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 Benchmarksdragonflydb: 50 - 1:5dragonflydb: 50 - 1:1brl-cad: VGR Performance Metricdragonflydb: 50 - 5:1pgbench: 100 - 250 - Read Write - Average Latencypgbench: 100 - 500 - Read Write - Average Latencypgbench: 100 - 500 - Read Writemysqlslap: 2048natron: Spaceshiponednn: Recurrent Neural Network Training - f32 - CPUonednn: Recurrent Neural Network Training - u8s8f32 - CPUonednn: Recurrent Neural Network Training - bf16bf16bf16 - CPUopenfoam: drivaerFastback, Small Mesh Size - Execution Timenpb: SP.Conednn: Recurrent Neural Network Inference - f32 - CPUappleseed: Material Testeronednn: Recurrent Neural Network Inference - u8s8f32 - CPUetcd: RANGE - 500 - 100 - Average Latencyetcd: PUT - 500 - 100 - Average Latencyetcd: RANGE - 500 - 100etcd: RANGE - 100 - 100openfoam: drivaerFastback, Large Mesh Size - Execution Timeetcd: PUT - 500 - 100etcd: RANGE - 100 - 100 - Average Latencyetcd: PUT - 100 - 100incompact3d: X3D-benchmarking input.i3dpennant: sedovbigcoremark: CoreMark Size 666 - Iterations Per Secondetcd: PUT - 100 - 100 - Average Latencyaskap: tConvolve MPI - Griddingopenvino: Age Gender Recognition Retail 0013 FP16-INT8 - CPUrocksdb: Rand Readopenssl: RSA4096openssl: RSA4096sysbench: CPUopenssl: SHA256astcenc: Exhaustiveaskap: tConvolve MPI - Degriddingopenvino: Face Detection FP16 - CPUwrf: conus 2.5kmhelsing: 14 digithpcg: relion: Basic - CPUliquid-dsp: 256 - 256 - 57nginx: 500primesieve: 1e13npb: FT.Copenvino: Person Detection FP32 - CPUetcd: RANGE - 500 - 1000openvino: Person Detection FP16 - CPUetcd: PUT - 500 - 1000astcenc: Thoroughm-queens: Time To Solveetcd: PUT - 500 - 1000 - Average Latencyetcd: RANGE - 500 - 1000 - Average Latencynpb: LU.Copenvino: Vehicle Detection FP16 - CPUetcd: PUT - 100 - 1000 - Average Latencyetcd: PUT - 100 - 1000ospray-studio: 1 - 4K - 32 - Path Traceramg: ospray-studio: 3 - 4K - 32 - Path Traceretcd: RANGE - 100 - 1000ospray-studio: 2 - 4K - 32 - Path Tracernpb: CG.Cnpb: EP.Clulesh: etcd: RANGE - 100 - 1000 - Average Latencyblender: Classroom - CPU-Onlykripke: openvino: Person Vehicle Bike Detection FP16 - CPUliquid-dsp: 128 - 256 - 57openvino: Machine Translation EN To DE FP16 - CPUopenvino: Face Detection FP16-INT8 - CPUopenvino: Vehicle Detection FP16-INT8 - CPUnginx: 1000openvino: Weld Porosity Detection FP16-INT8 - CPUopenvino: Weld Porosity Detection FP16 - CPUnpb: BT.Cebizzy: asmfish: 1024 Hash Memory, 26 Depthcompress-7zip: Decompression Ratingblender: Pabellon Barcelona - CPU-Onlyospray-studio: 1 - 4K - 1 - Path Tracerospray-studio: 1 - 4K - 16 - Path Tracerospray-studio: 3 - 4K - 16 - Path Tracerospray-studio: 3 - 4K - 1 - Path Tracerxmrig: Monero - 1Mnpb: IS.Dblender: BMW27 - CPU-Onlymysqlslap: 4096ospray-studio: 2 - 4K - 16 - Path Tracernpb: MG.Connx: ArcFace ResNet-100 - CPU - Standardospray-studio: 2 - 4K - 1 - Path Tracerblender: Fishy Cat - CPU-Onlyblender: Barbershop - CPU-Onlyembree: Pathtracer ISPC - Crownrodinia: OpenMP LavaMDembree: Pathtracer - Crownnamd: ATPase Simulation - 327,506 Atomsbuild-linux-kernel: allmodconfigwebp2: Quality 100, Compression Effort 5gpaw: Carbon Nanotubeaskap: Hogbom Clean OpenMPoidn: RT.hdr_alb_nrm.3840x2160openvino: Age Gender Recognition Retail 0013 FP16 - CPUastcenc: Mediumoidn: RT.ldr_alb_nrm.3840x2160gromacs: MPI CPU - water_GMX50_barerocksdb: Read While Writingnpb: SP.Brenaissance: Finagle HTTP Requestsmt-dgemm: Sustained Floating-Point Ratepgbench: 100 - 250 - Read Only - Average Latencyrenaissance: Savina Reactors.IOpgbench: 100 - 250 - Read Onlygraphics-magick: Sharpensvt-hevc: 1 - Bosphorus 4Kbuild-llvm: Ninjaluxcorerender: Orange Juice - CPUkvazaar: Bosphorus 4K - Very Fastpgbench: 100 - 500 - Read Only - Average Latencycompress-7zip: Compression Ratingluxcorerender: DLSC - CPUpgbench: 100 - 500 - Read Onlyonednn: Convolution Batch Shapes Auto - f32 - CPUredis: GET - 1000onnx: super-resolution-10 - CPU - Standardbuild-nodejs: Time To Compilegraphics-magick: Enhancedpovray: Trace Timesysbench: RAM / Memorybuild-linux-kernel: defconfigbasis: UASTC Level 3compress-lz4: 3 - Decompression Speedrenaissance: ALS Movie Lenscompress-lz4: 9 - Decompression Speedrenaissance: Apache Spark PageRankwebp2: Quality 95, Compression Effort 7apache: 1000jpegxl-decode: Allbuild-ffmpeg: Time To Compileonnx: yolov4 - CPU - Standardapache: 500x265: Bosphorus 4Kspec-jbb2015: SPECjbb2015-Composite critical-jOPSliquid-dsp: 64 - 256 - 57renaissance: In-Memory Database Shootoutbuild-llvm: Unix Makefilesopenvino: Age Gender Recognition Retail 0013 FP16 - CPUrocksdb: Read Rand Write Randappleseed: Disney Materialbasis: UASTC Level 2luxcorerender: Danish Mood - CPUgraph500: 26cassandra: Writesgraphics-magick: HWB Color Spaceonnx: GPT-2 - CPU - Standardgraph500: 26compress-zstd: 19, Long Mode - Compression Speedembree: Pathtracer ISPC - Asian Dragonaom-av1: Speed 9 Realtime - Bosphorus 4Krocksdb: Update Randopenvino: Age Gender Recognition Retail 0013 FP16-INT8 - CPUxmrig: Wownero - 1Mwebp2: Quality 75, Compression Effort 7openfoam: drivaerFastback, Large Mesh Size - Mesh Timesvt-hevc: 10 - Bosphorus 4Klczero: BLASdacapobench: Tradebeansnwchem: C240 Buckyballappleseed: Emilybuild-mesa: Time To Compilelczero: Eigenminife: Smallaom-av1: Speed 10 Realtime - Bosphorus 4Kopenfoam: drivaerFastback, Small Mesh Size - Mesh Timebuild-gem5: Time To Compilebuild2: Time To Compileembree: Pathtracer - Asian Dragonmlpack: scikit_qdarodinia: OpenMP Leukocyterenaissance: Apache Spark Bayesrodinia: OpenMP CFD Solveropenvino: Weld Porosity Detection FP16 - CPUopenvino: Weld Porosity Detection FP16-INT8 - CPUopenvino: Vehicle Detection FP16-INT8 - CPUopenvino: Face Detection FP16-INT8 - CPUonnx: bertsquad-12 - CPU - Standardopenvino: Machine Translation EN To DE FP16 - CPUopenvino: Person Vehicle Bike Detection FP16 - CPUsvt-av1: Preset 10 - Bosphorus 4Kbuild-godot: Time To Compilesvt-hevc: 7 - Bosphorus 4Ksrsran: OFDM_Testopenvino: Vehicle Detection FP16 - CPUopenvino: Person Detection FP16 - CPUlibraw: Post-Processing Benchmarkjpegxl: JPEG - 7openvino: Person Detection FP32 - CPUbuild-php: Time To Compilekvazaar: Bosphorus 4K - Ultra Fastjpegxl: PNG - 7pjsip: OPTIONS, Statefulrenaissance: Genetic Algorithm Using Jenetics + Futuresgraph500: 26avifenc: 10, Losslesssvt-av1: Preset 8 - Bosphorus 4Kavifenc: 6node-express-loadtest: graph500: 26compress-lz4: 9 - Compression Speedtnn: CPU - MobileNet