EPYC 7543 + TYAN S8036GM2NE-LE

AMD EPYC 7543 with TYAN S8036GM2NE-LE server. Sensor monitoring benchmarks by Michael Larabel.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2107280-IB-EPYC7543T98
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EPYC 7543 + S8036GM2NE
July 28 2021
  6 Hours, 40 Minutes
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EPYC 7543 + S8036GM2NE

Processor: AMD EPYC 7543 32-Core @ 2.80GHz (32 Cores / 64 Threads), Motherboard: TYAN S8036GM2NE-LE (V2.00.B21 BIOS), Chipset: AMD Starship/Matisse, Memory: 8 x GB DDR4-3200MT/s HMA81GR7CJR8N-XN, Disk: 1000GB Corsair Force MP600, Graphics: ASPEED, Monitor: VE228, Network: 2 x Broadcom NetXtreme BCM5720 2-port PCIe

OS: Ubuntu 21.04, Kernel: 5.11.0-25-generic (x86_64), Desktop: GNOME Shell 3.38.4, Display Server: X Server, Vulkan: 1.0.2, Compiler: GCC 10.3.0, 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-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-mutex --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-10-gDeRY6/gcc-10-10.3.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-10-gDeRY6/gcc-10-10.3.0/debian/tmp-gcn/usr,hsa --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 schedutil (Boost: Enabled) - CPU Microcode: 0xa001119
Java Notes: OpenJDK Runtime Environment (build 11.0.11+9-Ubuntu-0ubuntu2)
Python Notes: Python 3.9.5
Security Notes: itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: 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 Full AMD retpoline IBPB: conditional IBRS_FW STIBP: always-on RSB filling + srbds: Not affected + tsx_async_abort: Not affected

EPYC 7543 + TYAN S8036GM2NE-LEOpenBenchmarking.orgPhoronix Test SuiteAMD EPYC 7543 32-Core @ 2.80GHz (32 Cores / 64 Threads)TYAN S8036GM2NE-LE (V2.00.B21 BIOS)AMD Starship/Matisse8 x GB DDR4-3200MT/s HMA81GR7CJR8N-XN1000GB Corsair Force MP600ASPEEDVE2282 x Broadcom NetXtreme BCM5720 2-port PCIeUbuntu 21.045.11.0-25-generic (x86_64)GNOME Shell 3.38.4X Server1.0.2GCC 10.3.0ext41920x1080ProcessorMotherboardChipsetMemoryDiskGraphicsMonitorNetworkOSKernelDesktopDisplay ServerVulkanCompilerFile-SystemScreen ResolutionEPYC 7543 + TYAN S8036GM2NE-LE BenchmarksSystem Logs- Transparent Huge Pages: madvise- --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --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-mutex --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-10-gDeRY6/gcc-10-10.3.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-10-gDeRY6/gcc-10-10.3.0/debian/tmp-gcn/usr,hsa --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 - Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0xa001119 - OpenJDK Runtime Environment (build 11.0.11+9-Ubuntu-0ubuntu2)- Python 3.9.5- itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: 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 Full AMD retpoline IBPB: conditional IBRS_FW STIBP: always-on RSB filling + srbds: Not affected + tsx_async_abort: Not affected

EPYC 7543 + TYAN S8036GM2NE-LEbuild-gcc: Time To Compileqe: AUSURF112pgbench: 100 - 250 - Read Write - Average Latencypgbench: 100 - 250 - Read Writeopenvkl: vklBenchmarkbuild-llvm: Unix Makefilestnn: CPU - DenseNetbuild-llvm: Ninjablender: Barbershop - CPU-Onlybrl-cad: VGR Performance Metricbuild-nodejs: Time To Compilecassandra: Writesaom-av1: Speed 6 Two-Pass - Bosphorus 4Ksvt-av1: Preset 4 - Bosphorus 4Ksvt-av1: Preset 8 - Bosphorus 4Kaom-av1: Speed 8 Realtime - Bosphorus 4Kncnn: CPU - regnety_400mncnn: CPU - squeezenet_ssdncnn: CPU - yolov4-tinyncnn: CPU - resnet50ncnn: CPU - alexnetncnn: CPU - resnet18ncnn: CPU - vgg16ncnn: CPU - googlenetncnn: CPU - blazefacencnn: CPU - efficientnet-b0ncnn: CPU - mnasnetncnn: CPU - shufflenet-v2ncnn: CPU-v3-v3 - mobilenet-v3ncnn: CPU-v2-v2 - mobilenet-v2ncnn: CPU - mobilenetmnn: inception-v3mnn: mobilenet-v1-1.0mnn: MobileNetV2_224mnn: SqueezeNetV1.0mnn: resnet-v2-50mnn: squeezenetv1.1mnn: mobilenetV3pyperformance: raytracepyperformance: python_startupoidn: RTLightmap.hdr.4096x4096pyperformance: 2to3rocksdb: Update Randrocksdb: Read Rand Write Randrocksdb: Rand Fillrocksdb: Read While Writingaom-av1: Speed 6 Realtime - Bosphorus 4Kbuild-godot: Time To Compilenpb: SP.Cvpxenc: Speed 5 - Bosphorus 4Kpyperformance: gobuild-gdb: Time To Compilepyperformance: django_templatenpb: BT.Cbuild-linux-kernel: Time To Compilepyperformance: regex_compileoidn: RT.hdr_alb_nrm.3840x2160phpbench: PHP Benchmark Suitepyperformance: crypto_pyaesastcenc: Exhaustivepyperformance: pathlibpyperformance: pickle_pure_pythonstockfish: Total Timenpb: IS.Dpyperformance: json_loadspyperformance: nbodybuild-ffmpeg: Time To Compilepybench: Total For Average Test Timespyperformance: chaostnn: CPU - MobileNet v2aom-av1: Speed 9 Realtime - Bosphorus 4Knpb: LU.Credis: SETpyperformance: floattnn: CPU - SqueezeNet v1.1redis: GETsvt-vp9: Visual Quality Optimized - Bosphorus 1080pnpb: FT.Cnpb: SP.Bnpb: CG.Csvt-hevc: 7 - Bosphorus 1080ptnn: CPU - SqueezeNet v2npb: MG.Cnpb: EP.Csvt-vp9: VMAF Optimized - Bosphorus 1080psvt-vp9: PSNR/SSIM Optimized - Bosphorus 1080psvt-hevc: 10 - Bosphorus 1080pEPYC 7543 + S8036GM2NE626.945547.9718.05613877328250.2132985.336208.747195.95414804140.1592662957.481.60421.20132.5619.9019.4127.2026.078.7013.3564.5518.193.769.617.177.537.328.0619.6925.0453.0234.5236.28920.7334.2622.3844397.420.482998018483048007988045589661810.5154.06932713.7514.7623439.61844.787355.3132.5571550.9565557799.623.473116407892083481760.8522.511521.589932104293.16745.74106020.342039663.44104271.1072496247.63231.6648624.0167693.5930198.41292.3564.85049178.152962.42240.66244.20468.15OpenBenchmarking.org

