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.

HTML result view exported from: https://openbenchmarking.org/result/2209132-NE-UBUNTU22004&sor.

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

Ubuntu 22.04 Server Benchmarksgpaw: Carbon Nanotubegromacs: MPI CPU - water_GMX50_barenamd: ATPase Simulation - 327,506 Atomsgraph500: 26graph500: 26graph500: 26graph500: 26hpcg: wrf: conus 2.5kmrelion: Basic - CPUamg: incompact3d: X3D-benchmarking input.i3dkripke: lulesh: pennant: leblancbigpennant: sedovbigminife: Smallmt-dgemm: Sustained Floating-Point Ratenwchem: C240 Buckyballqe: AUSURF112npb: BT.Cnpb: EP.Cnpb: EP.Dnpb: FT.Cnpb: LU.Cnpb: SP.Bnpb: SP.Cnpb: IS.Dnpb: MG.Cnpb: CG.Crodinia: OpenMP CFD Solverrodinia: OpenMP LavaMDrodinia: OpenMP Leukocyterodinia: OpenMP HotSpot3Dopenfoam: drivaerFastback, Small Mesh Size - Mesh Timeopenfoam: drivaerFastback, Small Mesh Size - Execution Timeopenfoam: drivaerFastback, Large Mesh Size - Mesh Timeopenfoam: drivaerFastback, Large Mesh Size - Execution Timeopenvino: Face Detection FP16 - CPUopenvino: Face Detection FP16 - CPUopenvino: Face Detection FP16-INT8 - CPUopenvino: Face Detection FP16-INT8 - CPUopenvino: Age Gender Recognition Retail 0013 FP16 - CPUopenvino: Age Gender Recognition Retail 0013 FP16 - CPUopenvino: Age Gender Recognition Retail 0013 FP16-INT8 - CPUopenvino: Age Gender Recognition Retail 0013 FP16-INT8 - CPUopenvino: Person Detection FP16 - CPUopenvino: Person Detection FP16 - CPUopenvino: Person Detection FP32 - CPUopenvino: Person Detection FP32 - CPUopenvino: Weld Porosity Detection FP16-INT8 - CPUopenvino: Weld Porosity Detection FP16-INT8 - CPUopenvino: Weld Porosity Detection FP16 - CPUopenvino: Weld Porosity Detection FP16 - CPUopenvino: Vehicle Detection FP16-INT8 - CPUopenvino: Vehicle Detection FP16-INT8 - CPUopenvino: Vehicle Detection FP16 - CPUopenvino: Vehicle Detection FP16 - CPUopenvino: Person Vehicle Bike Detection FP16 - CPUopenvino: Person Vehicle Bike Detection FP16 - CPUopenvino: Machine Translation EN To DE FP16 - CPUopenvino: Machine Translation EN To DE FP16 - CPUonnx: yolov4 - CPU - Standardonnx: fcn-resnet101-11 - CPU - Standardonnx: super-resolution-10 - CPU - Standardonnx: bertsquad-12 - CPU - Standardonnx: GPT-2 - CPU - Standardonnx: ArcFace ResNet-100 - CPU - Standardaskap: tConvolve MPI - Degriddingaskap: tConvolve MPI - Griddingaskap: Hogbom Clean OpenMPcloverleaf: Lagrangian-Eulerian Hydrodynamicslczero: BLASlczero: Eigenonednn: Convolution Batch Shapes Auto - f32 - CPUonednn: Matrix Multiply Batch Shapes Transformer - u8s8f32 - CPUonednn: Recurrent Neural Network Training - f32 - CPUonednn: Recurrent Neural Network Training - u8s8f32 - CPUonednn: Recurrent Neural Network Training - bf16bf16bf16 - CPUonednn: Recurrent Neural Network Inference - f32 - CPUonednn: Recurrent Neural Network Inference - u8s8f32 - CPUonednn: Recurrent Neural Network Inference - bf16bf16bf16 - CPUmlpack: scikit_svmmlpack: scikit_qdamlpack: scikit_icatnn: CPU - DenseNettnn: CPU - MobileNet v2tnn: CPU - SqueezeNet v1.1tnn: CPU - SqueezeNet v2build-apache: Time To Compilebuild-ffmpeg: Time To Compilebuild-llvm: Ninjabuild-llvm: Unix Makefilesbuild-gem5: Time To Compilebuild-godot: Time To Compilebuild-linux-kernel: defconfigbuild-linux-kernel: allmodconfigbuild-mesa: Time To Compilebuild-nodejs: Time To Compilebuild-php: Time To Compilebuild-python: Defaultbuild-python: Released Build, PGO + LTO Optimizedbuild-wasmer: Time To Compilebuild2: Time To Compiledacapobench: Jythondacapobench: Tradebeansrenaissance: Savina Reactors.IOrenaissance: Apache Spark Bayesrenaissance: Apache Spark PageRankrenaissance: In-Memory Database Shootoutrenaissance: Finagle HTTP Requestsrenaissance: Genetic Algorithm Using Jenetics + Futuresrenaissance: ALS Movie Lenscompress-7zip: Compression Ratingcompress-7zip: Decompression Ratingcompress-zstd: 19 - Compression Speedcompress-zstd: 19 - Decompression Speedcompress-zstd: 19, Long Mode - Compression Speedcompress-zstd: 19, Long Mode - Decompression Speedcompress-lz4: 3 - Compression Speedcompress-lz4: 3 - Decompression Speedcompress-lz4: 9 - Compression Speedcompress-lz4: 9 - Decompression Speedblender: BMW27 - CPU-Onlyblender: Classroom - CPU-Onlyblender: Fishy Cat - CPU-Onlyblender: Pabellon Barcelona - CPU-Onlyblender: Barbershop - CPU-Onlyappleseed: