BetterJPEG XL libjxl 0.6.1Input: PNG - Encode Speed: 7EPYC 7713EPYC 7713 2P0.02840.05680.08520.11360.1420.1260.062
MinAvgMaxEPYC 771318.787.9109.3EPYC 7713 2P80.0173.1209.8OpenBenchmarking.orgWatts, Fewer Is BetterJPEG XL libjxl 0.6.1CPU Power Consumption Monitor601201802403001. EPYC 7713: Approximate power consumption of 8997 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 18346 Joules per run.

Appleseed

Appleseed is an open-source production renderer focused on physically-based global illumination rendering engine primarily designed for animation and visual effects. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterAppleseed 2.0 BetaScene: EmilyEPYC 7713EPYC 7713 2P306090120150133.04151.53
MinAvgMaxEPYC 771338.1184.3230.4EPYC 7713 2P80.6328.8422.8OpenBenchmarking.orgWatts, Fewer Is BetterAppleseed 2.0 BetaCPU Power Consumption Monitor110220330440550

OpenVINO

This is a test of the Intel OpenVINO, a toolkit around neural networks, using its built-in benchmarking support and analyzing the throughput and latency for various models. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2022.2.devModel: Face Detection FP16 - Device: CPUEPYC 7713EPYC 7713 2P8001600240032004000SE +/- 2.50, N = 3SE +/- 39.69, N = 53555.783597.47MIN: 3306.65 / MAX: 3702.18MIN: 1881.18 / MAX: 6268.951. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared
MinAvgMaxEPYC 771338.1202.8230.8EPYC 7713 2P80.6405.9462.0OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2022.2.devCPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 14802 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 30930 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2022.2.devModel: Face Detection FP16 - Device: CPUEPYC 7713 2PEPYC 771348121620SE +/- 0.18, N = 5SE +/- 0.01, N = 317.598.971. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared

simdjson

This is a benchmark of SIMDJSON, a high performance JSON parser. SIMDJSON aims to be the fastest JSON parser and is used by projects like Microsoft FishStore, Yandex ClickHouse, Shopify, and others. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgGB/s, More Is Bettersimdjson 2.0Throughput Test: DistinctUserIDEPYC 7713 2PEPYC 77130.99451.9892.98353.9784.9725SE +/- 0.01, N = 3SE +/- 0.01, N = 34.424.401. (CXX) g++ options: -O3
OpenBenchmarking.orgGB/s Per Watt, More Is Bettersimdjson 2.0Throughput Test: DistinctUserIDEPYC 7713EPYC 7713 2P0.01330.02660.03990.05320.06650.0590.030
MinAvgMaxEPYC 771337.274.878.5EPYC 7713 2P80.7149.3157.9OpenBenchmarking.orgWatts, Fewer Is Bettersimdjson 2.0CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 7256 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 14581 Joules per run.

OpenBenchmarking.orgGB/s, More Is Bettersimdjson 2.0Throughput Test: TopTweetEPYC 7713EPYC 7713 2P0.98781.97562.96343.95124.939SE +/- 0.00, N = 3SE +/- 0.02, N = 34.394.391. (CXX) g++ options: -O3
OpenBenchmarking.orgGB/s Per Watt, More Is Bettersimdjson 2.0Throughput Test: TopTweetEPYC 7713EPYC 7713 2P0.01330.02660.03990.05320.06650.0590.029
MinAvgMaxEPYC 771337.274.978.4EPYC 7713 2P80.5149.7157.7OpenBenchmarking.orgWatts, Fewer Is Bettersimdjson 2.0CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 7263 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 14422 Joules per run.

Apache HTTP Server

This is a test of the Apache HTTPD web server. This Apache HTTPD web server benchmark test profile makes use of the Golang "Bombardier" program for facilitating the HTTP requests over a fixed period time with a configurable number of concurrent clients. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgRequests Per Second, More Is BetterApache HTTP Server 2.4.48Concurrent Requests: 1000EPYC 7713EPYC 7713 2P20K40K60K80K100KSE +/- 83.20, N = 3SE +/- 151.47, N = 3109150.3084767.731. (CC) gcc options: -shared -fPIC -O2
OpenBenchmarking.orgRequests Per Second Per Watt, More Is BetterApache HTTP Server 2.4.48Concurrent Requests: 1000EPYC 7713EPYC 7713 2P120240360480600557.36210.61
MinAvgMaxEPYC 771337.7195.8218.1EPYC 7713 2P80.5402.5436.3OpenBenchmarking.orgWatts, Fewer Is BetterApache HTTP Server 2.4.48CPU Power Consumption Monitor1102203304405501. EPYC 7713: Approximate power consumption of 19583 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 39309 Joules per run.

Helsing

Helsing is an open-source POSIX vampire number generator. This test profile measures the time it takes to generate vampire numbers between varying numbers of digits. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterHelsing 1.0-betaDigit Range: 14 digitEPYC 7713 2PEPYC 7713306090120150SE +/- 0.11, N = 3SE +/- 0.16, N = 362.13121.001. (CC) gcc options: -O2 -pthread
MinAvgMaxEPYC 771337.1222.6233.0EPYC 7713 2P79.7434.6461.0OpenBenchmarking.orgWatts, Fewer Is BetterHelsing 1.0-betaCPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 27751 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 28685 Joules per run.

simdjson

This is a benchmark of SIMDJSON, a high performance JSON parser. SIMDJSON aims to be the fastest JSON parser and is used by projects like Microsoft FishStore, Yandex ClickHouse, Shopify, and others. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgGB/s, More Is Bettersimdjson 2.0Throughput Test: PartialTweetsEPYC 7713EPYC 7713 2P0.86851.7372.60553.4744.3425SE +/- 0.01, N = 3SE +/- 0.00, N = 33.863.841. (CXX) g++ options: -O3
OpenBenchmarking.orgGB/s Per Watt, More Is Bettersimdjson 2.0Throughput Test: PartialTweetsEPYC 7713EPYC 7713 2P0.01170.02340.03510.04680.05850.0520.026
MinAvgMaxEPYC 771337.274.876.7EPYC 7713 2P76.3149.2159.5OpenBenchmarking.orgWatts, Fewer Is Bettersimdjson 2.0CPU Power Consumption Monitor501001502002501. EPYC 7713: Approximate power consumption of 7104 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 14071 Joules per run.

Apache HTTP Server

This is a test of the Apache HTTPD web server. This Apache HTTPD web server benchmark test profile makes use of the Golang "Bombardier" program for facilitating the HTTP requests over a fixed period time with a configurable number of concurrent clients. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgRequests Per Second, More Is BetterApache HTTP Server 2.4.48Concurrent Requests: 500EPYC 7713EPYC 7713 2P20K40K60K80K100KSE +/- 303.82, N = 3SE +/- 594.33, N = 3115006.9991255.791. (CC) gcc options: -shared -fPIC -O2
OpenBenchmarking.orgRequests Per Second Per Watt, More Is BetterApache HTTP Server 2.4.48Concurrent Requests: 500EPYC 7713EPYC 7713 2P120240360480600566.88226.90
MinAvgMaxEPYC 771337.8202.9225.4EPYC 7713 2P80.5402.2431.7OpenBenchmarking.orgWatts, Fewer Is BetterApache HTTP Server 2.4.48CPU Power Consumption Monitor1102203304405501. EPYC 7713: Approximate power consumption of 19679 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 38744 Joules per run.

nginx

This is a benchmark of the lightweight Nginx HTTP(S) web-server. This Nginx web server benchmark test profile makes use of the Golang "Bombardier" program for facilitating the HTTP requests over a fixed period time with a configurable number of concurrent clients. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgRequests Per Second, More Is Betternginx 1.21.1Concurrent Requests: 500EPYC 7713EPYC 7713 2P40K80K120K160K200KSE +/- 283.75, N = 3SE +/- 154.59, N = 3173899.1990312.001. (CC) gcc options: -lcrypt -lz -O3 -march=native
OpenBenchmarking.orgRequests Per Second Per Watt, More Is Betternginx 1.21.1Concurrent Requests: 500EPYC 7713EPYC 7713 2P20040060080010001159.00286.39
MinAvgMaxEPYC 771337.6150.0162.3EPYC 7713 2P81.2315.3336.3OpenBenchmarking.orgWatts, Fewer Is Betternginx 1.21.1CPU Power Consumption Monitor801602403204001. EPYC 7713: Approximate power consumption of 14554 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 30484 Joules per run.

OpenBenchmarking.orgRequests Per Second, More Is Betternginx 1.21.1Concurrent Requests: 1000EPYC 7713EPYC 7713 2P40K80K120K160K200KSE +/- 625.94, N = 3SE +/- 98.75, N = 3174524.7994018.121. (CC) gcc options: -lcrypt -lz -O3 -march=native
OpenBenchmarking.orgRequests Per Second Per Watt, More Is Betternginx 1.21.1Concurrent Requests: 1000EPYC 7713EPYC 7713 2P300600900120015001168.38302.21
MinAvgMaxEPYC 771337.4149.4161.0EPYC 7713 2P80.9311.1338.1OpenBenchmarking.orgWatts, Fewer Is Betternginx 1.21.1CPU Power Consumption Monitor801602403204001. EPYC 7713: Approximate power consumption of 14390 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 30592 Joules per run.

Sysbench

This is a benchmark of Sysbench with the built-in CPU and memory sub-tests. Sysbench is a scriptable multi-threaded benchmark tool based on LuaJIT. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgEvents Per Second, More Is BetterSysbench 1.0.20Test: CPUEPYC 7713 2PEPYC 7713110K220K330K440K550KSE +/- 659.19, N = 3SE +/- 51.07, N = 3500125.79252440.571. (CC) gcc options: -O2 -funroll-loops -rdynamic -ldl -laio -lm
OpenBenchmarking.orgEvents Per Second Per Watt, More Is BetterSysbench 1.0.20Test: CPUEPYC 7713EPYC 7713 2P300600900120015001186.321152.51
MinAvgMaxEPYC 771338.1212.8221.7EPYC 7713 2P81.4433.9456.4OpenBenchmarking.orgWatts, Fewer Is BetterSysbench 1.0.20CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 19932 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 40646 Joules per run.

Timed CPython Compilation

This test times how long it takes to build the reference Python implementation, CPython, with optimizations and LTO enabled for a release build. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed CPython Compilation 3.10.6Build Configuration: Released Build, PGO + LTO OptimizedEPYC 7713 2PEPYC 771360120180240300261.34261.37
MinAvgMaxEPYC 771337.684.0215.1EPYC 7713 2P81.1161.6395.6OpenBenchmarking.orgWatts, Fewer Is BetterTimed CPython Compilation 3.10.6CPU Power Consumption Monitor110220330440550

etcd

Etcd is a distributed, reliable key-value store intended for critical data of a distributed system. Etcd is written in Golang and part of the Cloud Native Computing Foundation (CNCF) and used by Kubernetes, Rook, CoreDNS, and other open-source software. This test profile uses Etcd's built-in benchmark to stress the PUT and RANGE performance of a single node / local system. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is Betteretcd 3.5.4Test: PUT - Connections: 100 - Clients: 100 - Average LatencyEPYC 7713EPYC 7713 2P0.5851.171.7552.342.925SE +/- 0.00, N = 3SE +/- 0.00, N = 31.32.6
MinAvgMaxEPYC 771337.8168.5202.8EPYC 7713 2P81.1304.3350.3OpenBenchmarking.orgWatts, Fewer Is Betteretcd 3.5.4CPU Power Consumption Monitor1002003004005001. EPYC 7713: Approximate power consumption of 10559 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 35505 Joules per run.

OpenBenchmarking.orgRequests/sec, More Is Betteretcd 3.5.4Test: PUT - Connections: 100 - Clients: 100EPYC 7713EPYC 7713 2P20K40K60K80K100KSE +/- 356.10, N = 3SE +/- 77.98, N = 378829.5938209.42

oneDNN

This is a test of the Intel oneDNN as an Intel-optimized library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Intel oneDNN was formerly known as DNNL (Deep Neural Network Library) and MKL-DNN before being rebranded as part of Intel oneAPI. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 2.6Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPUEPYC 7713EPYC 7713 2P16003200480064008000SE +/- 8.18, N = 3SE +/- 91.23, N = 32943.087531.12MIN: 2859.58MIN: 7148.441. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -std=c++11 -pie -ldl -lpthread
MinAvgMaxEPYC 771337.5168.7226.2EPYC 7713 2P80.3314.7424.8OpenBenchmarking.orgWatts, Fewer Is BetteroneDNN 2.6CPU Power Consumption Monitor1102203304405501. EPYC 7713: Approximate power consumption of 14118 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 29897 Joules per run.

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 2.6Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPUEPYC 7713EPYC 7713 2P16003200480064008000SE +/- 31.08, N = 3SE +/- 55.80, N = 32950.437435.34MIN: 2820.96MIN: 7037.371. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -std=c++11 -pie -ldl -lpthread
MinAvgMaxEPYC 771337.9168.5224.6EPYC 7713 2P82.0315.9436.6OpenBenchmarking.orgWatts, Fewer Is BetteroneDNN 2.6CPU Power Consumption Monitor1102203304405501. EPYC 7713: Approximate power consumption of 14154 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 29799 Joules per run.

etcd

Etcd is a distributed, reliable key-value store intended for critical data of a distributed system. Etcd is written in Golang and part of the Cloud Native Computing Foundation (CNCF) and used by Kubernetes, Rook, CoreDNS, and other open-source software. This test profile uses Etcd's built-in benchmark to stress the PUT and RANGE performance of a single node / local system. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is Betteretcd 3.5.4Test: RANGE - Connections: 500 - Clients: 100 - Average LatencyEPYC 7713EPYC 7713 2P0.5851.171.7552.342.925SE +/- 0.00, N = 3SE +/- 0.00, N = 31.22.6
MinAvgMaxEPYC 771337.7168.7202.9EPYC 7713 2P82.4302.8348.9OpenBenchmarking.orgWatts, Fewer Is Betteretcd 3.5.4CPU Power Consumption Monitor1002003004005001. EPYC 7713: Approximate power consumption of 10291 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 35028 Joules per run.

OpenBenchmarking.orgRequests/sec, More Is Betteretcd 3.5.4Test: RANGE - Connections: 500 - Clients: 100EPYC 7713EPYC 7713 2P20K40K60K80K100KSE +/- 99.62, N = 3SE +/- 133.45, N = 382098.4238506.61

OpenBenchmarking.orgms, Fewer Is Betteretcd 3.5.4Test: PUT - Connections: 500 - Clients: 100 - Average LatencyEPYC 7713EPYC 7713 2P0.5851.171.7552.342.925SE +/- 0.00, N = 3SE +/- 0.03, N = 31.22.6
MinAvgMaxEPYC 771337.8168.8203.6EPYC 7713 2P82.3302.6347.0OpenBenchmarking.orgWatts, Fewer Is Betteretcd 3.5.4CPU Power Consumption Monitor1002003004005001. EPYC 7713: Approximate power consumption of 10298 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 34800 Joules per run.

OpenBenchmarking.orgRequests/sec, More Is Betteretcd 3.5.4Test: PUT - Connections: 500 - Clients: 100EPYC 7713EPYC 7713 2P20K40K60K80K100KSE +/- 259.78, N = 3SE +/- 317.01, N = 381895.9638992.82

OpenBenchmarking.orgms, Fewer Is Betteretcd 3.5.4Test: PUT - Connections: 100 - Clients: 1000 - Average LatencyEPYC 7713EPYC 7713 2P612182430SE +/- 0.06, N = 3SE +/- 0.06, N = 312.423.7
MinAvgMaxEPYC 771337.9158.7192.4EPYC 7713 2P82.0283.8322.1OpenBenchmarking.orgWatts, Fewer Is Betteretcd 3.5.4CPU Power Consumption Monitor801602403204001. EPYC 7713: Approximate power consumption of 9788 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 30370 Joules per run.

OpenBenchmarking.orgRequests/sec, More Is Betteretcd 3.5.4Test: PUT - Connections: 100 - Clients: 1000EPYC 7713EPYC 7713 2P20K40K60K80K100KSE +/- 324.17, N = 3SE +/- 80.91, N = 379978.1941860.48

OpenBenchmarking.orgms, Fewer Is Betteretcd 3.5.4Test: RANGE - Connections: 100 - Clients: 1000 - Average LatencyEPYC 7713EPYC 7713 2P612182430SE +/- 0.03, N = 3SE +/- 0.12, N = 312.423.4
MinAvgMaxEPYC 771337.7158.6191.6EPYC 7713 2P81.6283.7323.1OpenBenchmarking.orgWatts, Fewer Is Betteretcd 3.5.4CPU Power Consumption Monitor801602403204001. EPYC 7713: Approximate power consumption of 9783 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 30262 Joules per run.

OpenBenchmarking.orgRequests/sec, More Is Betteretcd 3.5.4Test: RANGE - Connections: 100 - Clients: 1000EPYC 7713EPYC 7713 2P20K40K60K80K100KSE +/- 303.55, N = 3SE +/- 218.72, N = 380131.8042305.62

ebizzy

This is a test of ebizzy, a program to generate workloads resembling web server workloads. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgRecords/s, More Is Betterebizzy 0.3EPYC 7713 2PEPYC 7713100K200K300K400K500KSE +/- 5345.80, N = 15SE +/- 2042.62, N = 94532582464941. (CC) gcc options: -pthread -lpthread -O3 -march=native
OpenBenchmarking.orgRecords/s Per Watt, More Is Betterebizzy 0.3EPYC 7713EPYC 7713 2P300600900120015001324.991178.37
MinAvgMaxEPYC 771337.6186.0227.6EPYC 7713 2P80.6384.6460.9OpenBenchmarking.orgWatts, Fewer Is Betterebizzy 0.3CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 4444 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 9206 Joules per run.

Node.js V8 Web Tooling Benchmark

Running the V8 project's Web-Tooling-Benchmark under Node.js. The Web-Tooling-Benchmark stresses JavaScript-related workloads common to web developers like Babel and TypeScript and Babylon. This test profile can test the system's JavaScript performance with Node.js. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgruns/s, More Is BetterNode.js V8 Web Tooling BenchmarkEPYC 7713EPYC 7713 2P3691215SE +/- 0.06, N = 3SE +/- 0.12, N = 310.6010.50
OpenBenchmarking.orgruns/s Per Watt, More Is BetterNode.js V8 Web Tooling BenchmarkEPYC 7713EPYC 7713 2P0.03060.06120.09180.12240.1530.1360.069
MinAvgMaxEPYC 771337.377.894.4EPYC 7713 2P81.1152.3167.8OpenBenchmarking.orgWatts, Fewer Is BetterNode.js V8 Web Tooling BenchmarkCPU Power Consumption Monitor501001502002501. EPYC 7713: Approximate power consumption of 6538 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 12796 Joules per run.

oneDNN

This is a test of the Intel oneDNN as an Intel-optimized library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Intel oneDNN was formerly known as DNNL (Deep Neural Network Library) and MKL-DNN before being rebranded as part of Intel oneAPI. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 2.6Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPUEPYC 7713EPYC 7713 2P6001200180024003000SE +/- 12.45, N = 3SE +/- 23.56, N = 31266.202840.33MIN: 1204.11MIN: 2353.161. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -std=c++11 -pie -ldl -lpthread
MinAvgMaxEPYC 771337.7177.4223.4EPYC 7713 2P81.0327.5425.5OpenBenchmarking.orgWatts, Fewer Is BetteroneDNN 2.6CPU Power Consumption Monitor1102203304405501. EPYC 7713: Approximate power consumption of 14194 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 27186 Joules per run.

etcd

Etcd is a distributed, reliable key-value store intended for critical data of a distributed system. Etcd is written in Golang and part of the Cloud Native Computing Foundation (CNCF) and used by Kubernetes, Rook, CoreDNS, and other open-source software. This test profile uses Etcd's built-in benchmark to stress the PUT and RANGE performance of a single node / local system. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is Betteretcd 3.5.4Test: PUT - Connections: 500 - Clients: 1000 - Average LatencyEPYC 7713EPYC 7713 2P510152025SE +/- 0.03, N = 3SE +/- 0.12, N = 311.922.8
MinAvgMaxEPYC 771337.8164.0205.0EPYC 7713 2P80.9293.2347.5OpenBenchmarking.orgWatts, Fewer Is Betteretcd 3.5.4CPU Power Consumption Monitor1002003004005001. EPYC 7713: Approximate power consumption of 9785 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 30296 Joules per run.

OpenBenchmarking.orgRequests/sec, More Is Betteretcd 3.5.4Test: PUT - Connections: 500 - Clients: 1000EPYC 7713EPYC 7713 2P20K40K60K80K100KSE +/- 327.59, N = 3SE +/- 202.15, N = 383927.6643696.76

OpenBenchmarking.orgms, Fewer Is Betteretcd 3.5.4Test: RANGE - Connections: 500 - Clients: 1000 - Average LatencyEPYC 7713EPYC 7713 2P510152025SE +/- 0.06, N = 3SE +/- 0.07, N = 311.822.6
MinAvgMaxEPYC 771337.8163.8200.2EPYC 7713 2P81.7292.8341.9OpenBenchmarking.orgWatts, Fewer Is Betteretcd 3.5.4CPU Power Consumption Monitor801602403204001. EPYC 7713: Approximate power consumption of 9716 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 30158 Joules per run.

OpenBenchmarking.orgRequests/sec, More Is Betteretcd 3.5.4Test: RANGE - Connections: 500 - Clients: 1000EPYC 7713EPYC 7713 2P20K40K60K80K100KSE +/- 408.33, N = 3SE +/- 121.36, N = 384602.7844040.71

Blender

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.3Blend File: Pabellon Barcelona - Compute: CPU-OnlyEPYC 7713 2PEPYC 771320406080100SE +/- 0.25, N = 3SE +/- 0.08, N = 353.8896.30
MinAvgMaxEPYC 771338.1219.6230.3EPYC 7713 2P80.9423.9461.5OpenBenchmarking.orgWatts, Fewer Is BetterBlender 3.3CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 22107 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 24585 Joules per run.