Timed GCC Compilation

This test times how long it takes to build the GNU Compiler Collection (GCC). Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed GCC Compilation 9.3.0Time To CompileEPYC 7543 + S8036GM2NE140280420560700SE +/- 0.45, N = 3626.95
MinAvgMaxEPYC 7543 + S8036GM2NE179936383729OpenBenchmarking.orgMegahertz, More Is BetterTimed GCC Compilation 9.3.0CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE90.3119.6206.4OpenBenchmarking.orgWatts, Fewer Is BetterTimed GCC Compilation 9.3.0CPU Power Consumption Monitor601201802403001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 3868 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE45.059.677.3OpenBenchmarking.orgCelsius, Fewer Is BetterTimed GCC Compilation 9.3.0CPU Temperature Monitor20406080100
MinAvgMaxEPYC 7543 + S8036GM2NE32.934.637.9OpenBenchmarking.orgCelsius, Fewer Is BetterTimed GCC Compilation 9.3.0Drive Temperature (nvme0n1) Monitor1122334455
MinAvgMaxEPYC 7543 + S8036GM2NE33.035.037.0OpenBenchmarking.orgCelsius, Fewer Is BetterTimed GCC Compilation 9.3.0Memory Temperature Monitor1122334455
MinAvgMaxEPYC 7543 + S8036GM2NE144.0188.8328.0OpenBenchmarking.orgWatts, Fewer Is BetterTimed GCC Compilation 9.3.0System Power Consumption Monitor801602403204001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 6293 Joules per run.

Quantum ESPRESSO

OpenBenchmarking.orgSeconds, Fewer Is BetterQuantum ESPRESSO 6.8Input: AUSURF112EPYC 7543 + S8036GM2NE120240360480600SE +/- 3.78, N = 3547.971. (F9X) gfortran options: -ldevXlib -lopenblas -lFoX_dom -lFoX_sax -lFoX_wxml -lFoX_common -lFoX_utils -lFoX_fsys -lfftw3 -pthread -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -ldl -levent_core -levent_pthreads -lutil -lm -lrt -lz
MinAvgMaxEPYC 7543 + S8036GM2NE270134343571OpenBenchmarking.orgMegahertz, More Is BetterQuantum ESPRESSO 6.8CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE137.9187.5208.3OpenBenchmarking.orgWatts, Fewer Is BetterQuantum ESPRESSO 6.8CPU Power Consumption Monitor601201802403001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 5374 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE53.563.973.5OpenBenchmarking.orgCelsius, Fewer Is BetterQuantum ESPRESSO 6.8CPU Temperature Monitor20406080100
MinAvgMaxEPYC 7543 + S8036GM2NE32.932.933.9OpenBenchmarking.orgCelsius, Fewer Is BetterQuantum ESPRESSO 6.8Drive Temperature (nvme0n1) Monitor1020304050
MinAvgMaxEPYC 7543 + S8036GM2NE32.032.834.0OpenBenchmarking.orgCelsius, Fewer Is BetterQuantum ESPRESSO 6.8Memory Temperature Monitor1020304050
MinAvgMaxEPYC 7543 + S8036GM2NE248.0286.6328.0OpenBenchmarking.orgWatts, Fewer Is BetterQuantum ESPRESSO 6.8System Power Consumption Monitor801602403204001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 8597 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 13.0Scaling Factor: 100 - Clients: 250 - Mode: Read Write - Average LatencyEPYC 7543 + S8036GM2NE48121620SE +/- 0.22, N = 1518.061. (CC) gcc options: -fno-strict-aliasing -fwrapv -O2 -lpgcommon -lpgport -lpq -lpthread -lrt -ldl -lm
MinAvgMaxEPYC 7543 + S8036GM2NE179730453732OpenBenchmarking.orgMegahertz, More Is BetterPostgreSQL pgbench 13.0CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE83.9133.8170.1OpenBenchmarking.orgWatts, Fewer Is BetterPostgreSQL pgbench 13.0CPU Power Consumption Monitor501001502002501. EPYC 7543 + S8036GM2NE: Approximate power consumption of 847 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE49.868.677.0OpenBenchmarking.orgCelsius, Fewer Is BetterPostgreSQL pgbench 13.0CPU Temperature Monitor20406080100
MinAvgMaxEPYC 7543 + S8036GM2NE35.936.637.9OpenBenchmarking.orgCelsius, Fewer Is BetterPostgreSQL pgbench 13.0Drive Temperature (nvme0n1) Monitor1122334455
MinAvgMaxEPYC 7543 + S8036GM2NE32.036.938.0OpenBenchmarking.orgCelsius, Fewer Is BetterPostgreSQL pgbench 13.0Memory Temperature Monitor1122334455
MinAvgMaxEPYC 7543 + S8036GM2NE136.0188.8248.0OpenBenchmarking.orgWatts, Fewer Is BetterPostgreSQL pgbench 13.0System Power Consumption Monitor601201802403001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 1246 Joules per run.