Emilyappleseed: Disney Materialappleseed: Material Testerpovray: Trace Timeembree: Pathtracer - Asian Dragonembree: Pathtracer - Crownembree: Pathtracer ISPC - Asian Dragonembree: Pathtracer ISPC - Crownoidn: RT.hdr_alb_nrm.3840x2160oidn: RT.ldr_alb_nrm.3840x2160ospray-studio: 1 - 4K - 1 - Path Tracerospray-studio: 1 - 4K - 16 - Path Tracerospray-studio: 1 - 4K - 32 - Path Tracerospray-studio: 2 - 4K - 1 - Path Tracerospray-studio: 2 - 4K - 16 - Path Tracerospray-studio: 2 - 4K - 32 - Path Tracerospray-studio: 3 - 4K - 1 - Path Tracerospray-studio: 3 - 4K - 16 - Path Tracerospray-studio: 3 - 4K - 32 - Path Tracerluxcorerender: DLSC - CPUluxcorerender: Rainbow Colors and Prism - CPUluxcorerender: LuxCore Benchmark - CPUluxcorerender: Orange Juice - CPUluxcorerender: Danish Mood - CPUnatron: Spaceshipaom-av1: Speed 10 Realtime - Bosphorus 4Kaom-av1: Speed 9 Realtime - Bosphorus 4Kvpxenc: Speed 5 - Bosphorus 4Kkvazaar: Bosphorus 4K - Very Fastkvazaar: Bosphorus 4K - Ultra Fastsvt-av1: Preset 12 - Bosphorus 4Ksvt-av1: Preset 10 - Bosphorus 4Ksvt-av1: Preset 8 - Bosphorus 4Ksvt-hevc: 1 - Bosphorus 4Ksvt-hevc: 7 - Bosphorus 4Ksvt-hevc: 10 - Bosphorus 4Kx265: Bosphorus 4Kjpegxl: JPEG - 7jpegxl: JPEG - 8jpegxl: PNG - 7jpegxl: PNG - 8jpegxl-decode: 1jpegxl-decode: Allavifenc: 2avifenc: 6avifenc: 6, Losslessavifenc: 10, Losslesswebp: Defaultwebp: Quality 100webp: Quality 100, Highest Compressionwebp: Quality 100, Losslesswebp: Quality 100, Lossless, Highest Compressionwebp2: Defaultwebp2: Quality 75, Compression Effort 7webp2: Quality 95, Compression Effort 7webp2: Quality 100, Compression Effort 5graphics-magick: HWB Color Spacegraphics-magick: Enhancedgraphics-magick: Rotategraphics-magick: Sharpenlibraw: Post-Processing Benchmarkastcenc: Mediumastcenc: Thoroughastcenc: Exhaustivebasis: UASTC Level 0basis: UASTC Level 2basis: UASTC Level 3draco: Church Facadedraco: Lionetcpak: Single-Threaded - ETC2etcpak: Multi-Threaded - ETC2mysqlslap: 2048mysqlslap: 4096pgbench: 100 - 250 - Read Writepgbench: 100 - 250 - Read Write - Average Latencypgbench: 100 - 250 - Read Onlypgbench: 100 - 250 - Read Only - Average Latencypgbench: 100 - 500 - Read Writepgbench: 100 - 500 - Read Write - Average Latencypgbench: 100 - 500 - Read Onlypgbench: 100 - 500 - Read Only - Average Latencyapache: 500apache: 1000nginx: 500nginx: 1000ebizzy: cassandra: Writesrocksdb: Rand Readrocksdb: Read While Writingrocksdb: Read Rand Write Randrocksdb: Update Randclickhouse: 100M Rows Web Analytics Dataset, First Run / Cold Cacheclickhouse: 100M Rows Web Analytics Dataset, Second Runclickhouse: 100M Rows Web Analytics Dataset, Third Rundragonflydb: 50 - 1:5dragonflydb: 50 - 1:1dragonflydb: 50 - 5:1etcd: PUT - 100 - 100etcd: PUT - 100 - 100 - Average Latencyetcd: PUT - 100 - 1000etcd: PUT - 100 - 1000 - Average Latencyetcd: PUT - 500 - 100etcd: PUT - 500 - 100 - Average Latencyetcd: PUT - 500 - 1000etcd: PUT - 500 - 1000 - Average Latencyetcd: RANGE - 100 - 100etcd: RANGE - 100 - 100 - Average Latencyetcd: RANGE - 100 - 1000etcd: RANGE - 100 - 1000 - Average Latencyetcd: RANGE - 500 - 100etcd: RANGE - 500 - 100 - Average Latencyetcd: RANGE - 500 - 1000etcd: RANGE - 500 - 1000 - Average Latencynode-express-loadtest: node-web-tooling: simdjson: PartialTweetssimdjson: LargeRandsimdjson: Kostyasimdjson: DistinctUserIDsimdjson: TopTweetredis: GET - 1000brl-cad: VGR Performance Metriccython-bench: N-Queenspybench: Total For Average Test Timespyperformance: chaospyperformance: pickle_pure_pythonpyperformance: python_startuppyperformance: regex_compilenumpy: phpbench: PHP Benchmark Suiteluaradio: Five Back to Back FIR Filtersluaradio: FM Deemphasis Filterluaradio: Hilbert Transformluaradio: Complex Phasesrsran: 4G PHY_DL_Test 100 PRB SISO 64-QAMsrsran: 4G PHY_DL_Test 100 PRB SISO 64-QAMsrsran: 4G PHY_DL_Test 100 PRB SISO 256-QAMsrsran: 4G PHY_DL_Test 100 PRB SISO 256-QAMsrsran: 4G PHY_DL_Test 100 PRB MIMO 64-QAMsrsran: 4G PHY_DL_Test 100 PRB MIMO 64-QAMsrsran: 4G PHY_DL_Test 100 PRB MIMO 256-QAMsrsran: 4G PHY_DL_Test 100 PRB MIMO 256-QAMsrsran: 5G PHY_DL_NR Test 52 PRB SISO 64-QAMsrsran: 5G PHY_DL_NR Test 52 PRB SISO 64-QAMsrsran: OFDM_Testliquid-dsp: 32 - 256 - 57liquid-dsp: 64 - 256 - 57liquid-dsp: 128 - 256 - 57liquid-dsp: 256 - 256 - 57pjsip: OPTIONS, Statefulpjsip: OPTIONS, Statelesspjsip: INVITEngspice: C2670ngspice: C7552octave-benchmark: quantlib: helsing: 14 digitprimesieve: 1e13encode-flac: WAV To FLACencode-mp3: WAV To MP3blake2: openssl: RSA4096openssl: RSA4096openssl: SHA256xmrig: Monero - 1Mxmrig: Wownero - 1Msysbench: CPUsysbench: RAM / Memoryctx-clock: Context Switch Timem-queens: Time To Solvecoremark: CoreMark Size 666 - Iterations Per Secondsynthmark: VoiceMark_100securemark: SecureMark-TLSasmfish: 1024 Hash Memory, 26 Depthstockfish: Total Timeaircrack-ng: spec-jbb2015: SPECjbb2015-Composite max-jOPSspec-jbb2015: SPECjbb2015-Composite critical-jOPSEPYC 7713 2PEPYC 771343.8578.2150.2671264251600065946700030233800039037700037.10118650.839290.9611923306667300.69114214395026736456.2293.5565705.67589524664.832.0712272183.6399.92235808.548338.279109.24116679.26259899.49142675.55116527.634690.06100740.8945587.106.28326.74047.28489.280124.96281.7776.387052.6217.593597.4745.841388.4137561.073.2757763.892.1113.104777.1513.094774.374559.4228.051905.5133.553136.1420.381788.3435.752884.4422.16218.13292.823302374569668787887739742.043735.8319.75619.44444940930.64083330.01167531.127435.347473.912840.332754.452840.0521.4531.8446.403065.920341.281273.84865.89420.52114.271105.032178.675159.01541.16721.584160.46318.100113.46638.33815.39261.33751.86953.2324106456812254.2638.13985.76136.410535.42314.924613.151661763408684.73473.039.93485.854.1810743.252.5110851.417.1940.8922.2553.88171.52151.53107150.464258336.5831197.59068.875989.600667.173682.92632.262.2613792215844624142422742454601646265105278512.2316.997.6318.607.501.957.0455.6213.8654.2458.52179.550123.80668.79715.08144.70195.6221.48100.7728.9210.741.0066.93569.9741.2133.9577.3345.55816.7510.653.471.420.569.700.550.316.411093134473077937.96380.646758.47446.44996.3798.77311.40275255735229.6376678.5651501401663315.07619831550.1261426135.09420127220.24891255.7984767.7390312.0094018.12453258209921480175332145646732922167304654377.52387.01396.82724442.87724466.22724635.9338209.42022.641860.482523.738992.81582.643696.761822.837230.32732.742305.618323.438506.60512.644040.713522.6619810.503.8412.94.424.391364479.13324051523.31295395.83857.71155469.387260031211.6364.398.4623.8392.3143.6427.4149.6394.1133.6426.1141.0127.962.213033333316142666673191533333508506666753509000008819676174692137.362105.9516.5452808.862.12928.71818.1337.4903.3925009.61638050.513462915652750749.641982.9500125.797260.661206.3764105747.139772748.057249582248870519279486942149778.7971308996894671.6585.1300.4545762268100064227800025447700032359300019.108816959.848563.2711012051667609.67747027072252719305.6566.13306011.4272321875.120.8802712491.1397.64127998.614406.024668.9660659.25135917.4389950.7651921.712648.9657329.7024085.736.84546.54743.39288.019139.89633.93888.8214972.978.973555.7824.591293.5130742.172.0349691.031.066.824601.786.814600.942462.4025.971030.7631.021689.1818.93935.5834.171535.0820.82116.33274.5741718566317139368153820252.121943.0520.84012.00389335980.93470612.56082943.082950.432984.471266.201253.501229.5621.3329.1945.273107.102332.611273.48165.61720.10018.146159.575218.445176.43043.35429.409272.60920.610164.00139.76315.708261.36551.96558.956403339988004.9585.63067.35009.26720.82243.218814.135107735460884.93401.647.43460.355.4314123.353.8914156.730.4377.1438.8596.30299.30133.0434761.265637152.39420410.68062.981451.833456.809847.55831.401.4124633956884968249640058860812922471341000518.3321.136.8412.486.206.063.9065.4915.8636.7156.53184.711131.27070.7839.91137.74223.6626.69104.5829.2411.111.0267.62731.8840.7663.8467.1845.39816.7810.693.481.400.569.000.480.2410.85130394174651139.40236.602030.45543.26596.22110.61515.38574115613230.4326702.518615247735273.40012976840.193626547.98013709350.365115006.99109150.30173899.19174524.7924649425059624207479891821673561841358336378.44392.92394.983599739.573382696.793244708.3678829.58881.379978.190512.481895.96231.283927.655811.979219.65091.380131.795812.482098.41781.284602.780311.8636710.603.8612.94.404.391990067.3369769823.38495996.53877.64156464.567333321189.0365.498.6628.9396.0143.6430.5149.6394.4133.7431.8141.4128.962.413663333316055666672577666667270690000027671666679115666024690136.020103.3876.5822795.3120.99955.25518.1297.4723.3912613.1825904.16810180985328612.436467.2252440.579990.1212012.2382045229.645896747.308247747138776102149383689149666.94312795785389OpenBenchmarking.org