OpenVINO

This is a test of the Intel OpenVINO, a toolkit around neural networks, using its built-in benchmarking support and analyzing the throughput and latency for various models. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2022.2.devModel: Person Detection FP16 - Device: CPUEPYC 7713EPYC 7713 2P10002000300040005000SE +/- 8.17, N = 3SE +/- 16.64, N = 34601.784777.15MIN: 2414.7 / MAX: 5219.01MIN: 2420.5 / MAX: 6044.241. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared
MinAvgMaxEPYC 771338.1210.0230.3EPYC 7713 2P80.4408.8461.3OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2022.2.devCPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 15680 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 32431 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2022.2.devModel: Person Detection FP16 - Device: CPUEPYC 7713 2PEPYC 77133691215SE +/- 0.05, N = 3SE +/- 0.02, N = 313.106.821. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared

LZ4 Compression

This test measures the time needed to compress/decompress a sample file (an Ubuntu ISO) using LZ4 compression. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMB/s, More Is BetterLZ4 Compression 1.9.3Compression Level: 9 - Decompression SpeedEPYC 7713EPYC 7713 2P3K6K9K12K15KSE +/- 33.09, N = 4SE +/- 34.05, N = 314156.710851.41. (CC) gcc options: -O3
OpenBenchmarking.orgMB/s Per Watt, More Is BetterLZ4 Compression 1.9.3Compression Level: 9 - Decompression SpeedEPYC 7713EPYC 7713 2P4080120160200178.9867.29
MinAvgMaxEPYC 771337.379.1104.8EPYC 7713 2P80.7161.3219.2OpenBenchmarking.orgWatts, Fewer Is BetterLZ4 Compression 1.9.3CPU Power Consumption Monitor601201802403001. EPYC 7713: Approximate power consumption of 5161 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 10859 Joules per run.

OpenBenchmarking.orgMB/s, More Is BetterLZ4 Compression 1.9.3Compression Level: 9 - Compression SpeedEPYC 7713EPYC 7713 2P1224364860SE +/- 0.66, N = 4SE +/- 0.20, N = 353.8952.511. (CC) gcc options: -O3

OpenVINO

This is a test of the Intel OpenVINO, a toolkit around neural networks, using its built-in benchmarking support and analyzing the throughput and latency for various models. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2022.2.devModel: Person Detection FP32 - Device: CPUEPYC 7713EPYC 7713 2P10002000300040005000SE +/- 5.38, N = 3SE +/- 9.45, N = 34600.944774.37MIN: 2381.08 / MAX: 5204.91MIN: 2371.17 / MAX: 6026.051. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared
MinAvgMaxEPYC 771338.1212.3230.5EPYC 7713 2P81.0412.0461.4OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2022.2.devCPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 15566 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 32134 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2022.2.devModel: Person Detection FP32 - Device: CPUEPYC 7713 2PEPYC 77133691215SE +/- 0.03, N = 3SE +/- 0.01, N = 313.096.811. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared

Dragonflydb

Dragonfly is an open-source database server that is a "modern Redis replacement" that aims to be the fastest memory store while being compliant with the Redis and Memcached protocols. For benchmarking Dragonfly, Memtier_benchmark is used as a NoSQL Redis/Memcache traffic generation plus benchmarking tool developed by Redis Labs. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgOps/sec, More Is BetterDragonflydb 0.6Clients: 50 - Set To Get Ratio: 1:5EPYC 7713EPYC 7713 2P800K1600K2400K3200K4000KSE +/- 3261.92, N = 3SE +/- 634.91, N = 33599739.57724442.871. (CXX) g++ options: -O2 -levent_openssl -levent -lcrypto -lssl -lpthread -lz -lpcre
OpenBenchmarking.orgOps/sec Per Watt, More Is BetterDragonflydb 0.6Clients: 50 - Set To Get Ratio: 1:5EPYC 7713EPYC 7713 2P4K8K12K16K20K19066.012246.96
MinAvgMaxEPYC 771337.6188.8212.4EPYC 7713 2P81.4322.4362.5OpenBenchmarking.orgWatts, Fewer Is BetterDragonflydb 0.6CPU Power Consumption Monitor1002003004005001. EPYC 7713: Approximate power consumption of 13342 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 22676 Joules per run.

OpenBenchmarking.orgOps/sec, More Is BetterDragonflydb 0.6Clients: 50 - Set To Get Ratio: 1:1EPYC 7713EPYC 7713 2P700K1400K2100K2800K3500KSE +/- 14735.29, N = 3SE +/- 1586.54, N = 33382696.79724466.221. (CXX) g++ options: -O2 -levent_openssl -levent -lcrypto -lssl -lpthread -lz -lpcre
OpenBenchmarking.orgOps/sec Per Watt, More Is BetterDragonflydb 0.6Clients: 50 - Set To Get Ratio: 1:1EPYC 7713EPYC 7713 2P4K8K12K16K20K18071.082245.75
MinAvgMaxEPYC 771337.9187.2210.6EPYC 7713 2P81.0322.6363.1OpenBenchmarking.orgWatts, Fewer Is BetterDragonflydb 0.6CPU Power Consumption Monitor1002003004005001. EPYC 7713: Approximate power consumption of 13415 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 22582 Joules per run.

OpenBenchmarking.orgOps/sec, More Is BetterDragonflydb 0.6Clients: 50 - Set To Get Ratio: 5:1EPYC 7713EPYC 7713 2P700K1400K2100K2800K3500KSE +/- 917.19, N = 3SE +/- 986.76, N = 33244708.36724635.931. (CXX) g++ options: -O2 -levent_openssl -levent -lcrypto -lssl -lpthread -lz -lpcre
OpenBenchmarking.orgOps/sec Per Watt, More Is BetterDragonflydb 0.6Clients: 50 - Set To Get Ratio: 5:1EPYC 7713EPYC 7713 2P4K8K12K16K20K17435.642257.57
MinAvgMaxEPYC 771337.8186.1209.2EPYC 7713 2P80.7321.0361.2OpenBenchmarking.orgWatts, Fewer Is BetterDragonflydb 0.6CPU Power Consumption Monitor1002003004005001. EPYC 7713: Approximate power consumption of 13213 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 22469 Joules per run.

OpenVINO

This is a test of the Intel OpenVINO, a toolkit around neural networks, using its built-in benchmarking support and analyzing the throughput and latency for various models. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2022.2.devModel: Face Detection FP16-INT8 - Device: CPUEPYC 7713EPYC 7713 2P30060090012001500SE +/- 1.10, N = 3SE +/- 2.23, N = 31293.511388.41MIN: 1118.15 / MAX: 1339.17MIN: 1233.32 / MAX: 1772.31. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared
MinAvgMaxEPYC 771337.8212.9230.3EPYC 7713 2P80.7412.9460.7OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2022.2.devCPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 14619 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 29729 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2022.2.devModel: Face Detection FP16-INT8 - Device: CPUEPYC 7713 2PEPYC 77131020304050SE +/- 0.04, N = 3SE +/- 0.02, N = 345.8424.591. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared

PyPerformance

PyPerformance is the reference Python performance benchmark suite. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: python_startupEPYC 7713EPYC 7713 2P246810SE +/- 0.00, N = 3SE +/- 0.00, N = 37.647.71
MinAvgMaxEPYC 771337.373.576.0EPYC 7713 2P80.0147.6155.9OpenBenchmarking.orgWatts, Fewer Is BetterPyPerformance 1.0.0CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 5098 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 10284 Joules per run.

PJSIP

PJSIP is a free and open source multimedia communication library written in C language implementing standard based protocols such as SIP, SDP, RTP, STUN, TURN, and ICE. It combines signaling protocol (SIP) with rich multimedia framework and NAT traversal functionality into high level API that is portable and suitable for almost any type of systems ranging from desktops, embedded systems, to mobile handsets. This test profile is making use of pjsip-perf with both the client/server on teh system. More details on the PJSIP benchmark at https://www.pjsip.org/high-performance-sip.htm Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgResponses Per Second, More Is BetterPJSIP 2.11Method: INVITEEPYC 7713 2PEPYC 771310002000300040005000SE +/- 8.69, N = 3SE +/- 16.00, N = 3469246901. (CC) gcc options: -lavformat -lavcodec -lswscale -lavutil -lstdc++ -lopus -lssl -lcrypto -luuid -lm -lrt -lpthread -lasound -O2
OpenBenchmarking.orgResponses Per Second Per Watt, More Is BetterPJSIP 2.11Method: INVITEEPYC 7713EPYC 7713 2P132639526557.2129.75
MinAvgMaxEPYC 771337.482.093.3EPYC 7713 2P80.4157.7174.4OpenBenchmarking.orgWatts, Fewer Is BetterPJSIP 2.11CPU Power Consumption Monitor501001502002501. EPYC 7713: Approximate power consumption of 5739 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 10934 Joules per run.

OpenBenchmarking.orgResponses Per Second, More Is BetterPJSIP 2.11Method: OPTIONS, StatefulEPYC 7713EPYC 7713 2P2K4K6K8K10KSE +/- 72.18, N = 3SE +/- 15.60, N = 3911588191. (CC) gcc options: -lavformat -lavcodec -lswscale -lavutil -lstdc++ -lopus -lssl -lcrypto -luuid -lm -lrt -lpthread -lasound -O2
OpenBenchmarking.orgResponses Per Second Per Watt, More Is BetterPJSIP 2.11Method: OPTIONS, StatefulEPYC 7713EPYC 7713 2P306090120150114.9257.42
MinAvgMaxEPYC 771337.479.395.7EPYC 7713 2P80.9153.6170.4OpenBenchmarking.orgWatts, Fewer Is BetterPJSIP 2.11CPU Power Consumption Monitor501001502002501. EPYC 7713: Approximate power consumption of 5393 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 10495 Joules per run.

OpenVINO

This is a test of the Intel OpenVINO, a toolkit around neural networks, using its built-in benchmarking support and analyzing the throughput and latency for various models. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2022.2.devModel: Machine Translation EN To DE FP16 - Device: CPUEPYC 7713EPYC 7713 2P60120180240300SE +/- 0.42, N = 3SE +/- 0.60, N = 3274.57292.82MIN: 116.27 / MAX: 347.98MIN: 139.85 / MAX: 449.451. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared
MinAvgMaxEPYC 771338.1212.8230.3EPYC 7713 2P81.0416.9461.0OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2022.2.devCPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 14186 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 28491 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2022.2.devModel: Machine Translation EN To DE FP16 - Device: CPUEPYC 7713 2PEPYC 771350100150200250SE +/- 0.45, N = 3SE +/- 0.19, N = 3218.13116.331. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2022.2.devModel: Person Vehicle Bike Detection FP16 - Device: CPUEPYC 7713EPYC 7713 2P510152025SE +/- 0.01, N = 3SE +/- 0.03, N = 320.8222.16MIN: 12.46 / MAX: 44.77MIN: 11.48 / MAX: 97.441. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared
MinAvgMaxEPYC 771338.1214.6230.3EPYC 7713 2P80.5419.4460.6OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2022.2.devCPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 14162 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 28796 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2022.2.devModel: Person Vehicle Bike Detection FP16 - Device: CPUEPYC 7713 2PEPYC 77136001200180024003000SE +/- 3.95, N = 3SE +/- 0.60, N = 32884.441535.081. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared

LuxCoreRender

LuxCoreRender is an open-source 3D physically based renderer formerly known as LuxRender. LuxCoreRender supports CPU-based rendering as well as GPU acceleration via OpenCL, NVIDIA CUDA, and NVIDIA OptiX interfaces. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgM samples/sec, More Is BetterLuxCoreRender 2.6Scene: DLSC - Acceleration: CPUEPYC 7713 2PEPYC 77133691215SE +/- 0.06, N = 3SE +/- 0.04, N = 312.238.33MIN: 11.53 / MAX: 15.5MIN: 8.11 / MAX: 9.42
OpenBenchmarking.orgM samples/sec Per Watt, More Is BetterLuxCoreRender 2.6Scene: DLSC - Acceleration: CPUEPYC 7713EPYC 7713 2P0.00880.01760.02640.03520.0440.0390.029
MinAvgMaxEPYC 771338.1214.7231.5EPYC 7713 2P79.9428.7460.3OpenBenchmarking.orgWatts, Fewer Is BetterLuxCoreRender 2.6CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 14168 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 27862 Joules per run.

OpenVINO

This is a test of the Intel OpenVINO, a toolkit around neural networks, using its built-in benchmarking support and analyzing the throughput and latency for various models. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2022.2.devModel: Vehicle Detection FP16-INT8 - Device: CPUEPYC 7713EPYC 7713 2P510152025SE +/- 0.02, N = 3SE +/- 0.01, N = 318.9320.38MIN: 11.41 / MAX: 69.86MIN: 8.69 / MAX: 87.481. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared
MinAvgMaxEPYC 771338.1217.2230.3EPYC 7713 2P80.8430.5460.5OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2022.2.devCPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 13971 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 27980 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2022.2.devModel: Vehicle Detection FP16-INT8 - Device: CPUEPYC 7713 2PEPYC 77137001400210028003500SE +/- 2.08, N = 3SE +/- 1.36, N = 33136.141689.181. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2022.2.devModel: Vehicle Detection FP16 - Device: CPUEPYC 7713EPYC 7713 2P816243240SE +/- 0.14, N = 3SE +/- 0.25, N = 334.1735.75MIN: 17.07 / MAX: 68.69MIN: 18.65 / MAX: 151.871. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared
MinAvgMaxEPYC 771338.1215.9230.3EPYC 7713 2P81.0431.0461.0OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2022.2.devCPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 13963 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 28015 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2022.2.devModel: Vehicle Detection FP16 - Device: CPUEPYC 7713 2PEPYC 7713400800120016002000SE +/- 12.59, N = 3SE +/- 3.92, N = 31788.34935.581. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2022.2.devModel: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPUEPYC 7713EPYC 7713 2P0.47480.94961.42441.89922.374SE +/- 0.00, N = 3SE +/- 0.01, N = 31.062.11MIN: 0.55 / MAX: 15.24MIN: 0.53 / MAX: 66.381. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared
MinAvgMaxEPYC 771338.1214.6229.9EPYC 7713 2P80.8422.9454.9OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2022.2.devCPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 13876 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 27487 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2022.2.devModel: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPUEPYC 7713 2PEPYC 771312K24K36K48K60KSE +/- 378.72, N = 3SE +/- 78.11, N = 357763.8949691.031. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2022.2.devModel: Weld Porosity Detection FP16-INT8 - Device: CPUEPYC 7713EPYC 7713 2P714212835SE +/- 0.02, N = 3SE +/- 0.01, N = 325.9728.05MIN: 12.2 / MAX: 39.52MIN: 10.65 / MAX: 66.971. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared
MinAvgMaxEPYC 771338.0216.7230.3EPYC 7713 2P80.0432.5460.9OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2022.2.devCPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 13944 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 28114 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2022.2.devModel: Weld Porosity Detection FP16-INT8 - Device: CPUEPYC 7713 2PEPYC 771310002000300040005000SE +/- 0.96, N = 3SE +/- 1.64, N = 34559.422462.401. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2022.2.devModel: Age Gender Recognition Retail 0013 FP16 - Device: CPUEPYC 7713EPYC 7713 2P0.73581.47162.20742.94323.679SE +/- 0.00, N = 3SE +/- 0.06, N = 32.033.27MIN: 0.97 / MAX: 18.02MIN: 0.88 / MAX: 81.361. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared
MinAvgMaxEPYC 771338.3215.1230.2EPYC 7713 2P80.5411.0459.0OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2022.2.devCPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 13837 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 26440 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2022.2.devModel: Age Gender Recognition Retail 0013 FP16 - Device: CPUEPYC 7713 2PEPYC 77138K16K24K32K40KSE +/- 533.11, N = 3SE +/- 11.83, N = 337561.0730742.171. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2022.2.devModel: Weld Porosity Detection FP16 - Device: CPUEPYC 7713EPYC 7713 2P816243240SE +/- 0.01, N = 3SE +/- 0.05, N = 331.0233.55MIN: 15.59 / MAX: 49.44MIN: 14.43 / MAX: 183.111. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared
MinAvgMaxEPYC 771338.2216.4230.3EPYC 7713 2P80.3430.2460.6OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2022.2.devCPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 14066 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 28104 Joules per run.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2022.2.devModel: Weld Porosity Detection FP16 - Device: CPUEPYC 7713 2PEPYC 7713400800120016002000SE +/- 2.71, N = 3SE +/- 0.14, N = 31905.511030.761. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared

Facebook RocksDB

OpenBenchmarking.orgOp/s, More Is BetterFacebook RocksDB 7.5.3Test: Update RandomEPYC 7713EPYC 7713 2P80K160K240K320K400KSE +/- 2028.78, N = 3SE +/- 2065.20, N = 33583363046541. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread
OpenBenchmarking.orgOp/s Per Watt, More Is BetterFacebook RocksDB 7.5.3Test: Update RandomEPYC 7713EPYC 7713 2P50010001500200025002226.861020.14
MinAvgMaxEPYC 771338.0160.9201.2EPYC 7713 2P81.9298.6330.6OpenBenchmarking.orgWatts, Fewer Is BetterFacebook RocksDB 7.5.3CPU Power Consumption Monitor801602403204001. EPYC 7713: Approximate power consumption of 10352 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 19412 Joules per run.

OpenBenchmarking.orgOp/s, More Is BetterFacebook RocksDB 7.5.3Test: Read Random Write RandomEPYC 7713EPYC 7713 2P800K1600K2400K3200K4000KSE +/- 34091.93, N = 3SE +/- 5555.94, N = 3356184129221671. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread
OpenBenchmarking.orgOp/s Per Watt, More Is BetterFacebook RocksDB 7.5.3Test: Read Random Write RandomEPYC 7713EPYC 7713 2P4K8K12K16K20K17058.247436.17
MinAvgMaxEPYC 771338.0208.8223.5EPYC 7713 2P80.8393.0445.3OpenBenchmarking.orgWatts, Fewer Is BetterFacebook RocksDB 7.5.3CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 13364 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 25019 Joules per run.

OpenBenchmarking.orgOp/s, More Is BetterFacebook RocksDB 7.5.3Test: Read While WritingEPYC 7713 2PEPYC 77133M6M9M12M15MSE +/- 195708.96, N = 3SE +/- 66207.97, N = 31456467391821671. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread
OpenBenchmarking.orgOp/s Per Watt, More Is BetterFacebook RocksDB 7.5.3Test: Read While WritingEPYC 7713EPYC 7713 2P9K18K27K36K45K43702.2034647.59
MinAvgMaxEPYC 771337.9210.1230.3EPYC 7713 2P80.8420.4460.7OpenBenchmarking.orgWatts, Fewer Is BetterFacebook RocksDB 7.5.3CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 13517 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 26903 Joules per run.

GraphicsMagick

This is a test of GraphicsMagick with its OpenMP implementation that performs various imaging tests on a sample 6000x4000 pixel JPEG image. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgIterations Per Minute, More Is BetterGraphicsMagick 1.3.38Operation: SharpenEPYC 7713 2PEPYC 77132004006008001000SE +/- 1.45, N = 3SE +/- 2.52, N = 37795111. (CC) gcc options: -fopenmp -O2 -ljbig -lwebp -lwebpmux -ltiff -lfreetype -ljpeg -lXext -lSM -lICE -lX11 -llzma -lbz2 -lxml2 -lz -lm -lpthread
OpenBenchmarking.orgIterations Per Minute Per Watt, More Is BetterGraphicsMagick 1.3.38Operation: SharpenEPYC 7713EPYC 7713 2P0.55461.10921.66382.21842.7732.4651.945
MinAvgMaxEPYC 771337.3207.3222.7EPYC 7713 2P80.9400.5428.8OpenBenchmarking.orgWatts, Fewer Is BetterGraphicsMagick 1.3.38CPU Power Consumption Monitor1102203304405501. EPYC 7713: Approximate power consumption of 13336 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 25500 Joules per run.

OpenSSL

OpenSSL is an open-source toolkit that implements SSL (Secure Sockets Layer) and TLS (Transport Layer Security) protocols. This test profile makes use of the built-in "openssl speed" benchmarking capabilities. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgverify/s, More Is BetterOpenSSL 3.0Algorithm: RSA4096EPYC 7713 2PEPYC 7713400K800K1200K1600K2000KSE +/- 338.78, N = 3SE +/- 47.96, N = 31638050.5825904.11. (CC) gcc options: -pthread -m64 -O3 -lssl -lcrypto -ldl
OpenBenchmarking.orgverify/s Per Watt, More Is BetterOpenSSL 3.0Algorithm: RSA4096EPYC 7713EPYC 7713 2P80016002400320040003819.783763.26
MinAvgMaxEPYC 771337.0216.2230.3EPYC 7713 2P79.5435.3461.5OpenBenchmarking.orgWatts, Fewer Is BetterOpenSSL 3.0CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 13766 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 27858 Joules per run.