OpenBenchmarking.orgTPS, More Is BetterPostgreSQL pgbench 13.0Scaling Factor: 100 - Clients: 250 - Mode: Read WriteEPYC 7543 + S8036GM2NE3K6K9K12K15KSE +/- 165.00, N = 15138771. (CC) gcc options: -fno-strict-aliasing -fwrapv -O2 -lpgcommon -lpgport -lpq -lpthread -lrt -ldl -lm

OpenVKL

OpenVKL is the Intel Open Volume Kernel Library that offers high-performance volume computation kernels and part of the Intel oneAPI rendering toolkit. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgItems / Sec, More Is BetterOpenVKL 0.9Benchmark: vklBenchmarkEPYC 7543 + S8036GM2NE70140210280350SE +/- 4.37, N = 3328MIN: 1 / MAX: 1489
MinAvgMaxEPYC 7543 + S8036GM2NE331235943731OpenBenchmarking.orgMegahertz, More Is BetterOpenVKL 0.9CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE92.0131.4153.0OpenBenchmarking.orgWatts, Fewer Is BetterOpenVKL 0.9CPU Power Consumption Monitor40801201602001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 2672 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE46.365.774.8OpenBenchmarking.orgCelsius, Fewer Is BetterOpenVKL 0.9CPU Temperature Monitor20406080100
MinAvgMaxEPYC 7543 + S8036GM2NE29.930.030.9OpenBenchmarking.orgCelsius, Fewer Is BetterOpenVKL 0.9Drive Temperature (nvme0n1) Monitor918273645
MinAvgMaxEPYC 7543 + S8036GM2NE33.035.637.0OpenBenchmarking.orgCelsius, Fewer Is BetterOpenVKL 0.9Memory Temperature Monitor1122334455
MinAvgMaxEPYC 7543 + S8036GM2NE136.0200.3248.0OpenBenchmarking.orgWatts, Fewer Is BetterOpenVKL 0.9System Power Consumption Monitor601201802403001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 4205 Joules per run.

Timed LLVM Compilation

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

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed LLVM Compilation 12.0Build System: Unix MakefilesEPYC 7543 + S8036GM2NE50100150200250SE +/- 2.77, N = 4250.21
MinAvgMaxEPYC 7543 + S8036GM2NE313335373729OpenBenchmarking.orgMegahertz, More Is BetterTimed LLVM Compilation 12.0CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE89.0168.7213.6OpenBenchmarking.orgWatts, Fewer Is BetterTimed LLVM Compilation 12.0CPU Power Consumption Monitor601201802403001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 1603 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE47.063.873.0OpenBenchmarking.orgCelsius, Fewer Is BetterTimed LLVM Compilation 12.0CPU Temperature Monitor20406080100
MinAvgMaxEPYC 7543 + S8036GM2NE36.937.337.9OpenBenchmarking.orgCelsius, Fewer Is BetterTimed LLVM Compilation 12.0Drive Temperature (nvme0n1) Monitor1122334455
MinAvgMaxEPYC 7543 + S8036GM2NE33.034.636.0OpenBenchmarking.orgCelsius, Fewer Is BetterTimed LLVM Compilation 12.0Memory Temperature Monitor1020304050
MinAvgMaxEPYC 7543 + S8036GM2NE152.0255.4328.0OpenBenchmarking.orgWatts, Fewer Is BetterTimed LLVM Compilation 12.0System Power Consumption Monitor801602403204001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 2682 Joules per run.