GPAW

Input: Carbon Nanotube

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

GPAW

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.0212.9229.5EPYC 7713 2P81.6422.5460.4OpenBenchmarking.orgWatts, Fewer Is BetterGPAW 22.1CPU Power Consumption Monitor120240360480600

GROMACS

Implementation: MPI CPU - Input: water_GMX50_bare

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

GROMACS

Implementation: MPI CPU - Input: water_GMX50_bare

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

GROMACS

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.8206.4230.3EPYC 7713 2P81.1402.3461.3OpenBenchmarking.orgWatts, Fewer Is BetterGROMACS 2022.1CPU Power Consumption Monitor120240360480600

NAMD

ATPase Simulation - 327,506 Atoms

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

NAMD

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.9200.4230.3EPYC 7713 2P80.5408.2461.0OpenBenchmarking.orgWatts, Fewer Is BetterNAMD 2.14CPU Power Consumption Monitor120240360480600

Graph500

Scale: 26

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

Graph500

Scale: 26

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

Graph500

Scale: 26

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

Graph500

Scale: 26

OpenBenchmarking.orgsssp max_TEPS, More Is BetterGraph500 3.0Scale: 26EPYC 7713 2PEPYC 771380M160M240M320M400M3903770003235930001. (CC) gcc options: -fcommon -O3 -lpthread -lm -lmpi

Graph500

Scale: 26

OpenBenchmarking.orgsssp max_TEPS Per Watt, More Is BetterGraph500 3.0Scale: 26EPYC 7713EPYC 7713 2P300K600K900K1200K1500K1477511.39880495.41

Graph500

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.7219.0227.4EPYC 7713 2P80.8443.4460.3OpenBenchmarking.orgWatts, Fewer Is BetterGraph500 3.0CPU Power Consumption Monitor120240360480600

High Performance Conjugate Gradient

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

High Performance Conjugate Gradient

OpenBenchmarking.orgGFLOP/s Per Watt, More Is BetterHigh Performance Conjugate Gradient 3.1EPYC 7713EPYC 7713 2P0.02070.04140.06210.08280.10350.0920.086

High Performance Conjugate Gradient

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.9208.1224.5EPYC 7713 2P80.3433.3453.1OpenBenchmarking.orgWatts, Fewer Is BetterHigh Performance Conjugate Gradient 3.1CPU Power Consumption Monitor120240360480600

WRF

Input: conus 2.5km

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

WRF

CPU Power Consumption Monitor

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

RELION

Test: Basic - Device: CPU

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

RELION

CPU Power Consumption Monitor

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: 460.84

Algebraic Multi-Grid Benchmark

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

Algebraic Multi-Grid Benchmark

OpenBenchmarking.orgFigure Of Merit Per Watt, More Is BetterAlgebraic Multi-Grid Benchmark 1.2EPYC 7713EPYC 7713 2P1.1M2.2M3.3M4.4M5.5M5339041.554855985.26

Algebraic Multi-Grid Benchmark

CPU Power Consumption Monitor

MinAvgMaxEPYC 771338.2189.6220.2EPYC 7713 2P80.3396.1449.8OpenBenchmarking.orgWatts, Fewer Is BetterAlgebraic Multi-Grid Benchmark 1.2CPU Power Consumption Monitor120240360480600

Xcompact3d Incompact3d

Input: X3D-benchmarking input.i3d

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

Xcompact3d Incompact3d

CPU Power Consumption Monitor

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: 448.76

Kripke

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

Kripke

OpenBenchmarking.orgThroughput FoM Per Watt, More Is BetterKripke 1.2.4EPYC 7713EPYC 7713 2P400K800K1200K1600K2000K1791600.71464047.76

Kripke

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.9151.1213.0EPYC 7713 2P80.6310.2398.8OpenBenchmarking.orgWatts, Fewer Is BetterKripke 1.2.4CPU Power Consumption Monitor110220330440550

LULESH

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

LULESH

OpenBenchmarking.orgz/s Per Watt, More Is BetterLULESH 2.0.3EPYC 7713EPYC 7713 2P306090120150113.62102.40

LULESH

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.6169.9219.1EPYC 7713 2P81.0356.0452.9OpenBenchmarking.orgWatts, Fewer Is BetterLULESH 2.0.3CPU Power Consumption Monitor120240360480600