v2basis: UASTC Level 0mlpack: scikit_icangspice: C7552compress-lz4: 3 - Compression Speedspec-jbb2015: SPECjbb2015-Composite max-jOPSgraphics-magick: Rotatedraco: Lioncompress-zstd: 19 - Decompression Speedbuild-apache: Time To Compileavifenc: 6, Losslessbuild-python: Defaultjpegxl: PNG - 8luaradio: Five Back to Back FIR Filtersdacapobench: Jythondraco: Church Facadeclickhouse: 100M Rows Web Analytics Dataset, Second Runpjsip: OPTIONS, Statelesswebp: Quality 100, Losslesstnn: CPU - DenseNetsrsran: 4G PHY_DL_Test 100 PRB MIMO 256-QAMopenvino: Face Detection FP16 - CPUjpegxl: JPEG - 8avifenc: 2numpy: jpegxl-decode: 1phpbench: PHP Benchmark Suitengspice: C2670node-web-tooling: srsran: 4G PHY_DL_Test 100 PRB SISO 64-QAMpyperformance: python_startupluaradio: Complex Phasesrsran: 5G PHY_DL_NR Test 52 PRB SISO 64-QAMsecuremark: SecureMark-TLScompress-zstd: 19, Long Mode - Decompression Speedpyperformance: chaossrsran: 4G PHY_DL_Test 100 PRB SISO 256-QAMpyperformance: regex_compilepybench: Total For Average Test Timesqe: AUSURF112octave-benchmark: mlpack: scikit_svmliquid-dsp: 32 - 256 - 57simdjson: PartialTweetspyperformance: pickle_pure_pythonquantlib: clickhouse: 100M Rows Web Analytics Dataset, Third Runsimdjson: DistinctUserIDtnn: CPU - SqueezeNet v2webp: Quality 100etcpak: Multi-Threaded - ETC2etcpak: Single-Threaded - ETC2srsran: 5G PHY_DL_NR Test 52 PRB SISO 64-QAMcython-bench: N-Queensluaradio: FM Deemphasis Filterwebp: Quality 100, Highest Compressionsrsran: 4G PHY_DL_Test 100 PRB MIMO 256-QAMclickhouse: 100M Rows Web Analytics Dataset, First Run / Cold Cacheencode-mp3: WAV To MP3compress-zstd: 19 - Compression Speedluaradio: Hilbert Transformbuild-wasmer: Time To Compilewebp: Defaulttnn: CPU - SqueezeNet v1.1synthmark: VoiceMark_100srsran: 4G PHY_DL_Test 100 PRB MIMO 64-QAMsrsran: 4G PHY_DL_Test 100 PRB MIMO 64-QAMaircrack-ng: pjsip: INVITEencode-flac: WAV To FLACbuild-python: Released Build, PGO + LTO Optimizedctx-clock: Context Switch Timeblake2: srsran: 4G PHY_DL_Test 100 PRB SISO 256-QAMsrsran: 4G PHY_DL_Test 100 PRB SISO 64-QAMsimdjson: TopTweetsimdjson: Kostyasimdjson: LargeRandwebp: Quality 100, Lossless, Highest Compressionstockfish: Total Timepgbench: 100 - 250 - Read Writewebp2: Defaultsvt-av1: Preset 12 - Bosphorus 4Kvpxenc: Speed 5 - Bosphorus 4Kluxcorerender: LuxCore Benchmark - CPUluxcorerender: Rainbow Colors and Prism - CPUonednn: Recurrent Neural Network Inference - bf16bf16bf16 - CPUonednn: Matrix Multiply Batch Shapes Transformer - u8s8f32 - CPUcloverleaf: Lagrangian-Eulerian Hydrodynamicsonnx: fcn-resnet101-11 - CPU - Standardrodinia: OpenMP HotSpot3Dnpb: EP.Dpennant: leblancbigEPYC 7713 2PEPYC 7713724442.87724466.223240515724635.9315.07635.094142611501.97531.127435.347473.91281.7116527.632840.33336.5831192754.452.62.638506.605137230.32737052.6238992.81582.738209.4202300.6911425.6758954105747.1397722.643735.82.114801753321638050.525009.6500125.791346291565276.449939742.017.598650.83962.12937.1011290.961535090000090312.0028.718116679.2613.0944040.713513.1043696.761858.47446.37622.822.6259899.491788.3423.741860.48254462419233066675278542305.61834546045587.108338.2736456.22923.440.891439502672884.445085066667218.1345.843136.