TNN

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

OpenBenchmarking.orgms, Fewer Is BetterTNN 0.3Target: CPU - Model: DenseNetEPYC 7543 + S8036GM2NE6001200180024003000SE +/- 16.23, N = 32985.34MIN: 2885.42 / MAX: 3826.661. (CXX) g++ options: -fopenmp -pthread -fvisibility=hidden -fvisibility=default -O3 -rdynamic -ldl
MinAvgMaxEPYC 7543 + S8036GM2NE355035893627OpenBenchmarking.orgMegahertz, More Is BetterTNN 0.3CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE95.5117.6126.5OpenBenchmarking.orgWatts, Fewer Is BetterTNN 0.3CPU Power Consumption Monitor40801201602001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 1215 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE51.560.668.5OpenBenchmarking.orgCelsius, Fewer Is BetterTNN 0.3CPU Temperature Monitor20406080100
MinAvgMaxEPYC 7543 + S8036GM2NE28.930.331.9OpenBenchmarking.orgCelsius, Fewer Is BetterTNN 0.3Drive Temperature (nvme0n1) Monitor918273645
MinAvgMaxEPYC 7543 + S8036GM2NE33.035.336.0OpenBenchmarking.orgCelsius, Fewer Is BetterTNN 0.3Memory Temperature Monitor1020304050
MinAvgMaxEPYC 7543 + S8036GM2NE144.0174.0184.0OpenBenchmarking.orgWatts, Fewer Is BetterTNN 0.3System Power Consumption Monitor501001502002501. EPYC 7543 + S8036GM2NE: Approximate power consumption of 1856 Joules per run.

Timed LLVM Compilation

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

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed LLVM Compilation 12.0Build System: NinjaEPYC 7543 + S8036GM2NE50100150200250SE +/- 1.00, N = 3208.75
MinAvgMaxEPYC 7543 + S8036GM2NE180033313725OpenBenchmarking.orgMegahertz, More Is BetterTimed LLVM Compilation 12.0CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE92.0160.7210.7OpenBenchmarking.orgWatts, Fewer Is BetterTimed LLVM Compilation 12.0CPU Power Consumption Monitor601201802403001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 643 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE46.061.573.8OpenBenchmarking.orgCelsius, Fewer Is BetterTimed LLVM Compilation 12.0CPU Temperature Monitor20406080100
MinAvgMaxEPYC 7543 + S8036GM2NE32.936.137.9OpenBenchmarking.orgCelsius, Fewer Is BetterTimed LLVM Compilation 12.0Drive Temperature (nvme0n1) Monitor1122334455
MinAvgMaxEPYC 7543 + S8036GM2NE33.034.836.0OpenBenchmarking.orgCelsius, Fewer Is BetterTimed LLVM Compilation 12.0Memory Temperature Monitor1020304050
MinAvgMaxEPYC 7543 + S8036GM2NE152.0267.7352.0OpenBenchmarking.orgWatts, Fewer Is BetterTimed LLVM Compilation 12.0System Power Consumption Monitor1002003004005001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 1339 Joules per run.

Blender

Blender is an open-source 3D creation and modeling software project. This test is of Blender's Cycles benchmark with various sample files. GPU computing via OpenCL, NVIDIA OptiX, and NVIDIA CUDA is supported. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 2.92Blend File: Barbershop - Compute: CPU-OnlyEPYC 7543 + S8036GM2NE4080120160200SE +/- 0.24, N = 3195.95
MinAvgMaxEPYC 7543 + S8036GM2NE149929443725OpenBenchmarking.orgMegahertz, More Is BetterBlender 2.92CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE90.1185.1212.0OpenBenchmarking.orgWatts, Fewer Is BetterBlender 2.92CPU Power Consumption Monitor601201802403001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 617 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE54.063.670.0OpenBenchmarking.orgCelsius, Fewer Is BetterBlender 2.92CPU Temperature Monitor20406080100
MinAvgMaxEPYC 7543 + S8036GM2NE28.932.033.9OpenBenchmarking.orgCelsius, Fewer Is BetterBlender 2.92Drive Temperature (nvme0n1) Monitor1020304050
MinAvgMaxEPYC 7543 + S8036GM2NE32.032.535.0OpenBenchmarking.orgCelsius, Fewer Is BetterBlender 2.92Memory Temperature Monitor1020304050
MinAvgMaxEPYC 7543 + S8036GM2NE280.0326.5352.0OpenBenchmarking.orgWatts, Fewer Is BetterBlender 2.92System Power Consumption Monitor1002003004005001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 1197 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 7.32.2VGR Performance MetricEPYC 7543 + S8036GM2NE90K180K270K360K450K4148041. (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 -pthread -ldl -lm
MinAvgMaxEPYC 7543 + S8036GM2NE187329293129OpenBenchmarking.orgMegahertz, More Is BetterBRL-CAD 7.32.2CPU Peak Freq (Highest CPU Core Frequency) Monitor8001600240032004000
MinAvgMaxEPYC 7543 + S8036GM2NE50.566.369.5OpenBenchmarking.orgCelsius, Fewer Is BetterBRL-CAD 7.32.2CPU Temperature Monitor20406080100
OpenBenchmarking.orgCelsius, Fewer Is BetterBRL-CAD 7.32.2Drive Temperature (nvme0n1) MonitorEPYC 7543 + S8036GM2NE81624324034.85
MinAvgMaxEPYC 7543 + S8036GM2NE32.032.634.0OpenBenchmarking.orgCelsius, Fewer Is BetterBRL-CAD 7.32.2Memory Temperature Monitor1020304050
MinAvgMaxEPYC 7543 + S8036GM2NE184.0317.0336.0OpenBenchmarking.orgWatts, Fewer Is BetterBRL-CAD 7.32.2System Power Consumption Monitor80160240320400