Pennant

Test: leblancbig

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

Pennant

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.5149.9230.3EPYC 7713 2P80.8257.4460.6OpenBenchmarking.orgWatts, Fewer Is BetterPennant 1.0.1CPU Power Consumption Monitor120240360480600

Pennant

Test: sedovbig

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

Pennant

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.5173.1229.8EPYC 7713 2P80.6294.1460.6OpenBenchmarking.orgWatts, Fewer Is BetterPennant 1.0.1CPU Power Consumption Monitor120240360480600

miniFE

Problem Size: Small

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

miniFE

Problem Size: Small

OpenBenchmarking.orgCG Mflops Per Watt, More Is BetterminiFE 2.2Problem Size: SmallEPYC 7713EPYC 7713 2P306090120150149.7286.98

miniFE

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.6146.1213.1EPYC 7713 2P81.1283.6438.7OpenBenchmarking.orgWatts, Fewer Is BetterminiFE 2.2CPU Power Consumption Monitor120240360480600

ACES DGEMM

Sustained Floating-Point Rate

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

ACES DGEMM

Sustained Floating-Point Rate

OpenBenchmarking.orgGFLOP/s Per Watt, More Is BetterACES DGEMM 1.0Sustained Floating-Point RateEPYC 7713EPYC 7713 2P0.02770.05540.08310.11080.13850.1230.104

ACES DGEMM

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.5169.8230.3EPYC 7713 2P81.0309.4460.6OpenBenchmarking.orgWatts, Fewer Is BetterACES DGEMM 1.0CPU Power Consumption Monitor120240360480600

NWChem

Input: C240 Buckyball

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

NWChem

CPU Power Consumption Monitor

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

Quantum ESPRESSO

Input: AUSURF112

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

Quantum ESPRESSO

CPU Power Consumption Monitor

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: 465.83

NAS Parallel Benchmarks

Test / Class: BT.C

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

NAS Parallel Benchmarks

Test / Class: BT.C

OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: BT.CEPYC 7713 2PEPYC 7713140280420560700670.57669.13

NAS Parallel Benchmarks

CPU Power Consumption Monitor

MinAvgMaxEPYC 771338.1191.3229.4EPYC 7713 2P79.7351.7458.8OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor120240360480600

NAS Parallel Benchmarks

Test / Class: EP.C

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

NAS Parallel Benchmarks

Test / Class: EP.C

OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: EP.CEPYC 7713 2PEPYC 7713112233445548.8044.17

NAS Parallel Benchmarks

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.599.7230.3EPYC 7713 2P80.3170.9461.2OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor120240360480600

NAS Parallel Benchmarks

Test / Class: EP.D

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

NAS Parallel Benchmarks

Test / Class: EP.D

OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: EP.DEPYC 7713 2PEPYC 771361218243024.3823.10

NAS Parallel Benchmarks

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.5202.1230.6EPYC 7713 2P80.8373.7460.6OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor120240360480600

NAS Parallel Benchmarks

Test / Class: FT.C

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

NAS Parallel Benchmarks

Test / Class: FT.C

OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: FT.CEPYC 7713 2PEPYC 7713100200300400500457.47412.62

NAS Parallel Benchmarks

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.9147.0225.1EPYC 7713 2P80.0255.1456.5OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor120240360480600

NAS Parallel Benchmarks

Test / Class: LU.C

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

NAS Parallel Benchmarks

Test / Class: LU.C

OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: LU.CEPYC 7713 2PEPYC 77132004006008001000816.26767.46

NAS Parallel Benchmarks

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.9177.1226.2EPYC 7713 2P80.7318.4459.8OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor120240360480600

NAS Parallel Benchmarks

Test / Class: SP.B

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

NAS Parallel Benchmarks

Test / Class: SP.B

OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: SP.BEPYC 7713EPYC 7713 2P150300450600750698.95628.09

NAS Parallel Benchmarks

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.7128.7229.8EPYC 7713 2P80.4227.2460.4OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor120240360480600

NAS Parallel Benchmarks

Test / Class: SP.C

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

NAS Parallel Benchmarks

Test / Class: SP.C

OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: SP.CEPYC 7713 2PEPYC 771370140210280350331.99271.37

NAS Parallel Benchmarks

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.6191.3226.8EPYC 7713 2P80.8351.0458.4OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor120240360480600

NAS Parallel Benchmarks

Test / Class: IS.D

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

NAS Parallel Benchmarks

Test / Class: IS.D

OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: IS.DEPYC 7713EPYC 7713 2P4812162016.1715.94

NAS Parallel Benchmarks

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.7163.8222.4EPYC 7713 2P80.6294.3451.7OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor120240360480600

NAS Parallel Benchmarks

Test / Class: MG.C

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

NAS Parallel Benchmarks

Test / Class: MG.C

OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: MG.CEPYC 7713 2PEPYC 7713110220330440550511.45503.96

NAS Parallel Benchmarks

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.6113.8220.8EPYC 7713 2P80.9197.0457.5OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor120240360480600

NAS Parallel Benchmarks

Test / Class: CG.C

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

NAS Parallel Benchmarks

Test / Class: CG.C

OpenBenchmarking.orgTotal Mop/s Per Watt, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: CG.CEPYC 7713 2PEPYC 77134080120160200181.81165.19

NAS Parallel Benchmarks

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.5145.8229.2EPYC 7713 2P81.4250.7460.8OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor120240360480600

Rodinia

Test: OpenMP CFD Solver

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

Rodinia

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.5134.1228.4EPYC 7713 2P81.0257.2439.8OpenBenchmarking.orgWatts, Fewer Is BetterRodinia 3.1CPU Power Consumption Monitor120240360480600