OpenBenchmarking.orgsign/s, More Is BetterOpenSSL 3.0Algorithm: RSA4096EPYC 7713 2PEPYC 77135K10K15K20K25KSE +/- 3.07, N = 3SE +/- 1.58, N = 325009.612613.11. (CC) gcc options: -pthread -m64 -O3 -lssl -lcrypto -ldl

GraphicsMagick

This is a test of GraphicsMagick with its OpenMP implementation that performs various imaging tests on a sample 6000x4000 pixel JPEG image. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgIterations Per Minute, More Is BetterGraphicsMagick 1.3.38Operation: EnhancedEPYC 7713 2PEPYC 771330060090012001500SE +/- 9.60, N = 3SE +/- 1.15, N = 313449411. (CC) gcc options: -fopenmp -O2 -ljbig -lwebp -lwebpmux -ltiff -lfreetype -ljpeg -lXext -lSM -lICE -lX11 -llzma -lbz2 -lxml2 -lz -lm -lpthread
OpenBenchmarking.orgIterations Per Minute Per Watt, More Is BetterGraphicsMagick 1.3.38Operation: EnhancedEPYC 7713EPYC 7713 2P1.01862.03723.05584.07445.0934.5273.370
MinAvgMaxEPYC 771337.3207.9221.1EPYC 7713 2P81.1398.8429.2OpenBenchmarking.orgWatts, Fewer Is BetterGraphicsMagick 1.3.38CPU Power Consumption Monitor1102203304405501. EPYC 7713: Approximate power consumption of 13234 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 25521 Joules per run.

Facebook RocksDB

OpenBenchmarking.orgOp/s, More Is BetterFacebook RocksDB 7.5.3Test: Random ReadEPYC 7713 2PEPYC 7713100M200M300M400M500MSE +/- 753688.43, N = 3SE +/- 482617.87, N = 34801753322420747981. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread
OpenBenchmarking.orgOp/s Per Watt, More Is BetterFacebook RocksDB 7.5.3Test: Random ReadEPYC 7713EPYC 7713 2P200K400K600K800K1000K1111169.801104494.04
MinAvgMaxEPYC 771337.8217.9230.3EPYC 7713 2P81.0434.7461.2OpenBenchmarking.orgWatts, Fewer Is BetterFacebook RocksDB 7.5.3CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 13870 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 27824 Joules per run.

GraphicsMagick

This is a test of GraphicsMagick with its OpenMP implementation that performs various imaging tests on a sample 6000x4000 pixel JPEG image. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgIterations Per Minute, More Is BetterGraphicsMagick 1.3.38Operation: RotateEPYC 7713EPYC 7713 2P160320480640800SE +/- 1.00, N = 3SE +/- 7.54, N = 37467301. (CC) gcc options: -fopenmp -O2 -ljbig -lwebp -lwebpmux -ltiff -lfreetype -ljpeg -lXext -lSM -lICE -lX11 -llzma -lbz2 -lxml2 -lz -lm -lpthread
OpenBenchmarking.orgIterations Per Minute Per Watt, More Is BetterGraphicsMagick 1.3.38Operation: RotateEPYC 7713EPYC 7713 2P369121510.0904.934
MinAvgMaxEPYC 771337.273.979.8EPYC 7713 2P42.7148.0157.7OpenBenchmarking.orgWatts, Fewer Is BetterGraphicsMagick 1.3.38CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 4732 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 9469 Joules per run.

OpenBenchmarking.orgIterations Per Minute, More Is BetterGraphicsMagick 1.3.38Operation: HWB Color SpaceEPYC 7713EPYC 7713 2P30060090012001500SE +/- 2.96, N = 3SE +/- 4.67, N = 3130310931. (CC) gcc options: -fopenmp -O2 -ljbig -lwebp -lwebpmux -ltiff -lfreetype -ljpeg -lXext -lSM -lICE -lX11 -llzma -lbz2 -lxml2 -lz -lm -lpthread
OpenBenchmarking.orgIterations Per Minute Per Watt, More Is BetterGraphicsMagick 1.3.38Operation: HWB Color SpaceEPYC 7713EPYC 7713 2P369121510.8264.281
MinAvgMaxEPYC 771337.4120.4127.5EPYC 7713 2P81.1255.3273.4OpenBenchmarking.orgWatts, Fewer Is BetterGraphicsMagick 1.3.38CPU Power Consumption Monitor701402102803501. EPYC 7713: Approximate power consumption of 7703 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 16256 Joules per run.

Etcpak

Etcpack is the self-proclaimed "fastest ETC compressor on the planet" with focused on providing open-source, very fast ETC and S3 texture compression support. The test profile uses a 8K x 8K game texture as a sample input. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMpx/s, More Is BetterEtcpak 1.0Benchmark: Single-Threaded - Configuration: ETC2EPYC 7713EPYC 7713 2P50100150200250SE +/- 0.14, N = 3SE +/- 0.03, N = 3230.43229.641. (CXX) g++ options: -O3 -march=native -std=c++11 -lpthread
OpenBenchmarking.orgMpx/s Per Watt, More Is BetterEtcpak 1.0Benchmark: Single-Threaded - Configuration: ETC2EPYC 7713EPYC 7713 2P0.70851.4172.12552.8343.54253.1491.567
MinAvgMaxEPYC 771337.173.275.8EPYC 7713 2P81.1146.5155.4OpenBenchmarking.orgWatts, Fewer Is BetterEtcpak 1.0CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 4635 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 9377 Joules per run.

Coremark

This is a test of EEMBC CoreMark processor benchmark. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgIterations/Sec, More Is BetterCoremark 1.0CoreMark Size 666 - Iterations Per SecondEPYC 7713 2PEPYC 7713900K1800K2700K3600K4500KSE +/- 2413.85, N = 3SE +/- 10186.88, N = 34105747.142045229.651. (CC) gcc options: -O2 -lrt" -lrt
OpenBenchmarking.orgIterations/Sec Per Watt, More Is BetterCoremark 1.0CoreMark Size 666 - Iterations Per SecondEPYC 7713 2PEPYC 77132K4K6K8K10K10250.929708.04
MinAvgMaxEPYC 771337.6210.7229.9EPYC 7713 2P80.2400.5461.7OpenBenchmarking.orgWatts, Fewer Is BetterCoremark 1.0CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 17345 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 17623 Joules per run.

Blender

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.3Blend File: Classroom - Compute: CPU-OnlyEPYC 7713 2PEPYC 771320406080100SE +/- 0.23, N = 3SE +/- 0.09, N = 340.8977.14
MinAvgMaxEPYC 771337.8218.4230.6EPYC 7713 2P80.3418.9460.7OpenBenchmarking.orgWatts, Fewer Is BetterBlender 3.3CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 17760 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 18710 Joules per run.

GPAW

GPAW is a density-functional theory (DFT) Python code based on the projector-augmented wave (PAW) method and the atomic simulation environment (ASE). Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterGPAW 22.1Input: Carbon NanotubeEPYC 7713 2PEPYC 77131632486480SE +/- 0.25, N = 3SE +/- 0.19, N = 343.8671.661. (CC) gcc options: -shared -fwrapv -O2 -lxc -lblas -lmpi
MinAvgMaxEPYC 771337.0212.9229.5EPYC 7713 2P81.6422.5460.4OpenBenchmarking.orgWatts, Fewer Is BetterGPAW 22.1CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 16255 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 20841 Joules per run.

CloverLeaf

CloverLeaf is a Lagrangian-Eulerian hydrodynamics benchmark. This test profile currently makes use of CloverLeaf's OpenMP version and benchmarked with the clover_bm.in input file (Problem 5). Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterCloverLeafLagrangian-Eulerian HydrodynamicsEPYC 7713EPYC 7713 2P510152025SE +/- 0.07, N = 4SE +/- 0.36, N = 1512.0019.441. (F9X) gfortran options: -O3 -march=native -funroll-loops -fopenmp
MinAvgMaxEPYC 771337.4174.1223.2EPYC 7713 2P80.3347.2423.2OpenBenchmarking.orgWatts, Fewer Is BetterCloverLeafCPU Power Consumption Monitor1102203304405501. EPYC 7713: Approximate power consumption of 2741 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 8125 Joules per run.

simdjson

This is a benchmark of SIMDJSON, a high performance JSON parser. SIMDJSON aims to be the fastest JSON parser and is used by projects like Microsoft FishStore, Yandex ClickHouse, Shopify, and others. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgGB/s, More Is Bettersimdjson 2.0Throughput Test: KostyaEPYC 7713EPYC 7713 2P0.65251.3051.95752.613.2625SE +/- 0.00, N = 3SE +/- 0.00, N = 32.92.91. (CXX) g++ options: -O3
OpenBenchmarking.orgGB/s Per Watt, More Is Bettersimdjson 2.0Throughput Test: KostyaEPYC 7713EPYC 7713 2P0.00720.01440.02160.02880.0360.0320.018
MinAvgMaxEPYC 771337.391.897.2EPYC 7713 2P80.7165.0173.5OpenBenchmarking.orgWatts, Fewer Is Bettersimdjson 2.0CPU Power Consumption Monitor501001502002501. EPYC 7713: Approximate power consumption of 5475 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 9898 Joules per run.

Build2

This test profile measures the time to bootstrap/install the build2 C++ build toolchain from source. Build2 is a cross-platform build toolchain for C/C++ code and features Cargo-like features. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterBuild2 0.13Time To CompileEPYC 7713 2PEPYC 77131326395265SE +/- 0.25, N = 3SE +/- 0.18, N = 353.2358.96
MinAvgMaxEPYC 771337.3134.2230.0EPYC 7713 2P79.9228.4460.7OpenBenchmarking.orgWatts, Fewer Is BetterBuild2 0.13CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 8456 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 13021 Joules per run.

AOM AV1

This is a test of the AOMedia AV1 encoder (libaom) developed by AOMedia and Google as the AV1 Codec Library. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterAOM AV1 3.4Encoder Mode: Speed 10 Realtime - Input: Bosphorus 4KEPYC 7713EPYC 7713 2P1428425670SE +/- 0.51, N = 15SE +/- 0.53, N = 1563.9057.041. (CXX) g++ options: -O3 -std=c++11 -U_FORTIFY_SOURCE -lm
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterAOM AV1 3.4Encoder Mode: Speed 10 Realtime - Input: Bosphorus 4KEPYC 7713EPYC 7713 2P0.14540.29080.43620.58160.7270.6460.298
MinAvgMaxEPYC 771337.398.9126.4EPYC 7713 2P78.4191.2238.5OpenBenchmarking.orgWatts, Fewer Is BetterAOM AV1 3.4CPU Power Consumption Monitor601201802403001. EPYC 7713: Approximate power consumption of 1437 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 2996 Joules per run.

Natron

Natron is an open-source, cross-platform compositing software for visual effects (VFX) and motion graphics. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFPS, More Is BetterNatron 2.4.3Input: SpaceshipEPYC 7713EPYC 7713 2P246810SE +/- 0.03, N = 3SE +/- 0.02, N = 56.01.9
OpenBenchmarking.orgFPS Per Watt, More Is BetterNatron 2.4.3Input: SpaceshipEPYC 7713EPYC 7713 2P0.00920.01840.02760.03680.0460.0410.007
MinAvgMaxEPYC 771337.9145.3220.7EPYC 7713 2P79.9285.4381.5OpenBenchmarking.orgWatts, Fewer Is BetterNatron 2.4.3CPU Power Consumption Monitor1002003004005001. EPYC 7713: Approximate power consumption of 3294 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 16840 Joules per run.

Rodinia

Rodinia is a suite focused upon accelerating compute-intensive applications with accelerators. CUDA, OpenMP, and OpenCL parallel models are supported by the included applications. This profile utilizes select OpenCL, NVIDIA CUDA and OpenMP test binaries at the moment. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterRodinia 3.1Test: OpenMP LeukocyteEPYC 7713EPYC 7713 2P1122334455SE +/- 0.47, N = 4SE +/- 0.41, N = 343.3947.281. (CXX) g++ options: -O2 -lOpenCL
MinAvgMaxEPYC 771337.9160.1185.5EPYC 7713 2P80.6252.9280.7OpenBenchmarking.orgWatts, Fewer Is BetterRodinia 3.1CPU Power Consumption Monitor701402102803501. EPYC 7713: Approximate power consumption of 7566 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 12899 Joules per run.

SVT-HEVC

This is a test of the Intel Open Visual Cloud Scalable Video Technology SVT-HEVC CPU-based multi-threaded video encoder for the HEVC / H.265 video format with a sample YUV video file. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-HEVC 1.5.0Tuning: 1 - Input: Bosphorus 4KEPYC 7713 2PEPYC 771348121620SE +/- 0.14, N = 3SE +/- 0.01, N = 315.089.911. (CC) gcc options: -fPIE -fPIC -O3 -O2 -pie -rdynamic -lpthread -lrt
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterSVT-HEVC 1.5.0Tuning: 1 - Input: Bosphorus 4KEPYC 7713EPYC 7713 2P0.01040.02080.03120.04160.0520.0460.038
MinAvgMaxEPYC 771337.4214.7230.3EPYC 7713 2P80.5395.3460.6OpenBenchmarking.orgWatts, Fewer Is BetterSVT-HEVC 1.5.0CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 14028 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 18183 Joules per run.

Timed Wasmer Compilation

This test times how long it takes to compile Wasmer. Wasmer is written in the Rust programming language and is a WebAssembly runtime implementation that supports WASI and EmScripten. This test profile builds Wasmer with the Cranelift and Singlepast compiler features enabled. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed Wasmer Compilation 2.3Time To CompileEPYC 7713 2PEPYC 77131224364860SE +/- 0.51, N = 3SE +/- 0.11, N = 351.8751.971. (CC) gcc options: -m64 -ldl -lgcc_s -lutil -lrt -lpthread -lm -lc -pie -nodefaultlibs
MinAvgMaxEPYC 771337.6114.1224.6EPYC 7713 2P80.8199.6385.3OpenBenchmarking.orgWatts, Fewer Is BetterTimed Wasmer Compilation 2.3CPU Power Consumption Monitor1002003004005001. EPYC 7713: Approximate power consumption of 6391 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 11176 Joules per run.

simdjson

This is a benchmark of SIMDJSON, a high performance JSON parser. SIMDJSON aims to be the fastest JSON parser and is used by projects like Microsoft FishStore, Yandex ClickHouse, Shopify, and others. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgGB/s, More Is Bettersimdjson 2.0Throughput Test: LargeRandomEPYC 7713EPYC 7713 2P0.2250.450.6750.91.125SE +/- 0.00, N = 3SE +/- 0.00, N = 3111. (CXX) g++ options: -O3
OpenBenchmarking.orgGB/s Per Watt, More Is Bettersimdjson 2.0Throughput Test: LargeRandomEPYC 7713EPYC 7713 2P0.00270.00540.00810.01080.01350.0120.006
MinAvgMaxEPYC 771337.383.590.5EPYC 7713 2P80.6156.7166.5OpenBenchmarking.orgWatts, Fewer Is Bettersimdjson 2.0CPU Power Consumption Monitor501001502002501. EPYC 7713: Approximate power consumption of 4646 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 8725 Joules per run.

srsRAN

srsRAN is an open-source LTE/5G software radio suite created by Software Radio Systems (SRS). The srsRAN radio suite was formerly known as srsLTE and can be used for building your own software-defined radio (SDR) 4G/5G mobile network. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgUE Mb/s, More Is BettersrsRAN 22.04.1Test: 4G PHY_DL_Test 100 PRB SISO 64-QAMEPYC 7713EPYC 7713 2P306090120150SE +/- 0.23, N = 4SE +/- 0.20, N = 15143.6143.61. (CXX) g++ options: -std=c++14 -fno-strict-aliasing -march=native -mfpmath=sse -mavx2 -fvisibility=hidden -O3 -fno-trapping-math -fno-math-errno -ldl -lpthread -lm
OpenBenchmarking.orgUE Mb/s Per Watt, More Is BettersrsRAN 22.04.1Test: 4G PHY_DL_Test 100 PRB SISO 64-QAMEPYC 7713EPYC 7713 2P0.47090.94181.41271.88362.35452.0931.036
MinAvgMaxEPYC 771337.268.676.6EPYC 7713 2P80.5138.6165.0OpenBenchmarking.orgWatts, Fewer Is BettersrsRAN 22.04.1CPU Power Consumption Monitor501001502002501. EPYC 7713: Approximate power consumption of 1389 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 2808 Joules per run.

OpenBenchmarking.orgeNb Mb/s, More Is BettersrsRAN 22.04.1Test: 4G PHY_DL_Test 100 PRB SISO 64-QAMEPYC 7713EPYC 7713 2P90180270360450SE +/- 0.59, N = 4SE +/- 2.15, N = 15396.0392.31. (CXX) g++ options: -std=c++14 -fno-strict-aliasing -march=native -mfpmath=sse -mavx2 -fvisibility=hidden -O3 -fno-trapping-math -fno-math-errno -ldl -lpthread -lm

miniFE

MiniFE Finite Element is an application for unstructured implicit finite element codes. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgCG Mflops, More Is BetterminiFE 2.2Problem Size: SmallEPYC 7713 2PEPYC 77135K10K15K20K25KSE +/- 292.61, N = 15SE +/- 3.33, N = 424664.821875.11. (CXX) g++ options: -O3 -fopenmp -lmpi_cxx -lmpi
OpenBenchmarking.orgCG Mflops Per Watt, More Is BetterminiFE 2.2Problem Size: SmallEPYC 7713EPYC 7713 2P306090120150149.7286.98
MinAvgMaxEPYC 771337.6146.1213.1EPYC 7713 2P81.1283.6438.7OpenBenchmarking.orgWatts, Fewer Is BetterminiFE 2.2CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 2995 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 5558 Joules per run.

LuxCoreRender

LuxCoreRender is an open-source 3D physically based renderer formerly known as LuxRender. LuxCoreRender supports CPU-based rendering as well as GPU acceleration via OpenCL, NVIDIA CUDA, and NVIDIA OptiX interfaces. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgM samples/sec, More Is BetterLuxCoreRender 2.6Scene: Rainbow Colors and Prism - Acceleration: CPUEPYC 7713EPYC 7713 2P510152025SE +/- 0.54, N = 15SE +/- 0.15, N = 1521.1316.99MIN: 17.03 / MAX: 23.53MIN: 15.23 / MAX: 18.28
OpenBenchmarking.orgM samples/sec Per Watt, More Is BetterLuxCoreRender 2.6Scene: Rainbow Colors and Prism - Acceleration: CPUEPYC 7713EPYC 7713 2P0.03060.06120.09180.12240.1530.1360.058
MinAvgMaxEPYC 771337.9155.6230.9EPYC 7713 2P80.7293.7392.4OpenBenchmarking.orgWatts, Fewer Is BetterLuxCoreRender 2.6CPU Power Consumption Monitor1102203304405501. EPYC 7713: Approximate power consumption of 1940 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 4307 Joules per run.

Mlpack Benchmark

Mlpack benchmark scripts for machine learning libraries Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_icaEPYC 7713EPYC 7713 2P1122334455SE +/- 0.05, N = 3SE +/- 0.13, N = 345.2746.40
MinAvgMaxEPYC 771337.6189.9220.0EPYC 7713 2P80.2334.4374.1OpenBenchmarking.orgWatts, Fewer Is BetterMlpack BenchmarkCPU Power Consumption Monitor1002003004005001. EPYC 7713: Approximate power consumption of 9748 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 17724 Joules per run.

ASKAP

ASKAP is a set of benchmarks from the Australian SKA Pathfinder. The principal ASKAP benchmarks are the Hogbom Clean Benchmark (tHogbomClean) and Convolutional Resamping Benchmark (tConvolve) as well as some previous ASKAP benchmarks being included as well for OpenCL and CUDA execution of tConvolve. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMpix/sec, More Is BetterASKAP 1.0Test: tConvolve MPI - GriddingEPYC 7713 2PEPYC 77139K18K27K36K45KSE +/- 263.02, N = 3SE +/- 76.73, N = 343735.821943.01. (CXX) g++ options: -O3 -fstrict-aliasing -fopenmp
OpenBenchmarking.orgMpix/sec Per Watt, More Is BetterASKAP 1.0Test: tConvolve MPI - GriddingEPYC 7713EPYC 7713 2P306090120150114.57109.93
MinAvgMaxEPYC 771337.8191.5223.8EPYC 7713 2P80.9397.8456.8OpenBenchmarking.orgWatts, Fewer Is BetterASKAP 1.0CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 9768 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 20820 Joules per run.

OpenBenchmarking.orgMpix/sec, More Is BetterASKAP 1.0Test: tConvolve MPI - DegriddingEPYC 7713 2PEPYC 77139K18K27K36K45KSE +/- 448.28, N = 3SE +/- 173.09, N = 339742.020252.11. (CXX) g++ options: -O3 -fstrict-aliasing -fopenmp

WebP2 Image Encode

This is a test of Google's libwebp2 library with the WebP2 image encode utility and using a sample 6000x4000 pixel JPEG image as the input, similar to the WebP/libwebp test profile. WebP2 is currently experimental and under heavy development as ultimately the successor to WebP. WebP2 supports 10-bit HDR, more efficienct lossy compression, improved lossless compression, animation support, and full multi-threading support compared to WebP. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMP/s, More Is BetterWebP2 Image Encode 20220823Encode Settings: Quality 75, Compression Effort 7EPYC 7713 2PEPYC 77130.12380.24760.37140.49520.619SE +/- 0.01, N = 3SE +/- 0.00, N = 30.550.481. (CXX) g++ options: -msse4.2 -fno-rtti -O3 -ldl
OpenBenchmarking.orgMP/s Per Watt, More Is BetterWebP2 Image Encode 20220823Encode Settings: Quality 75, Compression Effort 7EPYC 7713EPYC 7713 2P0.00070.00140.00210.00280.00350.0030.002
MinAvgMaxEPYC 771337.0190.9230.3EPYC 7713 2P80.3297.9379.6OpenBenchmarking.orgWatts, Fewer Is BetterWebP2 Image Encode 20220823CPU Power Consumption Monitor1002003004005001. EPYC 7713: Approximate power consumption of 10370 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 14198 Joules per run.