Timed Node.js Compilation

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

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed Node.js Compilation 15.11Time To CompileEPYC 7543 + S8036GM2NE306090120150SE +/- 0.06, N = 3140.16
MinAvgMaxEPYC 7543 + S8036GM2NE318134753725OpenBenchmarking.orgMegahertz, More Is BetterTimed Node.js Compilation 15.11CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE106.1168.4207.1OpenBenchmarking.orgWatts, Fewer Is BetterTimed Node.js Compilation 15.11CPU Power Consumption Monitor601201802403001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 617 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE50.861.376.5OpenBenchmarking.orgCelsius, Fewer Is BetterTimed Node.js Compilation 15.11CPU Temperature Monitor20406080100
MinAvgMaxEPYC 7543 + S8036GM2NE33.935.937.9OpenBenchmarking.orgCelsius, Fewer Is BetterTimed Node.js Compilation 15.11Drive Temperature (nvme0n1) Monitor1122334455
MinAvgMaxEPYC 7543 + S8036GM2NE34.035.037.0OpenBenchmarking.orgCelsius, Fewer Is BetterTimed Node.js Compilation 15.11Memory Temperature Monitor1122334455
MinAvgMaxEPYC 7543 + S8036GM2NE184.0279.4352.0OpenBenchmarking.orgWatts, Fewer Is BetterTimed Node.js Compilation 15.11System Power Consumption Monitor1002003004005001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 1211 Joules per run.

Apache Cassandra

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

OpenBenchmarking.orgOp/s, More Is BetterApache Cassandra 4.0Test: WritesEPYC 7543 + S8036GM2NE60K120K180K240K300KSE +/- 2563.16, N = 3266295
MinAvgMaxEPYC 7543 + S8036GM2NE193533833714OpenBenchmarking.orgMegahertz, More Is BetterApache Cassandra 4.0CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE81.7174.3203.1OpenBenchmarking.orgWatts, Fewer Is BetterApache Cassandra 4.0CPU Power Consumption Monitor501001502002501. EPYC 7543 + S8036GM2NE: Approximate power consumption of 1394 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE52.863.173.0OpenBenchmarking.orgCelsius, Fewer Is BetterApache Cassandra 4.0CPU Temperature Monitor20406080100
MinAvgMaxEPYC 7543 + S8036GM2NE33.937.539.9OpenBenchmarking.orgCelsius, Fewer Is BetterApache Cassandra 4.0Drive Temperature (nvme0n1) Monitor1122334455
MinAvgMaxEPYC 7543 + S8036GM2NE33.034.436.0OpenBenchmarking.orgCelsius, Fewer Is BetterApache Cassandra 4.0Memory Temperature Monitor1020304050
MinAvgMaxEPYC 7543 + S8036GM2NE136.0270.1328.0OpenBenchmarking.orgWatts, Fewer Is BetterApache Cassandra 4.0System Power Consumption Monitor801602403204001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 2251 Joules per run.

AOM AV1

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

OpenBenchmarking.orgFrames Per Second, More Is BetterAOM AV1 3.1Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 4KEPYC 7543 + S8036GM2NE246810SE +/- 0.04, N = 37.481. (CXX) g++ options: -O3 -std=c++11 -U_FORTIFY_SOURCE -lm -lpthread
MinAvgMaxEPYC 7543 + S8036GM2NE210033843671OpenBenchmarking.orgMegahertz, More Is BetterAOM AV1 3.1CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE117.1134.7155.5OpenBenchmarking.orgWatts, Fewer Is BetterAOM AV1 3.1CPU Power Consumption Monitor40801201602001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 808 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE55.063.081.8OpenBenchmarking.orgCelsius, Fewer Is BetterAOM AV1 3.1CPU Temperature Monitor20406080100
OpenBenchmarking.orgCelsius, Fewer Is BetterAOM AV1 3.1Drive Temperature (nvme0n1) MonitorEPYC 7543 + S8036GM2NE71421283528.85
MinAvgMaxEPYC 7543 + S8036GM2NE35.036.038.0OpenBenchmarking.orgCelsius, Fewer Is BetterAOM AV1 3.1Memory Temperature Monitor1122334455
MinAvgMaxEPYC 7543 + S8036GM2NE152.0197.9232.0OpenBenchmarking.orgWatts, Fewer Is BetterAOM AV1 3.1System Power Consumption Monitor601201802403001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 1253 Joules per run.