Rodinia

Test: OpenMP LavaMD

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

Rodinia

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.5200.8230.3EPYC 7713 2P81.3362.6462.2OpenBenchmarking.orgWatts, Fewer Is BetterRodinia 3.1CPU Power Consumption Monitor120240360480600

Rodinia

Test: OpenMP Leukocyte

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

Rodinia

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.9160.1185.5EPYC 7713 2P80.6252.9280.7OpenBenchmarking.orgWatts, Fewer Is BetterRodinia 3.1CPU Power Consumption Monitor70140210280350

Rodinia

Test: OpenMP HotSpot3D

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

Rodinia

CPU Power Consumption Monitor

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: 331.47

OpenFOAM

Input: drivaerFastback, Small Mesh Size - Mesh Time

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

OpenFOAM

Input: drivaerFastback, Small Mesh Size - Execution Time

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

OpenFOAM

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.2217.7230.3EPYC 7713 2P81.3443.3460.6OpenBenchmarking.orgWatts, Fewer Is BetterOpenFOAM 9CPU Power Consumption Monitor120240360480600

OpenFOAM

Input: drivaerFastback, Large Mesh Size - Mesh Time

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

OpenFOAM

Input: drivaerFastback, Large Mesh Size - Execution Time

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

OpenFOAM

CPU Power Consumption Monitor

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

OpenVINO

Model: Face Detection FP16 - Device: CPU

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

OpenVINO

Model: Face Detection FP16 - Device: CPU

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

OpenVINO

CPU Power Consumption Monitor

MinAvgMaxEPYC 771338.1202.8230.8EPYC 7713 2P80.6405.9462.0OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2022.2.devCPU Power Consumption Monitor120240360480600

OpenVINO

Model: Face Detection FP16-INT8 - Device: CPU

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

OpenVINO

Model: Face Detection FP16-INT8 - Device: CPU

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

OpenVINO

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.8212.9230.3EPYC 7713 2P80.7412.9460.7OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2022.2.devCPU Power Consumption Monitor120240360480600

OpenVINO

Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU

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

OpenVINO

Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU

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

OpenVINO

CPU Power Consumption Monitor

MinAvgMaxEPYC 771338.3215.1230.2EPYC 7713 2P80.5411.0459.0OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2022.2.devCPU Power Consumption Monitor120240360480600

OpenVINO

Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU

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

OpenVINO

Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU

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

OpenVINO

CPU Power Consumption Monitor

MinAvgMaxEPYC 771338.1214.6229.9EPYC 7713 2P80.8422.9454.9OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2022.2.devCPU Power Consumption Monitor120240360480600

OpenVINO

Model: Person Detection FP16 - Device: CPU

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

OpenVINO

Model: Person Detection FP16 - Device: CPU

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

OpenVINO

CPU Power Consumption Monitor

MinAvgMaxEPYC 771338.1210.0230.3EPYC 7713 2P80.4408.8461.3OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2022.2.devCPU Power Consumption Monitor120240360480600

OpenVINO

Model: Person Detection FP32 - Device: CPU

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

OpenVINO

Model: Person Detection FP32 - Device: CPU

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

OpenVINO

CPU Power Consumption Monitor

MinAvgMaxEPYC 771338.1212.3230.5EPYC 7713 2P81.0412.0461.4OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2022.2.devCPU Power Consumption Monitor120240360480600

OpenVINO

Model: Weld Porosity Detection FP16-INT8 - Device: CPU

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

OpenVINO

Model: Weld Porosity Detection FP16-INT8 - Device: CPU

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

OpenVINO

CPU Power Consumption Monitor

MinAvgMaxEPYC 771338.0216.7230.3EPYC 7713 2P80.0432.5460.9OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2022.2.devCPU Power Consumption Monitor120240360480600

OpenVINO

Model: Weld Porosity Detection FP16 - Device: CPU

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

OpenVINO

Model: Weld Porosity Detection FP16 - Device: CPU

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

OpenVINO

CPU Power Consumption Monitor

MinAvgMaxEPYC 771338.2216.4230.3EPYC 7713 2P80.3430.2460.6OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2022.2.devCPU Power Consumption Monitor120240360480600

OpenVINO

Model: Vehicle Detection FP16-INT8 - Device: CPU

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

OpenVINO

Model: Vehicle Detection FP16-INT8 - Device: CPU

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

OpenVINO

CPU Power Consumption Monitor

MinAvgMaxEPYC 771338.1217.2230.3EPYC 7713 2P80.8430.5460.5OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2022.2.devCPU Power Consumption Monitor120240360480600

OpenVINO

Model: Vehicle Detection FP16 - Device: CPU

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

OpenVINO

Model: Vehicle Detection FP16 - Device: CPU

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

OpenVINO

CPU Power Consumption Monitor

MinAvgMaxEPYC 771338.1215.9230.3EPYC 7713 2P81.0431.0461.0OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2022.2.devCPU Power Consumption Monitor120240360480600

OpenVINO

Model: Person Vehicle Bike Detection FP16 - Device: CPU

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

OpenVINO

Model: Person Vehicle Bike Detection FP16 - Device: CPU

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

OpenVINO

CPU Power Consumption Monitor

MinAvgMaxEPYC 771338.1214.6230.3EPYC 7713 2P80.5419.4460.6OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2022.2.devCPU Power Consumption Monitor120240360480600