Zstd Compression

This test measures the time needed to compress/decompress a sample file (a FreeBSD disk image - FreeBSD-12.2-RELEASE-amd64-memstick.img) using Zstd compression with options for different compression levels / settings. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMB/s, More Is BetterZstd Compression 1.5.0Compression Level: 19 - Decompression SpeedEPYC 7713 2PEPYC 77137001400210028003500SE +/- 23.51, N = 3SE +/- 14.77, N = 33473.03401.61. (CC) gcc options: -O3 -pthread -lz -llzma
OpenBenchmarking.orgMB/s Per Watt, More Is BetterZstd Compression 1.5.0Compression Level: 19 - Decompression SpeedEPYC 7713EPYC 7713 2P71421283530.7117.54
MinAvgMaxEPYC 771337.7110.8165.3EPYC 7713 2P78.9198.0279.6OpenBenchmarking.orgWatts, Fewer Is BetterZstd Compression 1.5.0CPU Power Consumption Monitor701402102803501. EPYC 7713: Approximate power consumption of 5538 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 10032 Joules per run.

OpenBenchmarking.orgMB/s, More Is BetterZstd Compression 1.5.0Compression Level: 19 - Compression SpeedEPYC 7713EPYC 7713 2P20406080100SE +/- 0.84, N = 3SE +/- 1.20, N = 384.984.71. (CC) gcc options: -O3 -pthread -lz -llzma

OpenBenchmarking.orgMB/s, More Is BetterZstd Compression 1.5.0Compression Level: 19, Long Mode - Decompression SpeedEPYC 7713 2PEPYC 77137001400210028003500SE +/- 21.87, N = 3SE +/- 14.37, N = 33485.83460.31. (CC) gcc options: -O3 -pthread -lz -llzma
OpenBenchmarking.orgMB/s Per Watt, More Is BetterZstd Compression 1.5.0Compression Level: 19, Long Mode - Decompression SpeedEPYC 7713EPYC 7713 2P81624324034.9918.77
MinAvgMaxEPYC 771337.698.9127.1EPYC 7713 2P80.8185.7245.6OpenBenchmarking.orgWatts, Fewer Is BetterZstd Compression 1.5.0CPU Power Consumption Monitor601201802403001. EPYC 7713: Approximate power consumption of 4715 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 9593 Joules per run.

OpenBenchmarking.orgMB/s, More Is BetterZstd Compression 1.5.0Compression Level: 19, Long Mode - Compression SpeedEPYC 7713EPYC 7713 2P1122334455SE +/- 0.21, N = 3SE +/- 0.25, N = 347.439.91. (CC) gcc options: -O3 -pthread -lz -llzma

Timed Linux Kernel Compilation

This test times how long it takes to build the Linux kernel in a default configuration (defconfig) for the architecture being tested or alternatively an allmodconfig for building all possible kernel modules for the build. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed Linux Kernel Compilation 5.18Build: defconfigEPYC 7713 2PEPYC 7713714212835SE +/- 0.20, N = 7SE +/- 0.33, N = 421.5829.41
MinAvgMaxEPYC 771337.7143.3230.4EPYC 7713 2P80.5246.1460.5OpenBenchmarking.orgWatts, Fewer Is BetterTimed Linux Kernel Compilation 5.18CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 5482 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 7209 Joules per run.

7-Zip Compression

This is a test of 7-Zip compression/decompression with its integrated benchmark feature. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMIPS, More Is Better7-Zip Compression 22.01Test: Decompression RatingEPYC 7713 2PEPYC 7713140K280K420K560K700KSE +/- 2913.98, N = 3SE +/- 180.78, N = 36340863546081. (CXX) g++ options: -lpthread -ldl -O2 -fPIC
OpenBenchmarking.orgMIPS Per Watt, More Is Better7-Zip Compression 22.01Test: Decompression RatingEPYC 7713EPYC 7713 2P4008001200160020001806.441638.03
MinAvgMaxEPYC 771337.4196.3229.2EPYC 7713 2P81.2387.1460.8OpenBenchmarking.orgWatts, Fewer Is Better7-Zip Compression 22.01CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 8572 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 20258 Joules per run.

OpenBenchmarking.orgMIPS, More Is Better7-Zip Compression 22.01Test: Compression RatingEPYC 7713 2PEPYC 7713110K220K330K440K550KSE +/- 4935.82, N = 3SE +/- 361.70, N = 35166173510771. (CXX) g++ options: -lpthread -ldl -O2 -fPIC

Kripke

Kripke is a simple, scalable, 3D Sn deterministic particle transport code. Its primary purpose is to research how data layout, programming paradigms and architectures effect the implementation and performance of Sn transport. Kripke is developed by LLNL. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgThroughput FoM, More Is BetterKripke 1.2.4EPYC 7713EPYC 7713 2P60M120M180M240M300MSE +/- 2567603.42, N = 15SE +/- 886909.06, N = 32707225271439502671. (CXX) g++ options: -O3 -fopenmp
OpenBenchmarking.orgThroughput FoM Per Watt, More Is BetterKripke 1.2.4EPYC 7713EPYC 7713 2P400K800K1200K1600K2000K1791600.71464047.76
MinAvgMaxEPYC 771337.9151.1213.0EPYC 7713 2P80.6310.2398.8OpenBenchmarking.orgWatts, Fewer Is BetterKripke 1.2.4CPU Power Consumption Monitor1102203304405501. EPYC 7713: Approximate power consumption of 2539 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 8686 Joules per run.

JPEG XL Decoding libjxl

The JPEG XL Image Coding System is designed to provide next-generation JPEG image capabilities with JPEG XL offering better image quality and compression over legacy JPEG. This test profile is suited for JPEG XL decode performance testing to PNG output file, the pts/jpexl test is for encode performance. The JPEG XL encoding/decoding is done using the libjxl codebase. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMP/s, More Is BetterJPEG XL Decoding libjxl 0.6.1CPU Threads: 1EPYC 7713EPYC 7713 2P1530456075SE +/- 0.16, N = 3SE +/- 0.11, N = 367.6266.93
OpenBenchmarking.orgMP/s Per Watt, More Is BetterJPEG XL Decoding libjxl 0.6.1CPU Threads: 1EPYC 7713EPYC 7713 2P0.20590.41180.61770.82361.02950.9150.457
MinAvgMaxEPYC 771337.173.978.2EPYC 7713 2P80.7146.6160.2OpenBenchmarking.orgWatts, Fewer Is BetterJPEG XL Decoding libjxl 0.6.1CPU Power Consumption Monitor501001502002501. EPYC 7713: Approximate power consumption of 3522 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 6937 Joules per run.

WebP Image Encode

This is a test of Google's libwebp with the cwebp image encode utility and using a sample 6000x4000 pixel JPEG image as the input. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMP/s, More Is BetterWebP Image Encode 1.2.4Encode Settings: Quality 100, Lossless, Highest CompressionEPYC 7713EPYC 7713 2P0.1260.2520.3780.5040.63SE +/- 0.00, N = 3SE +/- 0.00, N = 30.560.561. (CC) gcc options: -fvisibility=hidden -O2 -lm
OpenBenchmarking.orgMP/s Per Watt, More Is BetterWebP Image Encode 1.2.4Encode Settings: Quality 100, Lossless, Highest CompressionEPYC 7713EPYC 7713 2P0.00160.00320.00480.00640.0080.0070.004
MinAvgMaxEPYC 771337.276.485.2EPYC 7713 2P80.2148.4158.5OpenBenchmarking.orgWatts, Fewer Is BetterWebP Image Encode 1.2.4CPU Power Consumption Monitor501001502002501. EPYC 7713: Approximate power consumption of 3591 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 6976 Joules per run.

Timed Godot Game Engine Compilation

This test times how long it takes to compile the Godot Game Engine. Godot is a popular, open-source, cross-platform 2D/3D game engine and is built using the SCons build system and targeting the X11 platform. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed Godot Game Engine Compilation 3.2.3Time To CompileEPYC 7713 2PEPYC 77131020304050SE +/- 0.08, N = 3SE +/- 0.08, N = 341.1743.35
MinAvgMaxEPYC 771337.5151.2228.9EPYC 7713 2P80.2253.8456.4OpenBenchmarking.orgWatts, Fewer Is BetterTimed Godot Game Engine Compilation 3.2.3CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 7309 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 11760 Joules per run.

Primesieve

Primesieve generates prime numbers using a highly optimized sieve of Eratosthenes implementation. Primesieve primarily benchmarks the CPU's L1/L2 cache performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPrimesieve 8.0Length: 1e13EPYC 7713 2PEPYC 77131224364860SE +/- 0.07, N = 3SE +/- 0.05, N = 328.7255.261. (CXX) g++ options: -O3
MinAvgMaxEPYC 771338.2216.1231.0EPYC 7713 2P81.7407.9460.7OpenBenchmarking.orgWatts, Fewer Is BetterPrimesieve 8.0CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 12749 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 13190 Joules per run.

srsRAN

srsRAN is an open-source LTE/5G software radio suite created by Software Radio Systems (SRS). The srsRAN radio suite was formerly known as srsLTE and can be used for building your own software-defined radio (SDR) 4G/5G mobile network. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgUE Mb/s, More Is BettersrsRAN 22.04.1Test: 4G PHY_DL_Test 100 PRB MIMO 256-QAMEPYC 7713EPYC 7713 2P306090120150SE +/- 0.24, N = 3SE +/- 0.26, N = 3141.4141.01. (CXX) g++ options: -std=c++14 -fno-strict-aliasing -march=native -mfpmath=sse -mavx2 -fvisibility=hidden -O3 -fno-trapping-math -fno-math-errno -ldl -lpthread -lm
OpenBenchmarking.orgUE Mb/s Per Watt, More Is BettersrsRAN 22.04.1Test: 4G PHY_DL_Test 100 PRB MIMO 256-QAMEPYC 7713EPYC 7713 2P0.43740.87481.31221.74962.1871.9440.970
MinAvgMaxEPYC 771337.472.776.8EPYC 7713 2P80.5145.3155.3OpenBenchmarking.orgWatts, Fewer Is BettersrsRAN 22.04.1CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 3298 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 6638 Joules per run.

OpenBenchmarking.orgeNb Mb/s, More Is BettersrsRAN 22.04.1Test: 4G PHY_DL_Test 100 PRB MIMO 256-QAMEPYC 7713EPYC 7713 2P90180270360450SE +/- 0.18, N = 3SE +/- 0.64, N = 3431.8426.11. (CXX) g++ options: -std=c++14 -fno-strict-aliasing -march=native -mfpmath=sse -mavx2 -fvisibility=hidden -O3 -fno-trapping-math -fno-math-errno -ldl -lpthread -lm

libavif avifenc

This is a test of the AOMedia libavif library testing the encoding of a JPEG image to AV1 Image Format (AVIF). Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is Betterlibavif avifenc 0.10Encoder Speed: 2EPYC 7713EPYC 7713 2P918273645SE +/- 0.21, N = 3SE +/- 0.21, N = 340.7741.211. (CXX) g++ options: -O3 -fPIC -lm
MinAvgMaxEPYC 771337.1115.8230.4EPYC 7713 2P80.1194.2358.5OpenBenchmarking.orgWatts, Fewer Is Betterlibavif avifenc 0.10CPU Power Consumption Monitor1002003004005001. EPYC 7713: Approximate power consumption of 5213 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 8740 Joules per run.

PyPerformance

PyPerformance is the reference Python performance benchmark suite. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: chaosEPYC 7713 2PEPYC 771320406080100SE +/- 0.15, N = 3SE +/- 0.13, N = 395.896.5
MinAvgMaxEPYC 771337.471.876.4EPYC 7713 2P80.2145.3159.2OpenBenchmarking.orgWatts, Fewer Is BetterPyPerformance 1.0.0CPU Power Consumption Monitor501001502002501. EPYC 7713: Approximate power consumption of 2873 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 6925 Joules per run.

GROMACS

The GROMACS (GROningen MAchine for Chemical Simulations) molecular dynamics package testing with the water_GMX50 data. This test profile allows selecting between CPU and GPU-based GROMACS builds. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgNs Per Day, More Is BetterGROMACS 2022.1Implementation: MPI CPU - Input: water_GMX50_bareEPYC 7713 2PEPYC 7713246810SE +/- 0.024, N = 3SE +/- 0.010, N = 38.2155.1301. (CXX) g++ options: -O3
OpenBenchmarking.orgNs Per Day Per Watt, More Is BetterGROMACS 2022.1Implementation: MPI CPU - Input: water_GMX50_bareEPYC 7713EPYC 7713 2P0.00560.01120.01680.02240.0280.0250.020
MinAvgMaxEPYC 771337.8206.4230.3EPYC 7713 2P81.1402.3461.3OpenBenchmarking.orgWatts, Fewer Is BetterGROMACS 2022.1CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 9906 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 15152 Joules per run.

Timed PHP Compilation

This test times how long it takes to build PHP. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed PHP Compilation 8.1.9Time To CompileEPYC 7713 2PEPYC 7713918273645SE +/- 0.46, N = 3SE +/- 0.41, N = 338.3439.76
MinAvgMaxEPYC 771337.6105.0229.7EPYC 7713 2P80.7191.0458.5OpenBenchmarking.orgWatts, Fewer Is BetterTimed PHP Compilation 8.1.9CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 5042 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 8912 Joules per run.

Redis

Redis is an open-source in-memory data structure store, used as a database, cache, and message broker. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgRequests Per Second, More Is BetterRedis 7.0.4Test: GET - Parallel Connections: 1000EPYC 7713EPYC 7713 2P400K800K1200K1600K2000KSE +/- 8028.55, N = 3SE +/- 7896.51, N = 31990067.331364479.131. (CXX) g++ options: -MM -MT -g3 -fvisibility=hidden -O3
OpenBenchmarking.orgRequests Per Second Per Watt, More Is BetterRedis 7.0.4Test: GET - Parallel Connections: 1000EPYC 7713EPYC 7713 2P3K6K9K12K15K12590.694701.77
MinAvgMaxEPYC 771337.2158.1228.0EPYC 7713 2P81.0290.2443.4OpenBenchmarking.orgWatts, Fewer Is BetterRedis 7.0.4CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 5637 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 13833 Joules per run.

Algebraic Multi-Grid Benchmark

AMG is a parallel algebraic multigrid solver for linear systems arising from problems on unstructured grids. The driver provided with AMG builds linear systems for various 3-dimensional problems. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFigure Of Merit, More Is BetterAlgebraic Multi-Grid Benchmark 1.2EPYC 7713 2PEPYC 7713400M800M1200M1600M2000MSE +/- 699794.57, N = 3SE +/- 863687.12, N = 3192330666710120516671. (CC) gcc options: -lparcsr_ls -lparcsr_mv -lseq_mv -lIJ_mv -lkrylov -lHYPRE_utilities -lm -fopenmp -lmpi
OpenBenchmarking.orgFigure Of Merit Per Watt, More Is BetterAlgebraic Multi-Grid Benchmark 1.2EPYC 7713EPYC 7713 2P1.1M2.2M3.3M4.4M5.5M5339041.554855985.26
MinAvgMaxEPYC 771338.2189.6220.2EPYC 7713 2P80.3396.1449.8OpenBenchmarking.orgWatts, Fewer Is BetterAlgebraic Multi-Grid Benchmark 1.2CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 7519 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 16899 Joules per run.

Appleseed

Appleseed is an open-source production renderer focused on physically-based global illumination rendering engine primarily designed for animation and visual effects. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterAppleseed 2.0 BetaScene: Disney MaterialEPYC 7713 2PEPYC 7713142842567050.4661.27
MinAvgMaxEPYC 771337.7198.4209.6EPYC 7713 2P80.3351.6395.6OpenBenchmarking.orgWatts, Fewer Is BetterAppleseed 2.0 BetaCPU Power Consumption Monitor110220330440550

Kvazaar

This is a test of Kvazaar as a CPU-based H.265/HEVC video encoder written in the C programming language and optimized in Assembly. Kvazaar is the winner of the 2016 ACM Open-Source Software Competition and developed at the Ultra Video Group, Tampere University, Finland. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterKvazaar 2.1Video Input: Bosphorus 4K - Video Preset: Ultra FastEPYC 7713 2PEPYC 77131326395265SE +/- 0.59, N = 6SE +/- 0.82, N = 1558.5256.531. (CC) gcc options: -pthread -ftree-vectorize -fvisibility=hidden -O2 -lpthread -lm -lrt
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterKvazaar 2.1Video Input: Bosphorus 4K - Video Preset: Ultra FastEPYC 7713EPYC 7713 2P0.08570.17140.25710.34280.42850.3810.229
MinAvgMaxEPYC 771337.7148.5207.0EPYC 7713 2P80.6256.0358.6OpenBenchmarking.orgWatts, Fewer Is BetterKvazaar 2.1CPU Power Consumption Monitor1002003004005001. EPYC 7713: Approximate power consumption of 2177 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 3670 Joules per run.

Rodinia

Rodinia is a suite focused upon accelerating compute-intensive applications with accelerators. CUDA, OpenMP, and OpenCL parallel models are supported by the included applications. This profile utilizes select OpenCL, NVIDIA CUDA and OpenMP test binaries at the moment. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterRodinia 3.1Test: OpenMP LavaMDEPYC 7713 2PEPYC 77131122334455SE +/- 0.14, N = 3SE +/- 0.08, N = 326.7446.551. (CXX) g++ options: -O2 -lOpenCL
MinAvgMaxEPYC 771337.5200.8230.3EPYC 7713 2P81.3362.6462.2OpenBenchmarking.orgWatts, Fewer Is BetterRodinia 3.1CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 10173 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 11121 Joules per run.

srsRAN

srsRAN is an open-source LTE/5G software radio suite created by Software Radio Systems (SRS). The srsRAN radio suite was formerly known as srsLTE and can be used for building your own software-defined radio (SDR) 4G/5G mobile network. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSamples / Second, More Is BettersrsRAN 22.04.1Test: OFDM_TestEPYC 7713EPYC 7713 2P30M60M90M120M150MSE +/- 1516941.37, N = 3SE +/- 1304266.50, N = 31366333331303333331. (CXX) g++ options: -std=c++14 -fno-strict-aliasing -march=native -mfpmath=sse -mavx2 -fvisibility=hidden -O3 -fno-trapping-math -fno-math-errno -ldl -lpthread -lm
OpenBenchmarking.orgSamples / Second Per Watt, More Is BettersrsRAN 22.04.1Test: OFDM_TestEPYC 7713EPYC 7713 2P400K800K1200K1600K2000K1822001.36887112.35
MinAvgMaxEPYC 771337.175.080.4EPYC 7713 2P80.7146.9158.5OpenBenchmarking.orgWatts, Fewer Is BettersrsRAN 22.04.1CPU Power Consumption Monitor501001502002501. EPYC 7713: Approximate power consumption of 2825 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 5730 Joules per run.

OpenBenchmarking.orgUE Mb/s, More Is BettersrsRAN 22.04.1Test: 4G PHY_DL_Test 100 PRB MIMO 64-QAMEPYC 7713EPYC 7713 2P306090120150SE +/- 0.30, N = 3SE +/- 0.46, N = 3133.7133.61. (CXX) g++ options: -std=c++14 -fno-strict-aliasing -march=native -mfpmath=sse -mavx2 -fvisibility=hidden -O3 -fno-trapping-math -fno-math-errno -ldl -lpthread -lm
OpenBenchmarking.orgUE Mb/s Per Watt, More Is BettersrsRAN 22.04.1Test: 4G PHY_DL_Test 100 PRB MIMO 64-QAMEPYC 7713EPYC 7713 2P0.41490.82981.24471.65962.07451.8440.926
MinAvgMaxEPYC 771337.372.576.8EPYC 7713 2P80.5144.3155.4OpenBenchmarking.orgWatts, Fewer Is BettersrsRAN 22.04.1CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 2756 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 5532 Joules per run.

OpenBenchmarking.orgeNb Mb/s, More Is BettersrsRAN 22.04.1Test: 4G PHY_DL_Test 100 PRB MIMO 64-QAMEPYC 7713EPYC 7713 2P90180270360450SE +/- 2.00, N = 3SE +/- 0.24, N = 3394.4394.11. (CXX) g++ options: -std=c++14 -fno-strict-aliasing -march=native -mfpmath=sse -mavx2 -fvisibility=hidden -O3 -fno-trapping-math -fno-math-errno -ldl -lpthread -lm

Embree

Intel Embree is a collection of high-performance ray-tracing kernels for execution on CPUs and supporting instruction sets such as SSE, AVX, AVX2, and AVX-512. Embree also supports making use of the Intel SPMD Program Compiler (ISPC). Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterEmbree 3.13Binary: Pathtracer - Model: Asian DragonEPYC 7713 2PEPYC 77131530456075SE +/- 0.62, N = 15SE +/- 0.07, N = 568.8862.98MIN: 62.6 / MAX: 74.57MIN: 61.69 / MAX: 65.06
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterEmbree 3.13Binary: Pathtracer - Model: Asian DragonEPYC 7713EPYC 7713 2P0.08370.16740.25110.33480.41850.3720.205
MinAvgMaxEPYC 771337.5169.1229.6EPYC 7713 2P80.4335.2459.6OpenBenchmarking.orgWatts, Fewer Is BetterEmbree 3.13CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 2435 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 4648 Joules per run.

OpenBenchmarking.orgFrames Per Second, More Is BetterEmbree 3.13Binary: Pathtracer ISPC - Model: Asian DragonEPYC 7713 2PEPYC 77131530456075SE +/- 0.75, N = 15SE +/- 0.17, N = 467.1756.81MIN: 59.54 / MAX: 73.59MIN: 55.32 / MAX: 58.61
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterEmbree 3.13Binary: Pathtracer ISPC - Model: Asian DragonEPYC 7713EPYC 7713 2P0.07340.14680.22020.29360.3670.3260.198
MinAvgMaxEPYC 771337.5174.3230.1EPYC 7713 2P81.7338.5459.5OpenBenchmarking.orgWatts, Fewer Is BetterEmbree 3.13CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 2701 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 4785 Joules per run.

oneDNN

This is a test of the Intel oneDNN as an Intel-optimized library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Intel oneDNN was formerly known as DNNL (Deep Neural Network Library) and MKL-DNN before being rebranded as part of Intel oneAPI. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 2.6Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPUEPYC 7713EPYC 7713 2P714212835SE +/- 0.24, N = 12SE +/- 0.34, N = 412.5630.01MIN: 9.16MIN: 23.661. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -std=c++11 -pie -ldl -lpthread
MinAvgMaxEPYC 771337.6112.3150.9EPYC 7713 2P80.7253.4318.5OpenBenchmarking.orgWatts, Fewer Is BetteroneDNN 2.6CPU Power Consumption Monitor801602403204001. EPYC 7713: Approximate power consumption of 1815 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 4118 Joules per run.