SVT-AV1

This is a benchmark of the SVT-AV1 open-source video encoder/decoder. SVT-AV1 was originally developed by Intel as part of their Open Visual Cloud / Scalable Video Technology (SVT). Development of SVT-AV1 has since moved to the Alliance for Open Media as part of upstream AV1 development. SVT-AV1 is a CPU-based multi-threaded video encoder for the AV1 video format with a sample YUV video file. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 0.8.7Encoder Mode: Preset 4 - Input: Bosphorus 4KEPYC 7543 + S8036GM2NE0.36090.72181.08271.44361.8045SE +/- 0.002, N = 31.6041. (CXX) g++ options: -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq -pie
MinAvgMaxEPYC 7543 + S8036GM2NE355336303724OpenBenchmarking.orgMegahertz, More Is BetterSVT-AV1 0.8.7CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE115.7134.2141.3OpenBenchmarking.orgWatts, Fewer Is BetterSVT-AV1 0.8.7CPU Power Consumption Monitor40801201602001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 850 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE58.368.080.3OpenBenchmarking.orgCelsius, Fewer Is BetterSVT-AV1 0.8.7CPU Temperature Monitor20406080100
OpenBenchmarking.orgCelsius, Fewer Is BetterSVT-AV1 0.8.7Drive Temperature (nvme0n1) MonitorEPYC 7543 + S8036GM2NE71421283528.85
MinAvgMaxEPYC 7543 + S8036GM2NE34.034.735.0OpenBenchmarking.orgCelsius, Fewer Is BetterSVT-AV1 0.8.7Memory Temperature Monitor1020304050
MinAvgMaxEPYC 7543 + S8036GM2NE152.0200.4248.0OpenBenchmarking.orgWatts, Fewer Is BetterSVT-AV1 0.8.7System Power Consumption Monitor601201802403001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 1269 Joules per run.

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 0.8.7Encoder Mode: Preset 8 - Input: Bosphorus 4KEPYC 7543 + S8036GM2NE510152025SE +/- 0.16, N = 1021.201. (CXX) g++ options: -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq -pie
MinAvgMaxEPYC 7543 + S8036GM2NE179933763722OpenBenchmarking.orgMegahertz, More Is BetterSVT-AV1 0.8.7CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE126.1140.7154.2OpenBenchmarking.orgWatts, Fewer Is BetterSVT-AV1 0.8.7CPU Power Consumption Monitor40801201602001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 253 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE54.064.176.5OpenBenchmarking.orgCelsius, Fewer Is BetterSVT-AV1 0.8.7CPU Temperature Monitor20406080100
OpenBenchmarking.orgCelsius, Fewer Is BetterSVT-AV1 0.8.7Drive Temperature (nvme0n1) MonitorEPYC 7543 + S8036GM2NE71421283528.85
MinAvgMaxEPYC 7543 + S8036GM2NE34.034.937.0OpenBenchmarking.orgCelsius, Fewer Is BetterSVT-AV1 0.8.7Memory Temperature Monitor1122334455
MinAvgMaxEPYC 7543 + S8036GM2NE144.0212.2240.0OpenBenchmarking.orgWatts, Fewer Is BetterSVT-AV1 0.8.7System Power Consumption Monitor601201802403001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 403 Joules per run.

AOM AV1

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

OpenBenchmarking.orgFrames Per Second, More Is BetterAOM AV1 3.1Encoder Mode: Speed 8 Realtime - Input: Bosphorus 4KEPYC 7543 + S8036GM2NE816243240SE +/- 0.32, N = 1532.561. (CXX) g++ options: -O3 -std=c++11 -U_FORTIFY_SOURCE -lm -lpthread
MinAvgMaxEPYC 7543 + S8036GM2NE149930843657OpenBenchmarking.orgMegahertz, More Is BetterAOM AV1 3.1CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE101.2110.6128.3OpenBenchmarking.orgWatts, Fewer Is BetterAOM AV1 3.1CPU Power Consumption Monitor40801201602001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 147 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE60.865.370.8OpenBenchmarking.orgCelsius, Fewer Is BetterAOM AV1 3.1CPU Temperature Monitor20406080100
MinAvgMaxEPYC 7543 + S8036GM2NE28.929.831.9OpenBenchmarking.orgCelsius, Fewer Is BetterAOM AV1 3.1Drive Temperature (nvme0n1) Monitor918273645
MinAvgMaxEPYC 7543 + S8036GM2NE37.037.938.0OpenBenchmarking.orgCelsius, Fewer Is BetterAOM AV1 3.1Memory Temperature Monitor1122334455
MinAvgMaxEPYC 7543 + S8036GM2NE136.0163.4184.0OpenBenchmarking.orgWatts, Fewer Is BetterAOM AV1 3.1System Power Consumption Monitor501001502002501. EPYC 7543 + S8036GM2NE: Approximate power consumption of 229 Joules per run.