1494018.124559.421905.51235808.5445325824887051963408653.8813792215826510164650749.64690.0617.1914022742100740.89877142422.25171.5282.926326.74089.60060.26712160.4636.4143.857319.7562.263.27380.64672.268.21514564673142675.5510535.432.0712270.12612254.2198315577915.08105.03218.6054.240.24851661712.2320127220.6408331364479.134569113.46613447.5907260.6621.58411.40210743.224613.110851.43985.70.3184767.73569.9714.27133091255.7921.486894631915333336136.4178.67537561.07292216750.4642588.7737.503903770002099211093787830233800039.967.173655.6230465457763.8941982.90.55776.38195.62444945682183.6151.53107118.100409324664.857.04124.96159.01553.23268.875931.8447.284638.16.28333.5528.0520.381388.41668292.8222.16123.80641.167144.7013033333335.754777.1537.96100.774774.3738.33858.5210.7488192314.96425160005.55868.7973.957619865946700052.51341.2816.37946.40105.95154.1813089973057353473.020.5217.33415.391.001211.641067525387.01676171.423065.920426.13597.4728.9241.213469.3866.93726003137.36210.50392.37.71623.8127.92495823485.895.8427.4155953399.926.54521.4516142666673.843852808.8396.824.4265.89410.656678.565229.63762.223.312364.33.47141.0377.527.49084.798.451.86916.75273.848748.057394.1133.6149778.797469218.133261.3371203.39149.6143.64.392.910.56279486942166339.70179.55013.867.6316.992840.0530.011619.4423789.2809109.243.5565703599739.573382696.796976983244708.363.4007.980626546156.02943.082950.432984.47633.9351921.711266.20152.3942041253.501.21.282098.417879219.650914972.9781895.96231.378829.5888609.67747011.427232045229.6458961.321943.01.06242074798825904.112613.1252440.57681018098533.265920252.18.9716959.848120.99919.1088563.2712767166667173899.1955.25560659.256.8184602.78036.8283927.655830.455412.23811.911.8135917.43935.5812.479978.190584968101205166710005180131.79588608124085.734406.0219305.65612.477.142707225271535.082706900000116.3324.591689.18174524.792462.401030.76127998.6124649413877610235460896.3024633956847134292228612.42648.9630.432474005857329.701538249638.85299.3047.558346.54751.83340.45457272.60910.8571.658520.8401.402.03236.60201.415.130918216789950.766720.820.8802710.1938004.912976845119.91159.57512.4836.710.3653510778.3313709350.9347061990067.336631164.00194110.6809990.1229.40915.38514123.318814.114156.73067.30.24109150.30731.8818.146417115006.9926.698538925776666675009.2218.44530742.17356184161.26563710.6156.203235930002505961303936825447700047.456.809865.4935833649691.0336467.20.48888.82223.66389339982491.1133.0434720.610359821875.163.90139.89176.43058.95662.981429.1943.392585.66.84531.0225.9718.931293.51713274.5720.82131.27043.354137.7413663333334.174601.7839.40104.584600.9439.76356.5311.1191152243.26226810005.39870.7833.846636764227800053.89332.6116.22145.27103.38755.4312795774656133401.620.1007.18415.7081.021189.040337411392.92666021.403107.102431.83555.7829.2440.766464.5667.62733332136.02010.60396.07.64628.9128.92477473460.396.5430.5156959397.646.58221.3316055666673.863872795.3394.984.4065.61710.696702.518230.43262.423.384365.43.48141.4378.447.47284.998.651.96516.78273.481747.308394.4133.7149666.943469018.129261.3651203.39149.6143.64.392.910.56149383689735279.00184.71115.866.8421.131229.5612.560812.0018588.0194668.966.133060OpenBenchmarking.org