OpenVINO

Model: Machine Translation EN To DE FP16 - Device: CPU

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

OpenVINO

Model: Machine Translation EN To DE FP16 - Device: CPU

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

OpenVINO

CPU Power Consumption Monitor

MinAvgMaxEPYC 771338.1212.8230.3EPYC 7713 2P81.0416.9461.0OpenBenchmarking.orgWatts, Fewer Is BetterOpenVINO 2022.2.devCPU Power Consumption Monitor120240360480600

ONNX Runtime

Model: yolov4 - Device: CPU - Executor: Standard

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

ONNX Runtime

Model: yolov4 - Device: CPU - Executor: Standard

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

ONNX Runtime

CPU Power Consumption Monitor

MinAvgMaxEPYC 771338.2199.5207.6EPYC 7713 2P81.1405.9431.2OpenBenchmarking.orgWatts, Fewer Is BetterONNX Runtime 1.11CPU Power Consumption Monitor110220330440550

ONNX Runtime

Model: fcn-resnet101-11 - Device: CPU - Executor: Standard

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

ONNX Runtime

Model: fcn-resnet101-11 - Device: CPU - Executor: Standard

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

ONNX Runtime

CPU Power Consumption Monitor

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: 454.19

ONNX Runtime

Model: super-resolution-10 - Device: CPU - Executor: Standard

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

ONNX Runtime

Model: super-resolution-10 - Device: CPU - Executor: Standard

OpenBenchmarking.orgInferences Per Minute Per Watt, More Is BetterONNX Runtime 1.11Model: super-resolution-10 - Device: CPU - Executor: StandardEPYC 7713EPYC 7713 2P112233445547.9119.08

ONNX Runtime

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.6138.4144.1EPYC 7713 2P81.0239.5266.4OpenBenchmarking.orgWatts, Fewer Is BetterONNX Runtime 1.11CPU Power Consumption Monitor70140210280350

ONNX Runtime

Model: bertsquad-12 - Device: CPU - Executor: Standard

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

ONNX Runtime

Model: bertsquad-12 - Device: CPU - Executor: Standard

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

ONNX Runtime

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.4207.8217.7EPYC 7713 2P80.1376.1410.7OpenBenchmarking.orgWatts, Fewer Is BetterONNX Runtime 1.11CPU Power Consumption Monitor110220330440550

ONNX Runtime

Model: GPT-2 - Device: CPU - Executor: Standard

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

ONNX Runtime

Model: GPT-2 - Device: CPU - Executor: Standard

OpenBenchmarking.orgInferences Per Minute Per Watt, More Is BetterONNX Runtime 1.11Model: GPT-2 - Device: CPU - Executor: StandardEPYC 7713EPYC 7713 2P163248648069.9032.83

ONNX Runtime

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.6134.0138.2EPYC 7713 2P81.2240.0259.0OpenBenchmarking.orgWatts, Fewer Is BetterONNX Runtime 1.11CPU Power Consumption Monitor70140210280350

ONNX Runtime

Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard

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

ONNX Runtime

Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard

OpenBenchmarking.orgInferences Per Minute Per Watt, More Is BetterONNX Runtime 1.11Model: ArcFace ResNet-100 - Device: CPU - Executor: StandardEPYC 7713EPYC 7713 2P2468107.3812.228

ONNX Runtime

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.4208.4220.6EPYC 7713 2P81.3393.7432.4OpenBenchmarking.orgWatts, Fewer Is BetterONNX Runtime 1.11CPU Power Consumption Monitor110220330440550

ASKAP

Test: tConvolve MPI - Degridding

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

ASKAP

Test: tConvolve MPI - Gridding

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

ASKAP

Test: tConvolve MPI - Gridding

OpenBenchmarking.orgMpix/sec Per Watt, More Is BetterASKAP 1.0Test: tConvolve MPI - GriddingEPYC 7713EPYC 7713 2P306090120150114.57109.93

ASKAP

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.8191.5223.8EPYC 7713 2P80.9397.8456.8OpenBenchmarking.orgWatts, Fewer Is BetterASKAP 1.0CPU Power Consumption Monitor120240360480600

ASKAP

Test: Hogbom Clean OpenMP

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

ASKAP

Test: Hogbom Clean OpenMP

OpenBenchmarking.orgIterations Per Second Per Watt, More Is BetterASKAP 1.0Test: Hogbom Clean OpenMPEPYC 7713EPYC 7713 2P1.27942.55883.83825.11766.3975.6861.759

ASKAP

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.792.5193.0EPYC 7713 2P81.1181.8371.2OpenBenchmarking.orgWatts, Fewer Is BetterASKAP 1.0CPU Power Consumption Monitor100200300400500

CloverLeaf

Lagrangian-Eulerian Hydrodynamics

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

CloverLeaf

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.4174.1223.2EPYC 7713 2P80.3347.2423.2OpenBenchmarking.orgWatts, Fewer Is BetterCloverLeafCPU Power Consumption Monitor110220330440550

LeelaChessZero

Backend: BLAS

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

LeelaChessZero

Backend: BLAS

OpenBenchmarking.orgNodes Per Second Per Watt, More Is BetterLeelaChessZero 0.28Backend: BLASEPYC 7713EPYC 7713 2P51015202519.0713.53

LeelaChessZero

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.6204.2230.7EPYC 7713 2P81.0328.9460.6OpenBenchmarking.orgWatts, Fewer Is BetterLeelaChessZero 0.28CPU Power Consumption Monitor120240360480600