PyPerformance

PyPerformance is the reference Python performance benchmark suite. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: regex_compileEPYC 7713 2PEPYC 7713306090120150SE +/- 0.33, N = 3SE +/- 0.33, N = 3155156
MinAvgMaxEPYC 771337.271.375.9EPYC 7713 2P80.7143.0155.5OpenBenchmarking.orgWatts, Fewer Is BetterPyPerformance 1.0.0CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 2568 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 5197 Joules per run.

PJSIP

PJSIP is a free and open source multimedia communication library written in C language implementing standard based protocols such as SIP, SDP, RTP, STUN, TURN, and ICE. It combines signaling protocol (SIP) with rich multimedia framework and NAT traversal functionality into high level API that is portable and suitable for almost any type of systems ranging from desktops, embedded systems, to mobile handsets. This test profile is making use of pjsip-perf with both the client/server on teh system. More details on the PJSIP benchmark at https://www.pjsip.org/high-performance-sip.htm Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgResponses Per Second, More Is BetterPJSIP 2.11Method: OPTIONS, StatelessEPYC 7713 2PEPYC 771314K28K42K56K70KSE +/- 232.96, N = 3SE +/- 187.70, N = 367617666021. (CC) gcc options: -lavformat -lavcodec -lswscale -lavutil -lstdc++ -lopus -lssl -lcrypto -luuid -lm -lrt -lpthread -lasound -O2
OpenBenchmarking.orgResponses Per Second Per Watt, More Is BetterPJSIP 2.11Method: OPTIONS, StatelessEPYC 7713EPYC 7713 2P160320480640800754.96422.51
MinAvgMaxEPYC 771337.388.299.4EPYC 7713 2P80.7160.0181.8OpenBenchmarking.orgWatts, Fewer Is BetterPJSIP 2.11CPU Power Consumption Monitor501001502002501. EPYC 7713: Approximate power consumption of 3205 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 5761 Joules per run.

LibRaw

LibRaw is a RAW image decoder for digital camera photos. This test profile runs LibRaw's post-processing benchmark. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMpix/sec, More Is BetterLibRaw 0.20Post-Processing BenchmarkEPYC 7713EPYC 7713 2P918273645SE +/- 0.18, N = 3SE +/- 0.12, N = 339.4037.961. (CXX) g++ options: -O2 -fopenmp -ljpeg -lz -lm
OpenBenchmarking.orgMpix/sec Per Watt, More Is BetterLibRaw 0.20Post-Processing BenchmarkEPYC 7713EPYC 7713 2P0.0950.190.2850.380.4750.4220.189
MinAvgMaxEPYC 771337.593.3109.3EPYC 7713 2P80.6200.9225.0OpenBenchmarking.orgWatts, Fewer Is BetterLibRaw 0.20CPU Power Consumption Monitor601201802403001. EPYC 7713: Approximate power consumption of 3265 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 7367 Joules per run.

Blender

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.3Blend File: Fishy Cat - Compute: CPU-OnlyEPYC 7713 2PEPYC 7713918273645SE +/- 0.08, N = 3SE +/- 0.03, N = 322.2538.85
MinAvgMaxEPYC 771338.2204.3230.3EPYC 7713 2P81.0376.8460.6OpenBenchmarking.orgWatts, Fewer Is BetterBlender 3.3CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 8920 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 10298 Joules per run.

NAS Parallel Benchmarks

NPB, NAS Parallel Benchmarks, is a benchmark developed by NASA for high-end computer systems. This test profile currently uses the MPI version of NPB. This test profile offers selecting the different NPB tests/problems and varying problem sizes. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: IS.DEPYC 7713 2PEPYC 771310002000300040005000SE +/- 43.17, N = 15SE +/- 13.59, N = 44690.062648.961. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.2
OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: IS.DEPYC 7713EPYC 7713 2P4812162016.1715.94
MinAvgMaxEPYC 771337.7163.8222.4EPYC 7713 2P80.6294.3451.7OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 3113 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 3689 Joules per run.

Renaissance

Renaissance is a suite of benchmarks designed to test the Java JVM from Apache Spark to a Twitter-like service to Scala and other features. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterRenaissance 0.14Test: Apache Spark BayesEPYC 7713EPYC 7713 2P140280420560700SE +/- 2.85, N = 3SE +/- 4.99, N = 3585.6638.1MIN: 368.12 / MAX: 849.82MIN: 368.33 / MAX: 1098.6
MinAvgMaxEPYC 771337.4139.9213.7EPYC 7713 2P80.2262.5385.5OpenBenchmarking.orgWatts, Fewer Is BetterRenaissance 0.14CPU Power Consumption Monitor1002003004005001. EPYC 7713: Approximate power consumption of 4476 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 10064 Joules per run.

Aircrack-ng

Aircrack-ng is a tool for assessing WiFi/WLAN network security. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgk/s, More Is BetterAircrack-ng 1.7EPYC 7713 2PEPYC 771330K60K90K120K150KSE +/- 293.66, N = 3SE +/- 854.57, N = 3149778.80149666.941. (CXX) g++ options: -std=gnu++17 -O3 -fvisibility=hidden -fcommon -rdynamic -lnl-3 -lnl-genl-3 -lpcre -lsqlite3 -lpthread -lz -lssl -lcrypto -lhwloc -ldl -lm -pthread
OpenBenchmarking.orgk/s Per Watt, More Is BetterAircrack-ng 1.7EPYC 7713EPYC 7713 2P160320480640800736.47377.12
MinAvgMaxEPYC 771338.2203.2230.1EPYC 7713 2P81.1397.2445.2OpenBenchmarking.orgWatts, Fewer Is BetterAircrack-ng 1.7CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 6977 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 13636 Joules per run.

FLAC Audio Encoding

OpenBenchmarking.orgSeconds, Fewer Is BetterFLAC Audio Encoding 1.4WAV To FLACEPYC 7713EPYC 7713 2P48121620SE +/- 0.01, N = 5SE +/- 0.01, N = 518.1318.131. (CXX) g++ options: -O3 -fvisibility=hidden -logg -lm
MinAvgMaxEPYC 771337.569.779.2EPYC 7713 2P80.8141.2157.0OpenBenchmarking.orgWatts, Fewer Is BetterFLAC Audio Encoding 1.4CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 1548 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 3135 Joules per run.

x265

This is a simple test of the x265 encoder run on the CPU with 1080p and 4K options for H.265 video encode performance with x265. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is Betterx265 3.4Video Input: Bosphorus 4KEPYC 7713EPYC 7713 2P612182430SE +/- 0.03, N = 3SE +/- 0.26, N = 426.6921.481. (CXX) g++ options: -O3 -rdynamic -lpthread -lrt -ldl -lnuma
OpenBenchmarking.orgFrames Per Second Per Watt, More Is Betterx265 3.4Video Input: Bosphorus 4KEPYC 7713EPYC 7713 2P0.04210.08420.12630.16840.21050.1870.089
MinAvgMaxEPYC 771337.5142.4181.5EPYC 7713 2P80.0241.3294.3OpenBenchmarking.orgWatts, Fewer Is Betterx265 3.4CPU Power Consumption Monitor701402102803501. EPYC 7713: Approximate power consumption of 3796 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 7782 Joules per run.

Google SynthMark

SynthMark is a cross platform tool for benchmarking CPU performance under a variety of real-time audio workloads. It uses a polyphonic synthesizer model to provide standardized tests for latency, jitter and computational throughput. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgVoices, More Is BetterGoogle SynthMark 20201109Test: VoiceMark_100EPYC 7713 2PEPYC 7713160320480640800SE +/- 0.12, N = 3SE +/- 1.04, N = 3748.06747.311. (CXX) g++ options: -lm -lpthread -std=c++11 -Ofast
OpenBenchmarking.orgVoices Per Watt, More Is BetterGoogle SynthMark 20201109Test: VoiceMark_100EPYC 7713EPYC 7713 2P369121510.8175.359
MinAvgMaxEPYC 771337.669.174.2EPYC 7713 2P79.9139.6148.3OpenBenchmarking.orgWatts, Fewer Is BetterGoogle SynthMark 20201109CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 2349 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 4792 Joules per run.

QuantLib

QuantLib is an open-source library/framework around quantitative finance for modeling, trading and risk management scenarios. QuantLib is written in C++ with Boost and its built-in benchmark used reports the QuantLib Benchmark Index benchmark score. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMFLOPS, More Is BetterQuantLib 1.21EPYC 7713 2PEPYC 77136001200180024003000SE +/- 1.70, N = 3SE +/- 28.31, N = 32808.82795.31. (CXX) g++ options: -O3 -march=native -rdynamic
OpenBenchmarking.orgMFLOPS Per Watt, More Is BetterQuantLib 1.21EPYC 7713EPYC 7713 2P91827364539.0719.75
MinAvgMaxEPYC 771337.271.676.8EPYC 7713 2P79.7142.3154.2OpenBenchmarking.orgWatts, Fewer Is BetterQuantLib 1.21CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 2433 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 4742 Joules per run.

Xmrig

Xmrig is an open-source cross-platform CPU/GPU miner for RandomX, KawPow, CryptoNight and AstroBWT. This test profile is setup to measure the Xmlrig CPU mining performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgH/s, More Is BetterXmrig 6.12.1Variant: Monero - Hash Count: 1MEPYC 7713 2PEPYC 771311K22K33K44K55KSE +/- 161.81, N = 3SE +/- 67.12, N = 350749.628612.41. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc
OpenBenchmarking.orgH/s Per Watt, More Is BetterXmrig 6.12.1Variant: Monero - Hash Count: 1MEPYC 7713EPYC 7713 2P306090120150140.22135.37
MinAvgMaxEPYC 771338.1204.1226.2EPYC 7713 2P80.4374.9459.8OpenBenchmarking.orgWatts, Fewer Is BetterXmrig 6.12.1CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 8162 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 9622 Joules per run.

NAMD

NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD was developed by the Theoretical and Computational Biophysics Group in the Beckman Institute for Advanced Science and Technology at the University of Illinois at Urbana-Champaign. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgdays/ns, Fewer Is BetterNAMD 2.14ATPase Simulation - 327,506 AtomsEPYC 7713 2PEPYC 77130.10230.20460.30690.40920.5115SE +/- 0.00056, N = 3SE +/- 0.00038, N = 30.267120.45457
MinAvgMaxEPYC 771337.9200.4230.3EPYC 7713 2P80.5408.2461.0OpenBenchmarking.orgWatts, Fewer Is BetterNAMD 2.14CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 6212 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 14150 Joules per run.

Pennant

Pennant is an application focused on hydrodynamics on general unstructured meshes in 2D. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgHydro Cycle Time - Seconds, Fewer Is BetterPennant 1.0.1Test: leblancbigEPYC 7713 2PEPYC 7713246810SE +/- 0.079319, N = 15SE +/- 0.094028, N = 153.5565706.1330601. (CXX) g++ options: -fopenmp -lmpi_cxx -lmpi
MinAvgMaxEPYC 771337.5149.9230.3EPYC 7713 2P80.8257.4460.6OpenBenchmarking.orgWatts, Fewer Is BetterPennant 1.0.1CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 1599 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 2179 Joules per run.

PHPBench

PHPBench is a benchmark suite for PHP. It performs a large number of simple tests in order to bench various aspects of the PHP interpreter. PHPBench can be used to compare hardware, operating systems, PHP versions, PHP accelerators and caches, compiler options, etc. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgScore, More Is BetterPHPBench 0.8.1PHP Benchmark SuiteEPYC 7713EPYC 7713 2P160K320K480K640K800KSE +/- 5040.99, N = 3SE +/- 2382.18, N = 3733332726003
OpenBenchmarking.orgScore Per Watt, More Is BetterPHPBench 0.8.1PHP Benchmark SuiteEPYC 7713EPYC 7713 2P2K4K6K8K10K10433.995162.81
MinAvgMaxEPYC 771337.370.376.1EPYC 7713 2P79.7140.6150.2OpenBenchmarking.orgWatts, Fewer Is BetterPHPBench 0.8.1CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 2202 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 4453 Joules per run.

JPEG XL libjxl

The JPEG XL Image Coding System is designed to provide next-generation JPEG image capabilities with JPEG XL offering better image quality and compression over legacy JPEG. This test profile is currently focused on the multi-threaded JPEG XL image encode performance using the reference libjxl library. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMP/s, More Is BetterJPEG XL libjxl 0.6.1Input: JPEG - Encode Speed: 7EPYC 7713EPYC 7713 2P20406080100SE +/- 0.16, N = 5SE +/- 0.79, N = 10104.58100.771. (CXX) g++ options: -funwind-tables -O3 -O2 -fPIE -pie
OpenBenchmarking.orgMP/s Per Watt, More Is BetterJPEG XL libjxl 0.6.1Input: JPEG - Encode Speed: 7EPYC 7713EPYC 7713 2P0.28780.57560.86341.15121.4391.2790.609
MinAvgMaxEPYC 771337.381.8105.1EPYC 7713 2P80.4165.6219.5OpenBenchmarking.orgWatts, Fewer Is BetterJPEG XL libjxl 0.6.1CPU Power Consumption Monitor601201802403001. EPYC 7713: Approximate power consumption of 1210 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 2484 Joules per run.

Xmrig

Xmrig is an open-source cross-platform CPU/GPU miner for RandomX, KawPow, CryptoNight and AstroBWT. This test profile is setup to measure the Xmlrig CPU mining performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgH/s, More Is BetterXmrig 6.12.1Variant: Wownero - Hash Count: 1MEPYC 7713 2PEPYC 77139K18K27K36K45KSE +/- 68.12, N = 3SE +/- 49.92, N = 341982.936467.21. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc
OpenBenchmarking.orgH/s Per Watt, More Is BetterXmrig 6.12.1Variant: Wownero - Hash Count: 1MEPYC 7713EPYC 7713 2P4080120160200182.74108.02
MinAvgMaxEPYC 771338.0199.6229.3EPYC 7713 2P81.4388.7457.6OpenBenchmarking.orgWatts, Fewer Is BetterXmrig 6.12.1CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 6519 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 11660 Joules per run.

Mlpack Benchmark

Mlpack benchmark scripts for machine learning libraries Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_svmEPYC 7713EPYC 7713 2P510152025SE +/- 0.02, N = 3SE +/- 0.01, N = 321.3321.45
MinAvgMaxEPYC 771337.671.798.0EPYC 7713 2P77.2142.1178.2OpenBenchmarking.orgWatts, Fewer Is BetterMlpack BenchmarkCPU Power Consumption Monitor501001502002501. EPYC 7713: Approximate power consumption of 2032 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 4074 Joules per run.

DaCapo Benchmark

This test runs the DaCapo Benchmarks written in Java and intended to test system/CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgmsec, Fewer Is BetterDaCapo Benchmark 9.12-MR1Java Test: TradebeansEPYC 7713EPYC 7713 2P10002000300040005000SE +/- 31.86, N = 5SE +/- 35.66, N = 939984568
MinAvgMaxEPYC 771337.380.4137.2EPYC 7713 2P79.8156.1234.0OpenBenchmarking.orgWatts, Fewer Is BetterDaCapo Benchmark 9.12-MR1CPU Power Consumption Monitor601201802403001. EPYC 7713: Approximate power consumption of 1125 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 2289 Joules per run.

Blender

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.3Blend File: BMW27 - Compute: CPU-OnlyEPYC 7713 2PEPYC 7713714212835SE +/- 0.23, N = 3SE +/- 0.08, N = 317.1930.43
MinAvgMaxEPYC 771337.4202.1230.3EPYC 7713 2P81.0369.5460.6OpenBenchmarking.orgWatts, Fewer Is BetterBlender 3.3CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 7073 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 8128 Joules per run.

LULESH

LULESH is the Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgz/s, More Is BetterLULESH 2.0.3EPYC 7713 2PEPYC 77138K16K24K32K40KSE +/- 104.62, N = 3SE +/- 64.60, N = 336456.2319305.661. (CXX) g++ options: -O3 -fopenmp -lm -lmpi_cxx -lmpi
OpenBenchmarking.orgz/s Per Watt, More Is BetterLULESH 2.0.3EPYC 7713EPYC 7713 2P306090120150113.62102.40
MinAvgMaxEPYC 771337.6169.9219.1EPYC 7713 2P81.0356.0452.9OpenBenchmarking.orgWatts, Fewer Is BetterLULESH 2.0.3CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 4701 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 10325 Joules per run.

PyPerformance

PyPerformance is the reference Python performance benchmark suite. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: pickle_pure_pythonEPYC 7713 2PEPYC 771380160240320400SE +/- 1.00, N = 3SE +/- 1.45, N = 3385387
MinAvgMaxEPYC 771337.270.376.1EPYC 7713 2P80.0142.9154.4OpenBenchmarking.orgWatts, Fewer Is BetterPyPerformance 1.0.0CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 1968 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 4049 Joules per run.

TNN

TNN is an open-source deep learning reasoning framework developed by Tencent. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterTNN 0.3Target: CPU - Model: MobileNet v2EPYC 7713EPYC 7713 2P70140210280350SE +/- 0.13, N = 3SE +/- 0.48, N = 3332.61341.28MIN: 331.37 / MAX: 346.44MIN: 339.15 / MAX: 401.21. (CXX) g++ options: -fopenmp -pthread -fvisibility=hidden -fvisibility=default -O3 -rdynamic -ldl
MinAvgMaxEPYC 771337.383.092.5EPYC 7713 2P81.6174.3194.2OpenBenchmarking.orgWatts, Fewer Is BetterTNN 0.3CPU Power Consumption Monitor501001502002501. EPYC 7713: Approximate power consumption of 2268 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 4881 Joules per run.

Cython Benchmark

Cython provides a superset of Python that is geared to deliver C-like levels of performance. This test profile makes use of Cython's bundled benchmark tests and runs an N-Queens sample test as a simple benchmark to the system's Cython performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterCython Benchmark 0.29.21Test: N-QueensEPYC 7713 2PEPYC 7713612182430SE +/- 0.04, N = 3SE +/- 0.08, N = 323.3123.38
MinAvgMaxEPYC 771337.570.777.4EPYC 7713 2P80.0140.2154.0OpenBenchmarking.orgWatts, Fewer Is BetterCython Benchmark 0.29.21CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 1957 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 3833 Joules per run.

NAS Parallel Benchmarks

NPB, NAS Parallel Benchmarks, is a benchmark developed by NASA for high-end computer systems. This test profile currently uses the MPI version of NPB. This test profile offers selecting the different NPB tests/problems and varying problem sizes. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: SP.CEPYC 7713 2PEPYC 771320K40K60K80K100KSE +/- 212.54, N = 4SE +/- 33.24, N = 3116527.6351921.711. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.2
OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: SP.CEPYC 7713 2PEPYC 771370140210280350331.99271.37
MinAvgMaxEPYC 771337.6191.3226.8EPYC 7713 2P80.8351.0458.4OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 6250 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 6142 Joules per run.

PyBench

This test profile reports the total time of the different average timed test results from PyBench. PyBench reports average test times for different functions such as BuiltinFunctionCalls and NestedForLoops, with this total result providing a rough estimate as to Python's average performance on a given system. This test profile runs PyBench each time for 20 rounds. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyBench 2018-02-16Total For Average Test TimesEPYC 7713 2PEPYC 77132004006008001000SE +/- 2.60, N = 3SE +/- 4.10, N = 3953959
MinAvgMaxEPYC 771337.270.376.9EPYC 7713 2P80.6139.8154.7OpenBenchmarking.orgWatts, Fewer Is BetterPyBench 2018-02-16CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 1852 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 3683 Joules per run.

Node.js Express HTTP Load Test

A Node.js Express server with a Node-based loadtest client for facilitating HTTP benchmarking. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgRequests Per Second, More Is BetterNode.js Express HTTP Load TestEPYC 7713EPYC 7713 2P14002800420056007000SE +/- 32.02, N = 4SE +/- 59.99, N = 463676198
OpenBenchmarking.orgRequests Per Second Per Watt, More Is BetterNode.js Express HTTP Load TestEPYC 7713EPYC 7713 2P2040608010084.0641.72
MinAvgMaxEPYC 771337.575.789.7EPYC 7713 2P81.1148.6183.3OpenBenchmarking.orgWatts, Fewer Is BetterNode.js Express HTTP Load TestCPU Power Consumption Monitor501001502002501. EPYC 7713: Approximate power consumption of 1515 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 3008 Joules per run.

srsRAN

srsRAN is an open-source LTE/5G software radio suite created by Software Radio Systems (SRS). The srsRAN radio suite was formerly known as srsLTE and can be used for building your own software-defined radio (SDR) 4G/5G mobile network. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgUE Mb/s, More Is BettersrsRAN 22.04.1Test: 5G PHY_DL_NR Test 52 PRB SISO 64-QAMEPYC 7713EPYC 7713 2P1428425670SE +/- 0.12, N = 3SE +/- 0.06, N = 362.462.21. (CXX) g++ options: -std=c++14 -fno-strict-aliasing -march=native -mfpmath=sse -mavx2 -fvisibility=hidden -O3 -fno-trapping-math -fno-math-errno -ldl -lpthread -lm
OpenBenchmarking.orgUE Mb/s Per Watt, More Is BettersrsRAN 22.04.1Test: 5G PHY_DL_NR Test 52 PRB SISO 64-QAMEPYC 7713EPYC 7713 2P0.20180.40360.60540.80721.0090.8970.445
MinAvgMaxEPYC 771337.269.576.0EPYC 7713 2P80.5139.8151.0OpenBenchmarking.orgWatts, Fewer Is BettersrsRAN 22.04.1CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 1715 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 3447 Joules per run.