NCNN

NCNN is a high performance neural network inference framework optimized for mobile and other platforms developed by Tencent. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: regnety_400mEPYC 7543 + S8036GM2NE510152025SE +/- 0.08, N = 319.90MIN: 19.41 / MAX: 52.711. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: squeezenet_ssdEPYC 7543 + S8036GM2NE510152025SE +/- 0.27, N = 319.41MIN: 18.56 / MAX: 38.461. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: yolov4-tinyEPYC 7543 + S8036GM2NE612182430SE +/- 0.40, N = 327.20MIN: 25.93 / MAX: 39.621. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: resnet50EPYC 7543 + S8036GM2NE612182430SE +/- 0.54, N = 326.07MIN: 24.7 / MAX: 184.991. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: alexnetEPYC 7543 + S8036GM2NE246810SE +/- 0.07, N = 38.70MIN: 8.31 / MAX: 20.561. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: resnet18EPYC 7543 + S8036GM2NE3691215SE +/- 0.02, N = 313.35MIN: 12.95 / MAX: 23.691. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: vgg16EPYC 7543 + S8036GM2NE1428425670SE +/- 2.66, N = 364.55MIN: 38.52 / MAX: 1172.351. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: googlenetEPYC 7543 + S8036GM2NE48121620SE +/- 0.17, N = 318.19MIN: 16.21 / MAX: 229.31. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: blazefaceEPYC 7543 + S8036GM2NE0.8461.6922.5383.3844.23SE +/- 0.24, N = 33.76MIN: 3.36 / MAX: 207.211. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: efficientnet-b0EPYC 7543 + S8036GM2NE3691215SE +/- 0.12, N = 39.61MIN: 9.28 / MAX: 19.181. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: mnasnetEPYC 7543 + S8036GM2NE246810SE +/- 0.05, N = 37.17MIN: 6.78 / MAX: 36.71. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: shufflenet-v2EPYC 7543 + S8036GM2NE246810SE +/- 0.05, N = 37.53MIN: 7.32 / MAX: 27.391. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU-v3-v3 - Model: mobilenet-v3EPYC 7543 + S8036GM2NE246810SE +/- 0.02, N = 37.32MIN: 7.19 / MAX: 8.991. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU-v2-v2 - Model: mobilenet-v2EPYC 7543 + S8036GM2NE246810SE +/- 0.09, N = 38.06MIN: 7.55 / MAX: 47.271. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20210720Target: CPU - Model: mobilenetEPYC 7543 + S8036GM2NE510152025SE +/- 0.25, N = 319.69MIN: 18.02 / MAX: 54.191. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread -pthread

Mobile Neural Network

MNN is the Mobile Neural Network as a highly efficient, lightweight deep learning framework developed by Alibaba. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 1.2Model: inception-v3EPYC 7543 + S8036GM2NE612182430SE +/- 0.09, N = 325.05MIN: 24.34 / MAX: 111.021. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl
MinAvgMaxEPYC 7543 + S8036GM2NE179933313578OpenBenchmarking.orgMegahertz, More Is BetterMobile Neural Network 1.2CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE130.0193.6208.9OpenBenchmarking.orgWatts, Fewer Is BetterMobile Neural Network 1.2CPU Power Consumption Monitor601201802403001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 1033 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE52.364.468.3OpenBenchmarking.orgCelsius, Fewer Is BetterMobile Neural Network 1.2CPU Temperature Monitor20406080100
MinAvgMaxEPYC 7543 + S8036GM2NE33.934.734.9OpenBenchmarking.orgCelsius, Fewer Is BetterMobile Neural Network 1.2Drive Temperature (nvme0n1) Monitor1020304050
MinAvgMaxEPYC 7543 + S8036GM2NE32.032.834.0OpenBenchmarking.orgCelsius, Fewer Is BetterMobile Neural Network 1.2Memory Temperature Monitor1020304050
MinAvgMaxEPYC 7543 + S8036GM2NE288.0295.5296.0OpenBenchmarking.orgWatts, Fewer Is BetterMobile Neural Network 1.2System Power Consumption Monitor701402102803501. EPYC 7543 + S8036GM2NE: Approximate power consumption of 1675 Joules per run.

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 1.2Model: mobilenet-v1-1.0EPYC 7543 + S8036GM2NE0.68021.36042.04062.72083.401SE +/- 0.277, N = 33.023MIN: 2.58 / MAX: 3.691. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 1.2Model: MobileNetV2_224EPYC 7543 + S8036GM2NE1.01772.03543.05314.07085.0885SE +/- 0.090, N = 34.523MIN: 4.29 / MAX: 4.841. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 1.2Model: SqueezeNetV1.0EPYC 7543 + S8036GM2NE246810SE +/- 0.096, N = 36.289MIN: 5.75 / MAX: 35.931. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 1.2Model: resnet-v2-50EPYC 7543 + S8036GM2NE510152025SE +/- 0.15, N = 320.73MIN: 20.23 / MAX: 74.921. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 1.2Model: squeezenetv1.1EPYC 7543 + S8036GM2NE0.9591.9182.8773.8364.795SE +/- 0.063, N = 34.262MIN: 4.12 / MAX: 4.561. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 1.2Model: mobilenetV3EPYC 7543 + S8036GM2NE0.53641.07281.60922.14562.682SE +/- 0.006, N = 32.384MIN: 2.33 / MAX: 2.451. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