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.

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 7713EPYC 7713 2P700K1400K2100K2800K3500K69769832405151. (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 7713EPYC 7713 2P4K8K12K16K20K3133.8317175.93
MinAvgMaxEPYC 771337.4222.6230.5EPYC 7713 2P78.8188.7340.4OpenBenchmarking.orgWatts, Fewer Is BetterBRL-CAD 7.32.6CPU Power Consumption Monitor80160240320400

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: 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.

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.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

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: 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.

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.

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.

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.

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 7713EPYC 7713 2P140280420560700633.93281.701. (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

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 7713EPYC 7713 2P20K40K60K80K100KSE +/- 33.24, N = 3SE +/- 212.54, N = 451921.71116527.631. (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 7713EPYC 7713 2P70140210280350271.37331.99
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.

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.

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

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.

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.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: RANGE - Connections: 500 - Clients: 100EPYC 7713EPYC 7713 2P20K40K60K80K100KSE +/- 99.62, N = 3SE +/- 133.45, N = 382098.4238506.61

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

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 7713EPYC 7713 2P3K6K9K12K15K14972.977052.621. (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

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.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: 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: PUT - Connections: 100 - Clients: 100EPYC 7713EPYC 7713 2P20K40K60K80K100KSE +/- 356.10, N = 3SE +/- 77.98, N = 378829.5938209.42

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 7713EPYC 7713 2P130260390520650SE +/- 1.64, N = 3SE +/- 0.29, N = 3609.68300.691. (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.

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 7713EPYC 7713 2P3691215SE +/- 0.119128, N = 4SE +/- 0.039464, N = 611.4272305.6758951. (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.

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 7713EPYC 7713 2P900K1800K2700K3600K4500KSE +/- 10186.88, N = 3SE +/- 2413.85, N = 32045229.654105747.141. (CC) gcc options: -O2 -lrt" -lrt
OpenBenchmarking.orgIterations/Sec Per Watt, More Is BetterCoremark 1.0CoreMark Size 666 - Iterations Per SecondEPYC 7713EPYC 7713 2P2K4K6K8K10K9708.0410250.92
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.

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.

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 7713EPYC 7713 2P9K18K27K36K45KSE +/- 76.73, N = 3SE +/- 263.02, N = 321943.043735.81. (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.

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: 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.

Facebook RocksDB

OpenBenchmarking.orgOp/s, More Is BetterFacebook RocksDB 7.5.3Test: Random ReadEPYC 7713EPYC 7713 2P100M200M300M400M500MSE +/- 482617.87, N = 3SE +/- 753688.43, N = 32420747984801753321. (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.

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 7713EPYC 7713 2P400K800K1200K1600K2000KSE +/- 47.96, N = 3SE +/- 338.78, N = 3825904.11638050.51. (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 7713EPYC 7713 2P5K10K15K20K25KSE +/- 1.58, N = 3SE +/- 3.07, N = 312613.125009.61. (CC) gcc options: -pthread -m64 -O3 -lssl -lcrypto -ldl

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 7713EPYC 7713 2P110K220K330K440K550KSE +/- 51.07, N = 3SE +/- 659.19, N = 3252440.57500125.791. (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.

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 7713EPYC 7713 2P30000M60000M90000M120000M150000MSE +/- 67558357.93, N = 3SE +/- 153008615.66, N = 3681018098531346291565271. (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.

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 7713EPYC 7713 2P246810SE +/- 0.0030, N = 3SE +/- 0.0100, N = 43.26596.44991. (CXX) g++ options: -O3 -flto -pthread
OpenBenchmarking.orgMT/s Per Watt, More Is BetterASTC Encoder 4.0Preset: ExhaustiveEPYC 7713EPYC 7713 2P0.00520.01040.01560.02080.0260.0190.023
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.

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 - DegriddingEPYC 7713EPYC 7713 2P9K18K27K36K45KSE +/- 173.09, N = 3SE +/- 448.28, N = 320252.139742.01. (CXX) g++ options: -O3 -fstrict-aliasing -fopenmp

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.orgFPS, More Is BetterOpenVINO 2022.2.devModel: Face Detection FP16 - Device: CPUEPYC 7713EPYC 7713 2P48121620SE +/- 0.01, N = 3SE +/- 0.18, N = 58.9717.591. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared

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 7713EPYC 7713 2P4K8K12K16K20K16959.858650.841. (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

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 7713EPYC 7713 2P306090120150SE +/- 0.16, N = 3SE +/- 0.11, N = 3121.0062.131. (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.