LeelaChessZero

Backend: Eigen

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

LeelaChessZero

Backend: Eigen

OpenBenchmarking.orgNodes Per Second Per Watt, More Is BetterLeelaChessZero 0.28Backend: EigenEPYC 7713EPYC 7713 2P4812162017.3412.41

LeelaChessZero

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.9207.5230.7EPYC 7713 2P81.1329.8460.4OpenBenchmarking.orgWatts, Fewer Is BetterLeelaChessZero 0.28CPU Power Consumption Monitor120240360480600

oneDNN

Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU

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

oneDNN

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.8149.1230.3EPYC 7713 2P80.5273.0439.5OpenBenchmarking.orgWatts, Fewer Is BetteroneDNN 2.6CPU Power Consumption Monitor120240360480600

oneDNN

Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU

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

oneDNN

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.6112.3150.9EPYC 7713 2P80.7253.4318.5OpenBenchmarking.orgWatts, Fewer Is BetteroneDNN 2.6CPU Power Consumption Monitor80160240320400

oneDNN

Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU

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

oneDNN

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.5168.7226.2EPYC 7713 2P80.3314.7424.8OpenBenchmarking.orgWatts, Fewer Is BetteroneDNN 2.6CPU Power Consumption Monitor110220330440550

oneDNN

Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU

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

oneDNN

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.9168.5224.6EPYC 7713 2P82.0315.9436.6OpenBenchmarking.orgWatts, Fewer Is BetteroneDNN 2.6CPU Power Consumption Monitor110220330440550

oneDNN

Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU

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

oneDNN

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.8168.8226.3EPYC 7713 2P80.9315.7435.6OpenBenchmarking.orgWatts, Fewer Is BetteroneDNN 2.6CPU Power Consumption Monitor110220330440550

oneDNN

Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU

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

oneDNN

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.7177.4223.4EPYC 7713 2P81.0327.5425.5OpenBenchmarking.orgWatts, Fewer Is BetteroneDNN 2.6CPU Power Consumption Monitor110220330440550

oneDNN

Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU

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

oneDNN

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.7177.8222.4EPYC 7713 2P80.5327.1426.0OpenBenchmarking.orgWatts, Fewer Is BetteroneDNN 2.6CPU Power Consumption Monitor110220330440550

oneDNN

Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU

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

oneDNN

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.7179.1224.2EPYC 7713 2P80.7328.4424.1OpenBenchmarking.orgWatts, Fewer Is BetteroneDNN 2.6CPU Power Consumption Monitor110220330440550

Mlpack Benchmark

Benchmark: scikit_svm

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_svmEPYC 7713EPYC 7713 2P510152025SE +/- 0.02, N = 3SE +/- 0.01, N = 321.3321.45

Mlpack Benchmark

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.671.798.0EPYC 7713 2P77.2142.1178.2OpenBenchmarking.orgWatts, Fewer Is BetterMlpack BenchmarkCPU Power Consumption Monitor50100150200250

Mlpack Benchmark

Benchmark: scikit_qda

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_qdaEPYC 7713EPYC 7713 2P714212835SE +/- 0.09, N = 3SE +/- 0.28, N = 1529.1931.84

Mlpack Benchmark

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.2129.3225.9EPYC 7713 2P80.3243.7428.2OpenBenchmarking.orgWatts, Fewer Is BetterMlpack BenchmarkCPU Power Consumption Monitor110220330440550

Mlpack Benchmark

Benchmark: scikit_ica

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_icaEPYC 7713EPYC 7713 2P1122334455SE +/- 0.05, N = 3SE +/- 0.13, N = 345.2746.40

Mlpack Benchmark

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.6189.9220.0EPYC 7713 2P80.2334.4374.1OpenBenchmarking.orgWatts, Fewer Is BetterMlpack BenchmarkCPU Power Consumption Monitor100200300400500

TNN

Target: CPU - Model: DenseNet

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

TNN

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.595.1101.9EPYC 7713 2P80.3200.2232.1OpenBenchmarking.orgWatts, Fewer Is BetterTNN 0.3CPU Power Consumption Monitor60120180240300

TNN

Target: CPU - Model: MobileNet v2

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

TNN

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.383.092.5EPYC 7713 2P81.6174.3194.2OpenBenchmarking.orgWatts, Fewer Is BetterTNN 0.3CPU Power Consumption Monitor50100150200250

TNN

Target: CPU - Model: SqueezeNet v1.1

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

TNN

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.268.675.3EPYC 7713 2P81.1138.9151.6OpenBenchmarking.orgWatts, Fewer Is BetterTNN 0.3CPU Power Consumption Monitor4080120160200

TNN

Target: CPU - Model: SqueezeNet v2

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

TNN

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.158.475.7EPYC 7713 2P80.0119.3154.0OpenBenchmarking.orgWatts, Fewer Is BetterTNN 0.3CPU Power Consumption Monitor4080120160200

Timed Apache Compilation

Time To Compile

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

Timed Apache Compilation

CPU Power Consumption Monitor

MinAvgMaxEPYC 771337.481.0129.0EPYC 7713 2P79.6154.9245.3OpenBenchmarking.orgWatts, Fewer Is BetterTimed Apache Compilation 2.4.41CPU Power Consumption Monitor60120180240300

Timed FFmpeg Compilation

Time To Compile

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed FFmpeg Compilation 4.4Time To CompileEPYC 7713 2PEPYC 771348121620