OpenBenchmarking.orgeNb Mb/s, More Is BettersrsRAN 22.04.1Test: 5G PHY_DL_NR Test 52 PRB SISO 64-QAMEPYC 7713EPYC 7713 2P306090120150SE +/- 0.18, N = 3SE +/- 0.52, N = 3128.9127.91. (CXX) g++ options: -std=c++14 -fno-strict-aliasing -march=native -mfpmath=sse -mavx2 -fvisibility=hidden -O3 -fno-trapping-math -fno-math-errno -ldl -lpthread -lm

Intel Open Image Denoise

Open Image Denoise is a denoising library for ray-tracing and part of the oneAPI rendering toolkit. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgImages / Sec, More Is BetterIntel Open Image Denoise 1.4.0Run: RT.hdr_alb_nrm.3840x2160EPYC 7713 2PEPYC 77130.50851.0171.52552.0342.5425SE +/- 0.00, N = 4SE +/- 0.00, N = 32.261.40
OpenBenchmarking.orgImages / Sec Per Watt, More Is BetterIntel Open Image Denoise 1.4.0Run: RT.hdr_alb_nrm.3840x2160EPYC 7713EPYC 7713 2P0.00230.00460.00690.00920.01150.0100.009
MinAvgMaxEPYC 771337.6144.1228.3EPYC 7713 2P81.1265.0458.5OpenBenchmarking.orgWatts, Fewer Is BetterIntel Open Image Denoise 1.4.0CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 3698 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 4903 Joules per run.

NAS Parallel Benchmarks

NPB, NAS Parallel Benchmarks, is a benchmark developed by NASA for high-end computer systems. This test profile currently uses the MPI version of NPB. This test profile offers selecting the different NPB tests/problems and varying problem sizes. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: BT.CEPYC 7713 2PEPYC 771350K100K150K200K250KSE +/- 391.85, N = 4SE +/- 152.57, N = 3235808.54127998.611. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.2
OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: BT.CEPYC 7713 2PEPYC 7713140280420560700670.57669.13
MinAvgMaxEPYC 771338.1191.3229.4EPYC 7713 2P79.7351.7458.8OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 5229 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 5978 Joules per run.

SVT-HEVC

This is a test of the Intel Open Visual Cloud Scalable Video Technology SVT-HEVC CPU-based multi-threaded video encoder for the HEVC / H.265 video format with a sample YUV video file. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-HEVC 1.5.0Tuning: 10 - Input: Bosphorus 4KEPYC 7713EPYC 7713 2P50100150200250SE +/- 0.71, N = 9SE +/- 2.01, N = 15223.66195.621. (CC) gcc options: -fPIE -fPIC -O3 -O2 -pie -rdynamic -lpthread -lrt
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterSVT-HEVC 1.5.0Tuning: 10 - Input: Bosphorus 4KEPYC 7713EPYC 7713 2P0.47750.9551.43251.912.38752.1221.009
MinAvgMaxEPYC 771337.5105.4226.3EPYC 7713 2P40.6194.0392.7OpenBenchmarking.orgWatts, Fewer Is BetterSVT-HEVC 1.5.0CPU Power Consumption Monitor1102203304405501. EPYC 7713: Approximate power consumption of 820 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 1978 Joules per run.

Intel Open Image Denoise

Open Image Denoise is a denoising library for ray-tracing and part of the oneAPI rendering toolkit. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgImages / Sec, More Is BetterIntel Open Image Denoise 1.4.0Run: RT.ldr_alb_nrm.3840x2160EPYC 7713 2PEPYC 77130.50851.0171.52552.0342.5425SE +/- 0.00, N = 4SE +/- 0.00, N = 32.261.41
OpenBenchmarking.orgImages / Sec Per Watt, More Is BetterIntel Open Image Denoise 1.4.0Run: RT.ldr_alb_nrm.3840x2160EPYC 7713EPYC 7713 2P0.00230.00460.00690.00920.01150.0100.009
MinAvgMaxEPYC 771337.4143.7228.4EPYC 7713 2P80.8264.4458.1OpenBenchmarking.orgWatts, Fewer Is BetterIntel Open Image Denoise 1.4.0CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 3687 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 4892 Joules per run.

Timed Apache Compilation

This test times how long it takes to build the Apache HTTPD web server. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed Apache Compilation 2.4.41Time To CompileEPYC 7713EPYC 7713 2P510152025SE +/- 0.01, N = 3SE +/- 0.01, N = 320.1020.52
MinAvgMaxEPYC 771337.481.0129.0EPYC 7713 2P79.6154.9245.3OpenBenchmarking.orgWatts, Fewer Is BetterTimed Apache Compilation 2.4.41CPU Power Consumption Monitor601201802403001. EPYC 7713: Approximate power consumption of 2484 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 4905 Joules per run.

ASKAP

ASKAP is a set of benchmarks from the Australian SKA Pathfinder. The principal ASKAP benchmarks are the Hogbom Clean Benchmark (tHogbomClean) and Convolutional Resamping Benchmark (tConvolve) as well as some previous ASKAP benchmarks being included as well for OpenCL and CUDA execution of tConvolve. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgIterations Per Second, More Is BetterASKAP 1.0Test: Hogbom Clean OpenMPEPYC 7713EPYC 7713 2P110220330440550SE +/- 1.11, N = 4SE +/- 1.13, N = 4520.84319.761. (CXX) g++ options: -O3 -fstrict-aliasing -fopenmp
OpenBenchmarking.orgIterations Per Second Per Watt, More Is BetterASKAP 1.0Test: Hogbom Clean OpenMPEPYC 7713EPYC 7713 2P1.27942.55883.83825.11766.3975.6861.759
MinAvgMaxEPYC 771337.792.5193.0EPYC 7713 2P81.1181.8371.2OpenBenchmarking.orgWatts, Fewer Is BetterASKAP 1.0CPU Power Consumption Monitor1002003004005001. EPYC 7713: Approximate power consumption of 1734 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 3635 Joules per run.

Liquid-DSP

LiquidSDR's Liquid-DSP is a software-defined radio (SDR) digital signal processing library. This test profile runs a multi-threaded benchmark of this SDR/DSP library focused on embedded platform usage. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgsamples/s, More Is BetterLiquid-DSP 2021.01.31Threads: 256 - Buffer Length: 256 - Filter Length: 57EPYC 7713 2PEPYC 77131100M2200M3300M4400M5500MSE +/- 1628905.56, N = 3SE +/- 11518439.32, N = 3535090000027671666671. (CC) gcc options: -O3 -pthread -lm -lc -lliquid
OpenBenchmarking.orgsamples/s Per Watt, More Is BetterLiquid-DSP 2021.01.31Threads: 256 - Buffer Length: 256 - Filter Length: 57EPYC 7713EPYC 7713 2P3M6M9M12M15M14002290.2913522276.42
MinAvgMaxEPYC 771337.8197.6230.3EPYC 7713 2P80.5395.7460.6OpenBenchmarking.orgWatts, Fewer Is BetterLiquid-DSP 2021.01.31CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 4875 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 9365 Joules per run.

Kvazaar

This is a test of Kvazaar as a CPU-based H.265/HEVC video encoder written in the C programming language and optimized in Assembly. Kvazaar is the winner of the 2016 ACM Open-Source Software Competition and developed at the Ultra Video Group, Tampere University, Finland. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterKvazaar 2.1Video Input: Bosphorus 4K - Video Preset: Very FastEPYC 7713 2PEPYC 77131224364860SE +/- 0.17, N = 5SE +/- 0.03, N = 454.2436.711. (CC) gcc options: -pthread -ftree-vectorize -fvisibility=hidden -O2 -lpthread -lm -lrt
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterKvazaar 2.1Video Input: Bosphorus 4K - Video Preset: Very FastEPYC 7713EPYC 7713 2P0.05130.10260.15390.20520.25650.2280.180
MinAvgMaxEPYC 771337.5161.3199.9EPYC 7713 2P81.2301.2419.8OpenBenchmarking.orgWatts, Fewer Is BetterKvazaar 2.1CPU Power Consumption Monitor1102203304405501. EPYC 7713: Approximate power consumption of 3307 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 4519 Joules per run.

Liquid-DSP

LiquidSDR's Liquid-DSP is a software-defined radio (SDR) digital signal processing library. This test profile runs a multi-threaded benchmark of this SDR/DSP library focused on embedded platform usage. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgsamples/s, More Is BetterLiquid-DSP 2021.01.31Threads: 128 - Buffer Length: 256 - Filter Length: 57EPYC 7713 2PEPYC 77131100M2200M3300M4400M5500MSE +/- 3142362.88, N = 3SE +/- 360555.13, N = 3508506666727069000001. (CC) gcc options: -O3 -pthread -lm -lc -lliquid
OpenBenchmarking.orgsamples/s Per Watt, More Is BetterLiquid-DSP 2021.01.31Threads: 128 - Buffer Length: 256 - Filter Length: 57EPYC 7713EPYC 7713 2P3M6M9M12M15M13782846.1212899838.02
MinAvgMaxEPYC 771337.7196.4230.3EPYC 7713 2P79.4394.2460.6OpenBenchmarking.orgWatts, Fewer Is BetterLiquid-DSP 2021.01.31CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 4779 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 9592 Joules per run.

OpenBenchmarking.orgsamples/s, More Is BetterLiquid-DSP 2021.01.31Threads: 64 - Buffer Length: 256 - Filter Length: 57EPYC 7713 2PEPYC 7713700M1400M2100M2800M3500MSE +/- 2355372.11, N = 3SE +/- 5184056.76, N = 3319153333325776666671. (CC) gcc options: -O3 -pthread -lm -lc -lliquid
OpenBenchmarking.orgsamples/s Per Watt, More Is BetterLiquid-DSP 2021.01.31Threads: 64 - Buffer Length: 256 - Filter Length: 57EPYC 7713EPYC 7713 2P3M6M9M12M15M13069058.458517166.22
MinAvgMaxEPYC 771337.9197.2230.3EPYC 7713 2P79.7374.7438.4OpenBenchmarking.orgWatts, Fewer Is BetterLiquid-DSP 2021.01.31CPU Power Consumption Monitor1102203304405501. EPYC 7713: Approximate power consumption of 4734 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 8868 Joules per run.

OpenBenchmarking.orgsamples/s, More Is BetterLiquid-DSP 2021.01.31Threads: 32 - Buffer Length: 256 - Filter Length: 57EPYC 7713 2PEPYC 7713300M600M900M1200M1500MSE +/- 6470531.49, N = 3SE +/- 1217009.08, N = 3161426666716055666671. (CC) gcc options: -O3 -pthread -lm -lc -lliquid
OpenBenchmarking.orgsamples/s Per Watt, More Is BetterLiquid-DSP 2021.01.31Threads: 32 - Buffer Length: 256 - Filter Length: 57EPYC 7713EPYC 7713 2P2M4M6M8M10M8924738.576183433.64
MinAvgMaxEPYC 771337.3179.9211.6EPYC 7713 2P80.3261.1314.8OpenBenchmarking.orgWatts, Fewer Is BetterLiquid-DSP 2021.01.31CPU Power Consumption Monitor801602403204001. EPYC 7713: Approximate power consumption of 4318 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 6353 Joules per run.

NAS Parallel Benchmarks

NPB, NAS Parallel Benchmarks, is a benchmark developed by NASA for high-end computer systems. This test profile currently uses the MPI version of NPB. This test profile offers selecting the different NPB tests/problems and varying problem sizes. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: SP.BEPYC 7713 2PEPYC 771330K60K90K120K150KSE +/- 1089.48, N = 15SE +/- 824.60, N = 15142675.5589950.761. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.2
OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: SP.BEPYC 7713EPYC 7713 2P150300450600750698.95628.09
MinAvgMaxEPYC 771337.7128.7229.8EPYC 7713 2P80.4227.2460.4OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 1107 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 1681 Joules per run.

srsRAN

srsRAN is an open-source LTE/5G software radio suite created by Software Radio Systems (SRS). The srsRAN radio suite was formerly known as srsLTE and can be used for building your own software-defined radio (SDR) 4G/5G mobile network. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgUE Mb/s, More Is BettersrsRAN 22.04.1Test: 4G PHY_DL_Test 100 PRB SISO 256-QAMEPYC 7713EPYC 7713 2P306090120150SE +/- 0.86, N = 3SE +/- 0.29, N = 3149.6149.61. (CXX) g++ options: -std=c++14 -fno-strict-aliasing -march=native -mfpmath=sse -mavx2 -fvisibility=hidden -O3 -fno-trapping-math -fno-math-errno -ldl -lpthread -lm
OpenBenchmarking.orgUE Mb/s Per Watt, More Is BettersrsRAN 22.04.1Test: 4G PHY_DL_Test 100 PRB SISO 256-QAMEPYC 7713EPYC 7713 2P0.48220.96441.44661.92882.4112.1431.069
MinAvgMaxEPYC 771337.269.876.8EPYC 7713 2P80.5140.0154.8OpenBenchmarking.orgWatts, Fewer Is BettersrsRAN 22.04.1CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 1676 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 3360 Joules per run.

OpenBenchmarking.orgeNb Mb/s, More Is BettersrsRAN 22.04.1Test: 4G PHY_DL_Test 100 PRB SISO 256-QAMEPYC 7713EPYC 7713 2P90180270360450SE +/- 1.02, N = 3SE +/- 0.95, N = 3430.5427.41. (CXX) g++ options: -std=c++14 -fno-strict-aliasing -march=native -mfpmath=sse -mavx2 -fvisibility=hidden -O3 -fno-trapping-math -fno-math-errno -ldl -lpthread -lm

Timed Mesa Compilation

This test profile times how long it takes to compile Mesa with Meson/Ninja. For minimizing build dependencies and avoid versioning conflicts, test this is just the core Mesa build without LLVM or the extra Gallium3D/Mesa drivers enabled. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed Mesa Compilation 21.0Time To CompileEPYC 7713 2PEPYC 7713510152025SE +/- 0.08, N = 3SE +/- 0.01, N = 318.1020.61
MinAvgMaxEPYC 771337.7126.1230.3EPYC 7713 2P80.8212.6461.0OpenBenchmarking.orgWatts, Fewer Is BetterTimed Mesa Compilation 21.0CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 3279 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 5031 Joules per run.

TNN

TNN is an open-source deep learning reasoning framework developed by Tencent. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterTNN 0.3Target: CPU - Model: SqueezeNet v1.1EPYC 7713EPYC 7713 2P60120180240300SE +/- 0.03, N = 3SE +/- 0.08, N = 3273.48273.85MIN: 273.31 / MAX: 275.21MIN: 273.49 / MAX: 274.551. (CXX) g++ options: -fopenmp -pthread -fvisibility=hidden -fvisibility=default -O3 -rdynamic -ldl
MinAvgMaxEPYC 771337.268.675.3EPYC 7713 2P81.1138.9151.6OpenBenchmarking.orgWatts, Fewer Is BetterTNN 0.3CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 1600 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 3195 Joules per run.

Timed FFmpeg Compilation

This test times how long it takes to build the FFmpeg multimedia library. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed FFmpeg Compilation 4.4Time To CompileEPYC 7713 2PEPYC 771348121620SE +/- 0.08, N = 4SE +/- 0.06, N = 314.2718.15
MinAvgMaxEPYC 771337.4129.8230.1EPYC 7713 2P80.6224.4460.3OpenBenchmarking.orgWatts, Fewer Is BetterTimed FFmpeg Compilation 4.4CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 3244 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 4600 Joules per run.

JPEG XL Decoding libjxl

The JPEG XL Image Coding System is designed to provide next-generation JPEG image capabilities with JPEG XL offering better image quality and compression over legacy JPEG. This test profile is suited for JPEG XL decode performance testing to PNG output file, the pts/jpexl test is for encode performance. The JPEG XL encoding/decoding is done using the libjxl codebase. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMP/s, More Is BetterJPEG XL Decoding libjxl 0.6.1CPU Threads: AllEPYC 7713EPYC 7713 2P160320480640800SE +/- 0.45, N = 4SE +/- 3.90, N = 3731.88569.97
OpenBenchmarking.orgMP/s Per Watt, More Is BetterJPEG XL Decoding libjxl 0.6.1CPU Threads: AllEPYC 7713EPYC 7713 2P2468107.3252.802
MinAvgMaxEPYC 771337.199.9169.3EPYC 7713 2P80.2203.4320.5OpenBenchmarking.orgWatts, Fewer Is BetterJPEG XL Decoding libjxl 0.6.1CPU Power Consumption Monitor801602403204001. EPYC 7713: Approximate power consumption of 1849 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 4271 Joules per run.

Basis Universal

Basis Universal is a GPU texture codec. This test times how long it takes to convert sRGB PNGs into Basis Univeral assets with various settings. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterBasis Universal 1.13Settings: UASTC Level 3EPYC 7713 2PEPYC 771348121620SE +/- 0.07, N = 4SE +/- 0.01, N = 411.4015.391. (CXX) g++ options: -std=c++11 -fvisibility=hidden -fPIC -fno-strict-aliasing -O3 -rdynamic -lm -lpthread
MinAvgMaxEPYC 771337.4141.1230.3EPYC 7713 2P80.4231.5460.4OpenBenchmarking.orgWatts, Fewer Is BetterBasis Universal 1.13CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 2751 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 3588 Joules per run.

Sysbench

This is a benchmark of Sysbench with the built-in CPU and memory sub-tests. Sysbench is a scriptable multi-threaded benchmark tool based on LuaJIT. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMiB/sec, More Is BetterSysbench 1.0.20Test: RAM / MemoryEPYC 7713EPYC 7713 2P2K4K6K8K10KSE +/- 21.53, N = 5SE +/- 89.77, N = 49990.127260.661. (CC) gcc options: -O2 -funroll-loops -rdynamic -ldl -laio -lm
OpenBenchmarking.orgMiB/sec Per Watt, More Is BetterSysbench 1.0.20Test: RAM / MemoryEPYC 7713EPYC 7713 2P2040608010087.2829.18
MinAvgMaxEPYC 771337.8114.5147.2EPYC 7713 2P80.4248.9306.9OpenBenchmarking.orgWatts, Fewer Is BetterSysbench 1.0.20CPU Power Consumption Monitor801602403204001. EPYC 7713: Approximate power consumption of 1625 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 4479 Joules per run.

NAS Parallel Benchmarks

NPB, NAS Parallel Benchmarks, is a benchmark developed by NASA for high-end computer systems. This test profile currently uses the MPI version of NPB. This test profile offers selecting the different NPB tests/problems and varying problem sizes. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: LU.CEPYC 7713 2PEPYC 771360K120K180K240K300KSE +/- 2852.04, N = 5SE +/- 264.51, N = 4259899.49135917.431. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.2
OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: LU.CEPYC 7713 2PEPYC 77132004006008001000816.26767.46
MinAvgMaxEPYC 771337.9177.1226.2EPYC 7713 2P80.7318.4459.8OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 3453 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 4076 Joules per run.

SVT-AV1

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 1.2Encoder Mode: Preset 12 - Input: Bosphorus 4KEPYC 7713EPYC 7713 2P4080120160200SE +/- 7.71, N = 15SE +/- 1.07, N = 7184.71179.551. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterSVT-AV1 1.2Encoder Mode: Preset 12 - Input: Bosphorus 4KEPYC 7713EPYC 7713 2P0.43830.87661.31491.75322.19151.9480.984
MinAvgMaxEPYC 771337.394.8191.2EPYC 7713 2P80.8182.5323.5OpenBenchmarking.orgWatts, Fewer Is BetterSVT-AV1 1.2CPU Power Consumption Monitor801602403204001. EPYC 7713: Approximate power consumption of 822 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 1695 Joules per run.

AOM AV1

This is a test of the AOMedia AV1 encoder (libaom) developed by AOMedia and Google as the AV1 Codec Library. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterAOM AV1 3.4Encoder Mode: Speed 9 Realtime - Input: Bosphorus 4KEPYC 7713EPYC 7713 2P1530456075SE +/- 0.10, N = 5SE +/- 0.33, N = 565.4955.621. (CXX) g++ options: -O3 -std=c++11 -U_FORTIFY_SOURCE -lm
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterAOM AV1 3.4Encoder Mode: Speed 9 Realtime - Input: Bosphorus 4KEPYC 7713EPYC 7713 2P0.14850.2970.44550.5940.74250.6600.301
MinAvgMaxEPYC 771337.499.3126.8EPYC 7713 2P80.2184.9234.4OpenBenchmarking.orgWatts, Fewer Is BetterAOM AV1 3.4CPU Power Consumption Monitor601201802403001. EPYC 7713: Approximate power consumption of 1390 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 2885 Joules per run.

SVT-AV1

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 1.2Encoder Mode: Preset 8 - Input: Bosphorus 4KEPYC 7713EPYC 7713 2P1632486480SE +/- 0.18, N = 5SE +/- 0.45, N = 570.7868.801. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterSVT-AV1 1.2Encoder Mode: Preset 8 - Input: Bosphorus 4KEPYC 7713EPYC 7713 2P0.12110.24220.36330.48440.60550.5380.297
MinAvgMaxEPYC 771337.3131.7204.2EPYC 7713 2P81.0231.8351.6OpenBenchmarking.orgWatts, Fewer Is BetterSVT-AV1 1.2CPU Power Consumption Monitor1002003004005001. EPYC 7713: Approximate power consumption of 1843 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 3476 Joules per run.