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 7713EPYC 7713 2P918273645SE +/- 0.01, N = 3SE +/- 0.11, N = 319.1137.101. (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.

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 7713EPYC 7713 2P120240360480600SE +/- 3.66, N = 3SE +/- 3.51, N = 4563.27290.961. (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.

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 7713EPYC 7713 2P1100M2200M3300M4400M5500MSE +/- 11518439.32, N = 3SE +/- 1628905.56, N = 3276716666753509000001. (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.

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.

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 7713EPYC 7713 2P1224364860SE +/- 0.05, N = 3SE +/- 0.07, N = 355.2628.721. (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.

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 7713EPYC 7713 2P20K40K60K80K100KSE +/- 292.35, N = 6SE +/- 650.27, N = 860659.25116679.261. (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 7713EPYC 7713 2P100200300400500412.62457.47
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.

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.orgFPS, More Is BetterOpenVINO 2022.2.devModel: Person Detection FP32 - Device: CPUEPYC 7713EPYC 7713 2P3691215SE +/- 0.01, N = 3SE +/- 0.03, N = 36.8113.091. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared

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.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

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.orgFPS, More Is BetterOpenVINO 2022.2.devModel: Person Detection FP16 - Device: CPUEPYC 7713EPYC 7713 2P3691215SE +/- 0.02, N = 3SE +/- 0.05, N = 36.8213.101. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared

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.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

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 7713EPYC 7713 2P1326395265SE +/- 0.02, N = 4SE +/- 0.06, N = 530.4658.471. (CXX) g++ options: -O3 -flto -pthread
OpenBenchmarking.orgMT/s Per Watt, More Is BetterASTC Encoder 4.0Preset: ThoroughEPYC 7713EPYC 7713 2P0.05580.11160.16740.22320.2790.2130.248
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.

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 7713EPYC 7713 2P3691215SE +/- 0.015, N = 4SE +/- 0.027, N = 612.2386.3761. (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.

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.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.

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 7713EPYC 7713 2P60K120K180K240K300KSE +/- 264.51, N = 4SE +/- 2852.04, N = 5135917.43259899.491. (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 7713EPYC 7713 2P2004006008001000767.46816.26
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.

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.orgFPS, More Is BetterOpenVINO 2022.2.devModel: Vehicle Detection FP16 - Device: CPUEPYC 7713EPYC 7713 2P400800120016002000SE +/- 3.92, N = 3SE +/- 12.59, N = 3935.581788.341. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -flto -shared

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: 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

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 7713EPYC 7713 2P20K40K60K80K100KSE +/- 162.94, N = 3SE +/- 224.62, N = 384968446241. (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.

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 7713EPYC 7713 2P400M800M1200M1600M2000MSE +/- 863687.12, N = 3SE +/- 699794.57, N = 3101205166719233066671. (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.

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 7713EPYC 7713 2P20K40K60K80K100KSE +/- 269.96, N = 3SE +/- 265.11, N = 3100051527851. (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.

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.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

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: 2 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path TracerEPYC 7713EPYC 7713 2P20K40K60K80K100KSE +/- 135.08, N = 3SE +/- 39.74, N = 386081454601. (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.

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 7713EPYC 7713 2P10K20K30K40K50KSE +/- 119.51, N = 6SE +/- 340.17, N = 1124085.7345587.101. (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 7713EPYC 7713 2P4080120160200165.19181.81
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.

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: EP.CEPYC 7713EPYC 7713 2P2K4K6K8K10KSE +/- 45.43, N = 15SE +/- 61.53, N = 154406.028338.271. (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 7713EPYC 7713 2P112233445544.1748.80
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.

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 7713EPYC 7713 2P8K16K24K32K40KSE +/- 64.60, N = 3SE +/- 104.62, N = 319305.6636456.231. (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.

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: 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.

Blender

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.3Blend File: Classroom - Compute: CPU-OnlyEPYC 7713EPYC 7713 2P20406080100SE +/- 0.09, N = 3SE +/- 0.23, N = 377.1440.89
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.

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