ASTC Encoder

ASTC Encoder (astcenc) is for the Adaptive Scalable Texture Compression (ASTC) format commonly used with OpenGL, OpenGL ES, and Vulkan graphics APIs. This test profile does a coding test of both compression/decompression. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMT/s, More Is BetterASTC Encoder 4.0Preset: ExhaustiveEPYC 7713 2PEPYC 7713246810SE +/- 0.0100, N = 4SE +/- 0.0030, N = 36.44993.26591. (CXX) g++ options: -O3 -flto -pthread
OpenBenchmarking.orgMT/s Per Watt, More Is BetterASTC Encoder 4.0Preset: ExhaustiveEPYC 7713 2PEPYC 77130.00520.01040.01560.02080.0260.0230.019
MinAvgMaxEPYC 771337.5169.6230.3EPYC 7713 2P80.8278.8460.9OpenBenchmarking.orgWatts, Fewer Is BetterASTC Encoder 4.0CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 3844 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 4252 Joules per run.

WebP Image Encode

This is a test of Google's libwebp with the cwebp image encode utility and using a sample 6000x4000 pixel JPEG image as the input. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMP/s, More Is BetterWebP Image Encode 1.2.4Encode Settings: Quality 100, LosslessEPYC 7713 2PEPYC 77130.31950.6390.95851.2781.5975SE +/- 0.01, N = 3SE +/- 0.01, N = 31.421.401. (CC) gcc options: -fvisibility=hidden -O2 -lm
OpenBenchmarking.orgMP/s Per Watt, More Is BetterWebP Image Encode 1.2.4Encode Settings: Quality 100, LosslessEPYC 7713EPYC 7713 2P0.00470.00940.01410.01880.02350.0210.010
MinAvgMaxEPYC 771337.267.777.7EPYC 7713 2P79.2136.0150.9OpenBenchmarking.orgWatts, Fewer Is BetterWebP Image Encode 1.2.4CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 1444 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 2901 Joules per run.

Embree

Intel Embree is a collection of high-performance ray-tracing kernels for execution on CPUs and supporting instruction sets such as SSE, AVX, AVX2, and AVX-512. Embree also supports making use of the Intel SPMD Program Compiler (ISPC). Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterEmbree 3.13Binary: Pathtracer ISPC - Model: CrownEPYC 7713 2PEPYC 771320406080100SE +/- 0.07, N = 6SE +/- 0.05, N = 482.9347.56MIN: 77.94 / MAX: 92.77MIN: 46.72 / MAX: 49.62
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterEmbree 3.13Binary: Pathtracer ISPC - Model: CrownEPYC 7713EPYC 7713 2P0.05990.11980.17970.23960.29950.2660.255
MinAvgMaxEPYC 771337.6179.0229.7EPYC 7713 2P81.0325.7458.8OpenBenchmarking.orgWatts, Fewer Is BetterEmbree 3.13CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 3133 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 3962 Joules per run.

JPEG XL libjxl

The JPEG XL Image Coding System is designed to provide next-generation JPEG image capabilities with JPEG XL offering better image quality and compression over legacy JPEG. This test profile is currently focused on the multi-threaded JPEG XL image encode performance using the reference libjxl library. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMP/s, More Is BetterJPEG XL libjxl 0.6.1Input: JPEG - Encode Speed: 8EPYC 7713EPYC 7713 2P714212835SE +/- 0.17, N = 5SE +/- 0.04, N = 529.2428.921. (CXX) g++ options: -funwind-tables -O3 -O2 -fPIE -pie
OpenBenchmarking.orgMP/s Per Watt, More Is BetterJPEG XL libjxl 0.6.1Input: JPEG - Encode Speed: 8EPYC 7713EPYC 7713 2P0.09320.18640.27960.37280.4660.4140.201
MinAvgMaxEPYC 771337.370.687.2EPYC 7713 2P80.5144.2180.4OpenBenchmarking.orgWatts, Fewer Is BetterJPEG XL libjxl 0.6.1CPU Power Consumption Monitor501001502002501. EPYC 7713: Approximate power consumption of 1003 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 2077 Joules per run.

POV-Ray

This is a test of POV-Ray, the Persistence of Vision Raytracer. POV-Ray is used to create 3D graphics using ray-tracing. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPOV-Ray 3.7.0.7Trace TimeEPYC 7713 2PEPYC 77133691215SE +/- 0.050, N = 5SE +/- 0.067, N = 47.59010.6801. (CXX) g++ options: -pipe -O3 -ffast-math -march=native -R/usr/lib -lSDL -lXpm -lSM -lICE -lX11 -lIlmImf -lIlmImf-2_5 -lImath-2_5 -lHalf-2_5 -lIex-2_5 -lIexMath-2_5 -lIlmThread-2_5 -lIlmThread -ltiff -ljpeg -lpng -lz -lrt -lm -lboost_thread -lboost_system
MinAvgMaxEPYC 771337.8150.2230.3EPYC 7713 2P80.6246.9460.5OpenBenchmarking.orgWatts, Fewer Is BetterPOV-Ray 3.7.0.7CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 2554 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 3407 Joules per run.

ACES DGEMM

This is a multi-threaded DGEMM benchmark. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgGFLOP/s, More Is BetterACES DGEMM 1.0Sustained Floating-Point RateEPYC 7713 2PEPYC 7713714212835SE +/- 0.29, N = 6SE +/- 0.08, N = 532.0720.881. (CC) gcc options: -O3 -march=native -fopenmp
OpenBenchmarking.orgGFLOP/s Per Watt, More Is BetterACES DGEMM 1.0Sustained Floating-Point RateEPYC 7713EPYC 7713 2P0.02770.05540.08310.11080.13850.1230.104
MinAvgMaxEPYC 771337.5169.8230.3EPYC 7713 2P81.0309.4460.6OpenBenchmarking.orgWatts, Fewer Is BetterACES DGEMM 1.0CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 2582 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 3507 Joules per run.

Basis Universal

Basis Universal is a GPU texture codec. This test times how long it takes to convert sRGB PNGs into Basis Univeral assets with various settings. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterBasis Universal 1.13Settings: UASTC Level 2EPYC 7713 2PEPYC 77133691215SE +/- 0.029, N = 5SE +/- 0.005, N = 58.77310.6151. (CXX) g++ options: -std=c++11 -fvisibility=hidden -fPIC -fno-strict-aliasing -O3 -rdynamic -lm -lpthread
MinAvgMaxEPYC 771337.3112.7230.3EPYC 7713 2P80.6187.4444.9OpenBenchmarking.orgWatts, Fewer Is BetterBasis Universal 1.13CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 1646 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 2437 Joules per run.

Embree

Intel Embree is a collection of high-performance ray-tracing kernels for execution on CPUs and supporting instruction sets such as SSE, AVX, AVX2, and AVX-512. Embree also supports making use of the Intel SPMD Program Compiler (ISPC). Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterEmbree 3.13Binary: Pathtracer - Model: CrownEPYC 7713 2PEPYC 771320406080100SE +/- 0.10, N = 6SE +/- 0.16, N = 489.6051.83MIN: 83.63 / MAX: 104.61MIN: 50.82 / MAX: 57.89
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterEmbree 3.13Binary: Pathtracer - Model: CrownEPYC 7713EPYC 7713 2P0.06620.13240.19860.26480.3310.2940.280
MinAvgMaxEPYC 771337.8176.2229.8EPYC 7713 2P81.1319.9458.4OpenBenchmarking.orgWatts, Fewer Is BetterEmbree 3.13CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 2951 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 3733 Joules per run.

ASTC Encoder

ASTC Encoder (astcenc) is for the Adaptive Scalable Texture Compression (ASTC) format commonly used with OpenGL, OpenGL ES, and Vulkan graphics APIs. This test profile does a coding test of both compression/decompression. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMT/s, More Is BetterASTC Encoder 4.0Preset: ThoroughEPYC 7713 2PEPYC 77131326395265SE +/- 0.06, N = 5SE +/- 0.02, N = 458.4730.461. (CXX) g++ options: -O3 -flto -pthread
OpenBenchmarking.orgMT/s Per Watt, More Is BetterASTC Encoder 4.0Preset: ThoroughEPYC 7713 2PEPYC 77130.05580.11160.16740.22320.2790.2480.213
MinAvgMaxEPYC 771337.2143.2230.0EPYC 7713 2P80.7235.5457.3OpenBenchmarking.orgWatts, Fewer Is BetterASTC Encoder 4.0CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 2292 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 2920 Joules per run.

LAME MP3 Encoding

LAME is an MP3 encoder licensed under the LGPL. This test measures the time required to encode a WAV file to MP3 format. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterLAME MP3 Encoding 3.100WAV To MP3EPYC 7713EPYC 7713 2P246810SE +/- 0.005, N = 6SE +/- 0.006, N = 67.4727.4901. (CC) gcc options: -O3 -ffast-math -funroll-loops -fschedule-insns2 -fbranch-count-reg -fforce-addr -pipe -lncurses -lm
MinAvgMaxEPYC 771337.262.475.8EPYC 7713 2P80.2128.1152.7OpenBenchmarking.orgWatts, Fewer Is BetterLAME MP3 Encoding 3.100CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 717 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 1474 Joules per run.

Google Draco

Draco is a library developed by Google for compressing/decompressing 3D geometric meshes and point clouds. This test profile uses some Artec3D PLY models as the sample 3D model input formats for Draco compression/decompression. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterGoogle Draco 1.5.0Model: Church FacadeEPYC 7713EPYC 7713 2P16003200480064008000SE +/- 13.40, N = 5SE +/- 3.69, N = 5741175251. (CXX) g++ options: -O3
MinAvgMaxEPYC 771337.263.980.2EPYC 7713 2P80.9129.6156.5OpenBenchmarking.orgWatts, Fewer Is BetterGoogle Draco 1.5.0CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 805 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 1710 Joules per run.

NAS Parallel Benchmarks

NPB, NAS Parallel Benchmarks, is a benchmark developed by NASA for high-end computer systems. This test profile currently uses the MPI version of NPB. This test profile offers selecting the different NPB tests/problems and varying problem sizes. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: CG.CEPYC 7713 2PEPYC 771310K20K30K40K50KSE +/- 340.17, N = 11SE +/- 119.51, N = 645587.1024085.731. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.2
OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: CG.CEPYC 7713 2PEPYC 77134080120160200181.81165.19
MinAvgMaxEPYC 771337.5145.8229.2EPYC 7713 2P81.4250.7460.8OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 1555 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 2074 Joules per run.

oneDNN

This is a test of the Intel oneDNN as an Intel-optimized library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Intel oneDNN was formerly known as DNNL (Deep Neural Network Library) and MKL-DNN before being rebranded as part of Intel oneAPI. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 2.6Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPUEPYC 7713 2PEPYC 77130.21030.42060.63090.84121.0515SE +/- 0.003112, N = 7SE +/- 0.000519, N = 70.6408330.934706MIN: 0.59MIN: 0.871. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -std=c++11 -pie -ldl -lpthread
MinAvgMaxEPYC 771337.8149.1230.3EPYC 7713 2P80.5273.0439.5OpenBenchmarking.orgWatts, Fewer Is BetteroneDNN 2.6CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 1533 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 2847 Joules per run.

Pennant

Pennant is an application focused on hydrodynamics on general unstructured meshes in 2D. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgHydro Cycle Time - Seconds, Fewer Is BetterPennant 1.0.1Test: sedovbigEPYC 7713 2PEPYC 77133691215SE +/- 0.039464, N = 6SE +/- 0.119128, N = 45.67589511.4272301. (CXX) g++ options: -fopenmp -lmpi_cxx -lmpi
MinAvgMaxEPYC 771337.5173.1229.8EPYC 7713 2P80.6294.1460.6OpenBenchmarking.orgWatts, Fewer Is BetterPennant 1.0.1CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 2770 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 3088 Joules per run.

m-queens

A solver for the N-queens problem with multi-threading support via the OpenMP library. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is Betterm-queens 1.2Time To SolveEPYC 7713 2PEPYC 77133691215SE +/- 0.027, N = 6SE +/- 0.015, N = 46.37612.2381. (CXX) g++ options: -fopenmp -O2 -march=native
MinAvgMaxEPYC 771337.2178.3230.1EPYC 7713 2P80.1309.7460.6OpenBenchmarking.orgWatts, Fewer Is Betterm-queens 1.2CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 2897 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 3200 Joules per run.

libavif avifenc

This is a test of the AOMedia libavif library testing the encoding of a JPEG image to AV1 Image Format (AVIF). Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is Betterlibavif avifenc 0.10Encoder Speed: 6, LosslessEPYC 7713EPYC 7713 2P246810SE +/- 0.022, N = 6SE +/- 0.018, N = 67.1847.3341. (CXX) g++ options: -O3 -fPIC -lm
MinAvgMaxEPYC 771337.395.9230.3EPYC 7713 2P80.2162.2308.4OpenBenchmarking.orgWatts, Fewer Is Betterlibavif avifenc 0.10CPU Power Consumption Monitor801602403204001. EPYC 7713: Approximate power consumption of 1071 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 1838 Joules per run.

Google Draco

Draco is a library developed by Google for compressing/decompressing 3D geometric meshes and point clouds. This test profile uses some Artec3D PLY models as the sample 3D model input formats for Draco compression/decompression. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterGoogle Draco 1.5.0Model: LionEPYC 7713EPYC 7713 2P12002400360048006000SE +/- 13.48, N = 6SE +/- 20.00, N = 6561357351. (CXX) g++ options: -O3
MinAvgMaxEPYC 771337.162.281.0EPYC 7713 2P80.0126.8158.2OpenBenchmarking.orgWatts, Fewer Is BetterGoogle Draco 1.5.0CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 695 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 1437 Joules per run.

WebP Image Encode

This is a test of Google's libwebp with the cwebp image encode utility and using a sample 6000x4000 pixel JPEG image as the input. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMP/s, More Is BetterWebP Image Encode 1.2.4Encode Settings: Quality 100, Highest CompressionEPYC 7713EPYC 7713 2P0.7831.5662.3493.1323.915SE +/- 0.00, N = 6SE +/- 0.00, N = 63.483.471. (CC) gcc options: -fvisibility=hidden -O2 -lm
OpenBenchmarking.orgMP/s Per Watt, More Is BetterWebP Image Encode 1.2.4Encode Settings: Quality 100, Highest CompressionEPYC 7713EPYC 7713 2P0.01260.02520.03780.05040.0630.0560.028
MinAvgMaxEPYC 771337.062.075.4EPYC 7713 2P78.6125.9150.5OpenBenchmarking.orgWatts, Fewer Is BetterWebP Image Encode 1.2.4CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 693 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 1406 Joules per run.

Rodinia

Rodinia is a suite focused upon accelerating compute-intensive applications with accelerators. CUDA, OpenMP, and OpenCL parallel models are supported by the included applications. This profile utilizes select OpenCL, NVIDIA CUDA and OpenMP test binaries at the moment. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterRodinia 3.1Test: OpenMP CFD SolverEPYC 7713 2PEPYC 7713246810SE +/- 0.026, N = 7SE +/- 0.014, N = 66.2836.8451. (CXX) g++ options: -O2 -lOpenCL
MinAvgMaxEPYC 771337.5134.1228.4EPYC 7713 2P81.0257.2439.8OpenBenchmarking.orgWatts, Fewer Is BetterRodinia 3.1CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 1453 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 2682 Joules per run.

SVT-AV1

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 1.2Encoder Mode: Preset 10 - Input: Bosphorus 4KEPYC 7713EPYC 7713 2P306090120150SE +/- 0.47, N = 7SE +/- 0.78, N = 6131.27123.811. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterSVT-AV1 1.2Encoder Mode: Preset 10 - Input: Bosphorus 4KEPYC 7713EPYC 7713 2P0.27990.55980.83971.11961.39951.2440.624
MinAvgMaxEPYC 771337.4105.5191.2EPYC 7713 2P81.6198.5329.2OpenBenchmarking.orgWatts, Fewer Is BetterSVT-AV1 1.2CPU Power Consumption Monitor801602403204001. EPYC 7713: Approximate power consumption of 1055 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 2216 Joules per run.

NAS Parallel Benchmarks

NPB, NAS Parallel Benchmarks, is a benchmark developed by NASA for high-end computer systems. This test profile currently uses the MPI version of NPB. This test profile offers selecting the different NPB tests/problems and varying problem sizes. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: FT.CEPYC 7713 2PEPYC 771320K40K60K80K100KSE +/- 650.27, N = 8SE +/- 292.35, N = 6116679.2660659.251. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.2
OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: FT.CEPYC 7713 2PEPYC 7713100200300400500457.47412.62
MinAvgMaxEPYC 771337.9147.0225.1EPYC 7713 2P80.0255.1456.5OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 1740 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 2200 Joules per run.

Basis Universal

Basis Universal is a GPU texture codec. This test times how long it takes to convert sRGB PNGs into Basis Univeral assets with various settings. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterBasis Universal 1.13Settings: UASTC Level 0EPYC 7713EPYC 7713 2P246810SE +/- 0.006, N = 7SE +/- 0.015, N = 66.2216.3791. (CXX) g++ options: -std=c++11 -fvisibility=hidden -fPIC -fno-strict-aliasing -O3 -rdynamic -lm -lpthread
MinAvgMaxEPYC 771337.366.3122.5EPYC 7713 2P80.6133.6224.3OpenBenchmarking.orgWatts, Fewer Is BetterBasis Universal 1.13CPU Power Consumption Monitor601201802403001. EPYC 7713: Approximate power consumption of 691 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 1403 Joules per run.

WebP2 Image Encode

This is a test of Google's libwebp2 library with the WebP2 image encode utility and using a sample 6000x4000 pixel JPEG image as the input, similar to the WebP/libwebp test profile. WebP2 is currently experimental and under heavy development as ultimately the successor to WebP. WebP2 supports 10-bit HDR, more efficienct lossy compression, improved lossless compression, animation support, and full multi-threading support compared to WebP. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMP/s, More Is BetterWebP2 Image Encode 20220823Encode Settings: DefaultEPYC 7713 2PEPYC 77133691215SE +/- 0.21, N = 15SE +/- 0.08, N = 159.709.001. (CXX) g++ options: -msse4.2 -fno-rtti -O3 -ldl
OpenBenchmarking.orgMP/s Per Watt, More Is BetterWebP2 Image Encode 20220823Encode Settings: DefaultEPYC 7713EPYC 7713 2P0.02390.04780.07170.09560.11950.1060.068
MinAvgMaxEPYC 771337.285.3200.9EPYC 7713 2P79.7143.6291.2OpenBenchmarking.orgWatts, Fewer Is BetterWebP2 Image Encode 20220823CPU Power Consumption Monitor701402102803501. EPYC 7713: Approximate power consumption of 586 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 957 Joules per run.

GNU Octave Benchmark

This test profile measures how long it takes to complete several reference GNU Octave files via octave-benchmark. GNU Octave is used for numerical computations and is an open-source alternative to MATLAB. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterGNU Octave Benchmark 6.4.0EPYC 7713 2PEPYC 7713246810SE +/- 0.021, N = 6SE +/- 0.020, N = 66.5456.582
MinAvgMaxEPYC 771337.273.6119.2OpenBenchmarking.orgWatts, Fewer Is BetterGNU Octave Benchmark 6.4.0CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 785 Joules per run.

DaCapo Benchmark

This test runs the DaCapo Benchmarks written in Java and intended to test system/CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgmsec, Fewer Is BetterDaCapo Benchmark 9.12-MR1Java Test: JythonEPYC 7713EPYC 7713 2P9001800270036004500SE +/- 15.40, N = 7SE +/- 23.41, N = 740334106
MinAvgMaxEPYC 771337.367.4119.8EPYC 7713 2P80.5136.9224.9OpenBenchmarking.orgWatts, Fewer Is BetterDaCapo Benchmark 9.12-MR1CPU Power Consumption Monitor601201802403001. EPYC 7713: Approximate power consumption of 636 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 1330 Joules per run.

SVT-HEVC

This is a test of the Intel Open Visual Cloud Scalable Video Technology SVT-HEVC CPU-based multi-threaded video encoder for the HEVC / H.265 video format with a sample YUV video file. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-HEVC 1.5.0Tuning: 7 - Input: Bosphorus 4KEPYC 7713 2PEPYC 7713306090120150SE +/- 0.72, N = 6SE +/- 0.23, N = 7144.70137.741. (CC) gcc options: -fPIE -fPIC -O3 -O2 -pie -rdynamic -lpthread -lrt
OpenBenchmarking.orgFrames Per Second Per Watt, More Is BetterSVT-HEVC 1.5.0Tuning: 7 - Input: Bosphorus 4KEPYC 7713EPYC 7713 2P0.24140.48280.72420.96561.2071.0730.644
MinAvgMaxEPYC 771337.7128.4228.5EPYC 7713 2P80.0224.7429.7OpenBenchmarking.orgWatts, Fewer Is BetterSVT-HEVC 1.5.0CPU Power Consumption Monitor1102203304405501. EPYC 7713: Approximate power consumption of 1210 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 2397 Joules per run.

libavif avifenc

This is a test of the AOMedia libavif library testing the encoding of a JPEG image to AV1 Image Format (AVIF). Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is Betterlibavif avifenc 0.10Encoder Speed: 10, LosslessEPYC 7713EPYC 7713 2P1.25062.50123.75185.00246.253SE +/- 0.011, N = 7SE +/- 0.021, N = 75.3985.5581. (CXX) g++ options: -O3 -fPIC -lm
MinAvgMaxEPYC 771337.272.2150.5EPYC 7713 2P79.6137.9232.7OpenBenchmarking.orgWatts, Fewer Is Betterlibavif avifenc 0.10CPU Power Consumption Monitor601201802403001. EPYC 7713: Approximate power consumption of 670 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 1320 Joules per run.

ASTC Encoder

ASTC Encoder (astcenc) is for the Adaptive Scalable Texture Compression (ASTC) format commonly used with OpenGL, OpenGL ES, and Vulkan graphics APIs. This test profile does a coding test of both compression/decompression. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMT/s, More Is BetterASTC Encoder 4.0Preset: MediumEPYC 7713 2PEPYC 771380160240320400SE +/- 1.30, N = 7SE +/- 0.11, N = 7380.65236.601. (CXX) g++ options: -O3 -flto -pthread
OpenBenchmarking.orgMT/s Per Watt, More Is BetterASTC Encoder 4.0Preset: MediumEPYC 7713EPYC 7713 2P0.55891.11781.67672.23562.79452.4842.209
MinAvgMaxEPYC 771337.295.2229.4EPYC 7713 2P80.5172.4441.3OpenBenchmarking.orgWatts, Fewer Is BetterASTC Encoder 4.0CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 925 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 1551 Joules per run.

TNN

TNN is an open-source deep learning reasoning framework developed by Tencent. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterTNN 0.3Target: CPU - Model: SqueezeNet v2EPYC 7713EPYC 7713 2P1530456075SE +/- 0.23, N = 8SE +/- 0.18, N = 865.6265.89MIN: 64.64 / MAX: 71.42MIN: 65.21 / MAX: 67.081. (CXX) g++ options: -fopenmp -pthread -fvisibility=hidden -fvisibility=default -O3 -rdynamic -ldl
MinAvgMaxEPYC 771337.158.475.7EPYC 7713 2P80.0119.3154.0OpenBenchmarking.orgWatts, Fewer Is BetterTNN 0.3CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 504 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 1014 Joules per run.

NAS Parallel Benchmarks

NPB, NAS Parallel Benchmarks, is a benchmark developed by NASA for high-end computer systems. This test profile currently uses the MPI version of NPB. This test profile offers selecting the different NPB tests/problems and varying problem sizes. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: EP.CEPYC 7713 2PEPYC 77132K4K6K8K10KSE +/- 61.53, N = 15SE +/- 45.43, N = 158338.274406.021. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.2
OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: EP.CEPYC 7713 2PEPYC 7713112233445548.8044.17
MinAvgMaxEPYC 771337.599.7230.3EPYC 7713 2P80.3170.9461.2OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 645 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 1025 Joules per run.

Etcpak

Etcpack is the self-proclaimed "fastest ETC compressor on the planet" with focused on providing open-source, very fast ETC and S3 texture compression support. The test profile uses a 8K x 8K game texture as a sample input. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMpx/s, More Is BetterEtcpak 1.0Benchmark: Multi-Threaded - Configuration: ETC2EPYC 7713EPYC 7713 2P14002800420056007000SE +/- 15.08, N = 9SE +/- 36.45, N = 86702.526678.571. (CXX) g++ options: -O3 -march=native -std=c++11 -lpthread
OpenBenchmarking.orgMpx/s Per Watt, More Is BetterEtcpak 1.0Benchmark: Multi-Threaded - Configuration: ETC2EPYC 7713EPYC 7713 2P153045607569.3137.95
MinAvgMaxEPYC 771337.296.7198.7EPYC 7713 2P80.5176.0322.1OpenBenchmarking.orgWatts, Fewer Is BetterEtcpak 1.0CPU Power Consumption Monitor801602403204001. EPYC 7713: Approximate power consumption of 741 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 1408 Joules per run.

libavif avifenc

This is a test of the AOMedia libavif library testing the encoding of a JPEG image to AV1 Image Format (AVIF). Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is Betterlibavif avifenc 0.10Encoder Speed: 6EPYC 7713EPYC 7713 2P0.89031.78062.67093.56124.4515SE +/- 0.024, N = 8SE +/- 0.022, N = 83.8463.9571. (CXX) g++ options: -O3 -fPIC -lm
MinAvgMaxEPYC 771337.3100.8230.3EPYC 7713 2P79.7163.7311.9OpenBenchmarking.orgWatts, Fewer Is Betterlibavif avifenc 0.10CPU Power Consumption Monitor801602403204001. EPYC 7713: Approximate power consumption of 794 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 1310 Joules per run.

NAS Parallel Benchmarks

NPB, NAS Parallel Benchmarks, is a benchmark developed by NASA for high-end computer systems. This test profile currently uses the MPI version of NPB. This test profile offers selecting the different NPB tests/problems and varying problem sizes. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: MG.CEPYC 7713 2PEPYC 771320K40K60K80K100KSE +/- 291.87, N = 10SE +/- 290.32, N = 9100740.8957329.701. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.2
OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: MG.CEPYC 7713 2PEPYC 7713110220330440550511.45503.96
MinAvgMaxEPYC 771337.6113.8220.8EPYC 7713 2P80.9197.0457.5OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor1202403604806001. EPYC 7713: Approximate power consumption of 885 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 1320 Joules per run.

WebP2 Image Encode

This is a test of Google's libwebp2 library with the WebP2 image encode utility and using a sample 6000x4000 pixel JPEG image as the input, similar to the WebP/libwebp test profile. WebP2 is currently experimental and under heavy development as ultimately the successor to WebP. WebP2 supports 10-bit HDR, more efficienct lossy compression, improved lossless compression, animation support, and full multi-threading support compared to WebP. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMP/s, More Is BetterWebP2 Image Encode 20220823Encode Settings: Quality 100, Compression Effort 5EPYC 7713EPYC 7713 2P3691215SE +/- 0.04, N = 10SE +/- 0.03, N = 810.856.411. (CXX) g++ options: -msse4.2 -fno-rtti -O3 -ldl
OpenBenchmarking.orgMP/s Per Watt, More Is BetterWebP2 Image Encode 20220823Encode Settings: Quality 100, Compression Effort 5EPYC 7713EPYC 7713 2P0.0250.050.0750.10.1250.1110.033
MinAvgMaxEPYC 771337.597.8228.0EPYC 7713 2P80.0192.5410.0OpenBenchmarking.orgWatts, Fewer Is BetterWebP2 Image Encode 20220823CPU Power Consumption Monitor1102203304405501. EPYC 7713: Approximate power consumption of 626 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 1564 Joules per run.

WebP Image Encode

This is a test of Google's libwebp with the cwebp image encode utility and using a sample 6000x4000 pixel JPEG image as the input. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMP/s, More Is BetterWebP Image Encode 1.2.4Encode Settings: Quality 100EPYC 7713EPYC 7713 2P3691215SE +/- 0.01, N = 10SE +/- 0.00, N = 1010.6910.651. (CC) gcc options: -fvisibility=hidden -O2 -lm
OpenBenchmarking.orgMP/s Per Watt, More Is BetterWebP Image Encode 1.2.4Encode Settings: Quality 100EPYC 7713EPYC 7713 2P0.04570.09140.13710.18280.22850.2030.097
MinAvgMaxEPYC 771336.952.775.7EPYC 7713 2P79.2109.5151.1OpenBenchmarking.orgWatts, Fewer Is BetterWebP Image Encode 1.2.4CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 342 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 712 Joules per run.

OpenBenchmarking.orgMP/s, More Is BetterWebP Image Encode 1.2.4Encode Settings: DefaultEPYC 7713EPYC 7713 2P48121620SE +/- 0.01, N = 11SE +/- 0.01, N = 1116.7816.751. (CC) gcc options: -fvisibility=hidden -O2 -lm
OpenBenchmarking.orgMP/s Per Watt, More Is BetterWebP Image Encode 1.2.4Encode Settings: DefaultEPYC 7713EPYC 7713 2P0.07650.1530.22950.3060.38250.3400.162
MinAvgMaxEPYC 771337.149.475.6EPYC 7713 2P79.4103.7149.9OpenBenchmarking.orgWatts, Fewer Is BetterWebP Image Encode 1.2.4CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 283 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 594 Joules per run.

Timed CPython Compilation

This test times how long it takes to build the reference Python implementation, CPython, with optimizations and LTO enabled for a release build. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed CPython Compilation 3.10.6Build Configuration: DefaultEPYC 7713 2PEPYC 77134812162015.3915.71
MinAvgMaxEPYC 771338.081.6208.3EPYC 7713 2P79.4157.2334.3OpenBenchmarking.orgWatts, Fewer Is BetterTimed CPython Compilation 3.10.6CPU Power Consumption Monitor80160240320400

ctx_clock

Ctx_clock is a simple test program to measure the context switch time in clock cycles. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgClocks, Fewer Is Betterctx_clockContext Switch TimeEPYC 7713 2PEPYC 7713306090120150SE +/- 0.00, N = 13SE +/- 0.00, N = 13120120
MinAvgMaxEPYC 771337.245.572.6EPYC 7713 2P80.698.4148.7OpenBenchmarking.orgWatts, Fewer Is Betterctx_clockCPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 224 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 492 Joules per run.

BLAKE2

This is a benchmark of BLAKE2 using the blake2s binary. BLAKE2 is a high-performance crypto alternative to MD5 and SHA-2/3. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgCycles Per Byte, Fewer Is BetterBLAKE2 20170307EPYC 7713 2PEPYC 77130.76281.52562.28843.05123.814SE +/- 0.00, N = 14SE +/- 0.00, N = 143.393.391. (CC) gcc options: -O3 -march=native -lcrypto -lz
MinAvgMaxEPYC 771337.042.962.1EPYC 7713 2P79.591.3129.4OpenBenchmarking.orgWatts, Fewer Is BetterBLAKE2 20170307CPU Power Consumption Monitor40801201602001. EPYC 7713: Approximate power consumption of 193 Joules per run.2. EPYC 7713 2P: Approximate power consumption of 411 Joules per run.

CPU Power Consumption Monitor

OpenBenchmarking.orgWattsCPU Power Consumption MonitorPhoronix Test Suite System MonitoringEPYC 77134080120160200Min: 18.73 / Avg: 175.52 / Max: 233.05

Meta Performance Per Watts

OpenBenchmarking.orgPerformance Per Watts, More Is BetterMeta Performance Per WattsPerformance Per WattsEPYC 771360012001800240030002802.51

308 Results Shown

WRF
OpenFOAM:
  drivaerFastback, Large Mesh Size - Execution Time
  drivaerFastback, Large Mesh Size - Mesh Time
SPECjbb 2015:
  SPECjbb2015-Composite critical-jOPS
  SPECjbb2015-Composite max-jOPS
MariaDB:
  4096
  2048
NWChem
Renaissance
RELION
BRL-CAD
Xcompact3d Incompact3d
Rodinia
Stockfish
LeelaChessZero:
  Eigen
  BLAS
Renaissance
Quantum ESPRESSO
oneDNN
PostgreSQL pgbench:
  100 - 250 - Read Write - Average Latency
  100 - 250 - Read Write
asmFish
PostgreSQL pgbench:
  100 - 500 - Read Write - Average Latency
  100 - 500 - Read Write
Renaissance
Graph500:
  26:
    sssp max_TEPS
    sssp median_TEPS
    bfs max_TEPS
    bfs median_TEPS
ONNX Runtime:
  ArcFace ResNet-100 - CPU - Standard
  fcn-resnet101-11 - CPU - Standard
  yolov4 - CPU - Standard
  super-resolution-10 - CPU - Standard
JPEG XL libjxl
SecureMark
High Performance Conjugate Gradient
LuaRadio:
  Complex Phase
  Hilbert Transform
  FM Deemphasis Filter
  Five Back to Back FIR Filters
Blender
oneDNN
Mlpack Benchmark
Timed Linux Kernel Compilation
TNN
OpenFOAM:
  drivaerFastback, Small Mesh Size - Execution Time
  drivaerFastback, Small Mesh Size - Mesh Time
Timed LLVM Compilation
Numpy Benchmark
LuxCoreRender:
  Danish Mood - CPU
  LuxCore Benchmark - CPU
LZ4 Compression:
  3 - Decompression Speed
  3 - Compression Speed
OpenSSL
Timed Gem5 Compilation
Appleseed
WebP2 Image Encode
OSPRay Studio
Timed Node.js Compilation
PostgreSQL pgbench:
  100 - 500 - Read Only - Average Latency
  100 - 500 - Read Only
  100 - 250 - Read Only - Average Latency
  100 - 250 - Read Only
OSPRay Studio
Ngspice
Timed LLVM Compilation
VP9 libvpx Encoding
LuxCoreRender
Apache Cassandra
ClickHouse:
  100M Rows Web Analytics Dataset, Third Run
  100M Rows Web Analytics Dataset, Second Run
  100M Rows Web Analytics Dataset, First Run / Cold Cache
OSPRay Studio:
  3 - 4K - 1 - Path Tracer
  2 - 4K - 32 - Path Tracer
ONNX Runtime:
  GPT-2 - CPU - Standard
  bertsquad-12 - CPU - Standard
OSPRay Studio:
  1 - 4K - 32 - Path Tracer
  2 - 4K - 1 - Path Tracer
  2 - 4K - 16 - Path Tracer
  1 - 4K - 1 - Path Tracer
  1 - 4K - 16 - Path Tracer
Renaissance
NAS Parallel Benchmarks
Renaissance:
  In-Memory Database Shootout
  Finagle HTTP Requests
Ngspice
etcd:
  RANGE - 100 - 100 - Average Latency
  RANGE - 100 - 100
oneDNN
JPEG XL libjxl
Appleseed
OpenVINO:
  Face Detection FP16 - CPU:
    ms
    FPS
simdjson:
  DistinctUserID
  TopTweet
Apache HTTP Server
Helsing
simdjson
Apache HTTP Server
nginx:
  500
  1000
Sysbench
Timed CPython Compilation
etcd:
  PUT - 100 - 100 - Average Latency
  PUT - 100 - 100
oneDNN:
  Recurrent Neural Network Training - f32 - CPU
  Recurrent Neural Network Training - u8s8f32 - CPU
etcd:
  RANGE - 500 - 100 - Average Latency
  RANGE - 500 - 100
  PUT - 500 - 100 - Average Latency
  PUT - 500 - 100
  PUT - 100 - 1000 - Average Latency
  PUT - 100 - 1000
  RANGE - 100 - 1000 - Average Latency
  RANGE - 100 - 1000
ebizzy
Node.js V8 Web Tooling Benchmark
oneDNN
etcd:
  PUT - 500 - 1000 - Average Latency
  PUT - 500 - 1000
  RANGE - 500 - 1000 - Average Latency
  RANGE - 500 - 1000
Blender
OpenVINO:
  Person Detection FP16 - CPU:
    ms
    FPS
LZ4 Compression:
  9 - Decompression Speed
  9 - Compression Speed
OpenVINO:
  Person Detection FP32 - CPU:
    ms
    FPS
Dragonflydb:
  50 - 1:5
  50 - 1:1
  50 - 5:1
OpenVINO:
  Face Detection FP16-INT8 - CPU:
    ms
    FPS
PyPerformance
PJSIP:
  INVITE
  OPTIONS, Stateful
OpenVINO:
  Machine Translation EN To DE FP16 - CPU:
    ms
    FPS
  Person Vehicle Bike Detection FP16 - CPU:
    ms
    FPS
LuxCoreRender
OpenVINO:
  Vehicle Detection FP16-INT8 - CPU:
    ms
    FPS
  Vehicle Detection FP16 - CPU:
    ms
    FPS
  Age Gender Recognition Retail 0013 FP16-INT8 - CPU:
    ms
    FPS
  Weld Porosity Detection FP16-INT8 - CPU:
    ms
    FPS
  Age Gender Recognition Retail 0013 FP16 - CPU:
    ms
    FPS
  Weld Porosity Detection FP16 - CPU:
    ms
    FPS
Facebook RocksDB:
  Update Rand
  Read Rand Write Rand
  Read While Writing
GraphicsMagick
OpenSSL:
  RSA4096:
    verify/s
    sign/s
GraphicsMagick
Facebook RocksDB
GraphicsMagick:
  Rotate
  HWB Color Space
Etcpak
Coremark
Blender
GPAW
CloverLeaf
simdjson
Build2
AOM AV1
Natron
Rodinia
SVT-HEVC
Timed Wasmer Compilation
simdjson
srsRAN:
  4G PHY_DL_Test 100 PRB SISO 64-QAM:
    UE Mb/s
    eNb Mb/s
miniFE
LuxCoreRender
Mlpack Benchmark
ASKAP:
  tConvolve MPI - Gridding
  tConvolve MPI - Degridding
WebP2 Image Encode
Zstd Compression:
  19 - Decompression Speed
  19 - Compression Speed
  19, Long Mode - Decompression Speed
  19, Long Mode - Compression Speed
Timed Linux Kernel Compilation
7-Zip Compression:
  Decompression Rating
  Compression Rating
Kripke
JPEG XL Decoding libjxl
WebP Image Encode
Timed Godot Game Engine Compilation
Primesieve
srsRAN:
  4G PHY_DL_Test 100 PRB MIMO 256-QAM:
    UE Mb/s
    eNb Mb/s
libavif avifenc
PyPerformance
GROMACS
Timed PHP Compilation
Redis
Algebraic Multi-Grid Benchmark
Appleseed
Kvazaar
Rodinia
srsRAN:
  OFDM_Test
  4G PHY_DL_Test 100 PRB MIMO 64-QAM
  4G PHY_DL_Test 100 PRB MIMO 64-QAM
Embree:
  Pathtracer - Asian Dragon
  Pathtracer ISPC - Asian Dragon
oneDNN
PyPerformance
PJSIP
LibRaw
Blender
NAS Parallel Benchmarks
Renaissance
Aircrack-ng
FLAC Audio Encoding
x265
Google SynthMark
QuantLib
Xmrig
NAMD
Pennant
PHPBench
JPEG XL libjxl
Xmrig
Mlpack Benchmark
DaCapo Benchmark
Blender
LULESH
PyPerformance
TNN
Cython Benchmark
NAS Parallel Benchmarks
PyBench
Node.js Express HTTP Load Test
srsRAN:
  5G PHY_DL_NR Test 52 PRB SISO 64-QAM:
    UE Mb/s
    eNb Mb/s
Intel Open Image Denoise
NAS Parallel Benchmarks
SVT-HEVC
Intel Open Image Denoise
Timed Apache Compilation
ASKAP
Liquid-DSP
Kvazaar
Liquid-DSP:
  128 - 256 - 57
  64 - 256 - 57
  32 - 256 - 57
NAS Parallel Benchmarks
srsRAN:
  4G PHY_DL_Test 100 PRB SISO 256-QAM:
    UE Mb/s
    eNb Mb/s
Timed Mesa Compilation
TNN
Timed FFmpeg Compilation
JPEG XL Decoding libjxl
Basis Universal
Sysbench
NAS Parallel Benchmarks
SVT-AV1
AOM AV1
SVT-AV1
ASTC Encoder
WebP Image Encode
Embree
JPEG XL libjxl
POV-Ray
ACES DGEMM
Basis Universal
Embree
ASTC Encoder
LAME MP3 Encoding
Google Draco
NAS Parallel Benchmarks
oneDNN
Pennant
m-queens
libavif avifenc
Google Draco
WebP Image Encode
Rodinia
SVT-AV1
NAS Parallel Benchmarks
Basis Universal
WebP2 Image Encode
GNU Octave Benchmark
DaCapo Benchmark
SVT-HEVC
libavif avifenc
ASTC Encoder
TNN
NAS Parallel Benchmarks
Etcpak
libavif avifenc
NAS Parallel Benchmarks
WebP2 Image Encode
WebP Image Encode:
  Quality 100
  Default
Timed CPython Compilation
ctx_clock
BLAKE2
CPU Power Consumption Monitor:
  Phoronix Test Suite System Monitoring
  Performance Per Watts

EPYC 7713 2P

Processor: 2 x AMD EPYC 7713 64-Core @ 2.00GHz (128 Cores / 256 Threads), Motherboard: AMD DAYTONA_X (RYM1009B BIOS), Chipset: AMD Starship/Matisse, Memory: 512GB, Disk: 3841GB Micron_9300_MTFDHAL3T8TDP, Graphics: ASPEED, Monitor: VE228, Network: 2 x Mellanox MT27710

OS: Ubuntu 22.04, Kernel: 5.15.0-47-generic (x86_64), Desktop: GNOME Shell 42.4, Display Server: X Server 1.21.1.3, Vulkan: 1.2.204, Compiler: GCC 11.2.0, File-System: ext4, Screen Resolution: 1920x1080

Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-11-gBFGDP/gcc-11-11.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-gBFGDP/gcc-11-11.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-build-config=bootstrap-lto-lean --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v
Processor Notes: Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xa001173
Java Notes: OpenJDK Runtime Environment (build 11.0.16+8-post-Ubuntu-0ubuntu122.04)
Python Notes: Python 3.10.4
Security Notes: itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl and seccomp + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional IBRS_FW STIBP: always-on RSB filling + srbds: Not affected + tsx_async_abort: Not affected

Testing initiated at 8 September 2022 21:06 by user phoronix.

EPYC 7713

Processor: AMD EPYC 7713 64-Core @ 2.00GHz (64 Cores / 128 Threads), Motherboard: AMD DAYTONA_X (RYM1009B BIOS), Chipset: AMD Starship/Matisse, Memory: 256GB, Disk: 3841GB Micron_9300_MTFDHAL3T8TDP, Graphics: ASPEED, Monitor: VE228, Network: 2 x Mellanox MT27710

OS: Ubuntu 22.04, Kernel: 5.15.0-47-generic (x86_64), Desktop: GNOME Shell 42.4, Display Server: X Server 1.21.1.3, Vulkan: 1.2.204, Compiler: GCC 11.2.0, File-System: ext4, Screen Resolution: 1920x1080

Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-11-gBFGDP/gcc-11-11.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-gBFGDP/gcc-11-11.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-build-config=bootstrap-lto-lean --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v
Processor Notes: Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xa001173
Java Notes: OpenJDK Runtime Environment (build 11.0.16+8-post-Ubuntu-0ubuntu122.04)
Python Notes: Python 3.10.4
Security Notes: itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl and seccomp + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional IBRS_FW STIBP: always-on RSB filling + srbds: Not affected + tsx_async_abort: Not affected

Testing initiated at 11 September 2022 20:52 by user phoronix.