PyPerformance

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

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: raytraceEPYC 7543 + S8036GM2NE100200300400500SE +/- 0.33, N = 3439
MinAvgMaxEPYC 7543 + S8036GM2NE372237253729OpenBenchmarking.orgMegahertz, More Is BetterPyPerformance 1.0.0CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE88.497.3101.2OpenBenchmarking.orgWatts, Fewer Is BetterPyPerformance 1.0.0CPU Power Consumption Monitor204060801001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 454 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE59.866.672.5OpenBenchmarking.orgCelsius, Fewer Is BetterPyPerformance 1.0.0CPU Temperature Monitor20406080100
OpenBenchmarking.orgCelsius, Fewer Is BetterPyPerformance 1.0.0Drive Temperature (nvme0n1) MonitorEPYC 7543 + S8036GM2NE71421283527.85
MinAvgMaxEPYC 7543 + S8036GM2NE36.036.637.0OpenBenchmarking.orgCelsius, Fewer Is BetterPyPerformance 1.0.0Memory Temperature Monitor1122334455
MinAvgMaxEPYC 7543 + S8036GM2NE136.0150.9152.0OpenBenchmarking.orgWatts, Fewer Is BetterPyPerformance 1.0.0System Power Consumption Monitor40801201602001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 755 Joules per run.

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: python_startupEPYC 7543 + S8036GM2NE246810SE +/- 0.00, N = 37.42
MinAvgMaxEPYC 7543 + S8036GM2NE179832783727OpenBenchmarking.orgMegahertz, More Is BetterPyPerformance 1.0.0CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE86.796.1102.9OpenBenchmarking.orgWatts, Fewer Is BetterPyPerformance 1.0.0CPU Power Consumption Monitor204060801001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 384 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE57.362.167.3OpenBenchmarking.orgCelsius, Fewer Is BetterPyPerformance 1.0.0CPU Temperature Monitor20406080100
OpenBenchmarking.orgCelsius, Fewer Is BetterPyPerformance 1.0.0Drive Temperature (nvme0n1) MonitorEPYC 7543 + S8036GM2NE71421283527.85
MinAvgMaxEPYC 7543 + S8036GM2NE36.036.137.0OpenBenchmarking.orgCelsius, Fewer Is BetterPyPerformance 1.0.0Memory Temperature Monitor1122334455
MinAvgMaxEPYC 7543 + S8036GM2NE136.0150.2152.0OpenBenchmarking.orgWatts, Fewer Is BetterPyPerformance 1.0.0System Power Consumption Monitor40801201602001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 651 Joules per run.

Intel Open Image Denoise

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

OpenBenchmarking.orgImages / Sec, More Is BetterIntel Open Image Denoise 1.4.0Run: RTLightmap.hdr.4096x4096EPYC 7543 + S8036GM2NE0.1080.2160.3240.4320.54SE +/- 0.00, N = 30.48
MinAvgMaxEPYC 7543 + S8036GM2NE179929913725OpenBenchmarking.orgMegahertz, More Is BetterIntel Open Image Denoise 1.4.0CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE88.4168.7223.7OpenBenchmarking.orgWatts, Fewer Is BetterIntel Open Image Denoise 1.4.0CPU Power Consumption Monitor601201802403001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 562 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE50.061.977.5OpenBenchmarking.orgCelsius, Fewer Is BetterIntel Open Image Denoise 1.4.0CPU Temperature Monitor20406080100
OpenBenchmarking.orgCelsius, Fewer Is BetterIntel Open Image Denoise 1.4.0Drive Temperature (nvme0n1) MonitorEPYC 7543 + S8036GM2NE71421283530.85
MinAvgMaxEPYC 7543 + S8036GM2NE33.034.836.0OpenBenchmarking.orgCelsius, Fewer Is BetterIntel Open Image Denoise 1.4.0Memory Temperature Monitor1020304050
MinAvgMaxEPYC 7543 + S8036GM2NE136.0242.2320.0OpenBenchmarking.orgWatts, Fewer Is BetterIntel Open Image Denoise 1.4.0System Power Consumption Monitor801602403204001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 888 Joules per run.

PyPerformance

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

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: 2to3EPYC 7543 + S8036GM2NE70140210280350299
MinAvgMaxEPYC 7543 + S8036GM2NE179735013725OpenBenchmarking.orgMegahertz, More Is BetterPyPerformance 1.0.0CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE92.996.9100.1OpenBenchmarking.orgWatts, Fewer Is BetterPyPerformance 1.0.0CPU Power Consumption Monitor20406080100
MinAvgMaxEPYC 7543 + S8036GM2NE54.862.870.8OpenBenchmarking.orgCelsius, Fewer Is BetterPyPerformance 1.0.0CPU Temperature Monitor20406080100
OpenBenchmarking.orgCelsius, Fewer Is BetterPyPerformance 1.0.0Drive Temperature (nvme0n1) MonitorEPYC 7543 + S8036GM2NE71421283528.85
OpenBenchmarking.orgCelsius, Fewer Is BetterPyPerformance 1.0.0Memory Temperature MonitorEPYC 7543 + S8036GM2NE81624324036.00
MinAvgMaxEPYC 7543 + S8036GM2NE136.0150.0152.0OpenBenchmarking.orgWatts, Fewer Is BetterPyPerformance 1.0.0System Power Consumption Monitor4080120160200

Facebook RocksDB

This is a benchmark of Facebook's RocksDB as an embeddable persistent key-value store for fast storage based on Google's LevelDB. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgOp/s, More Is BetterFacebook RocksDB 6.22.1Test: Update RandomEPYC 7543 + S8036GM2NE200K400K600K800K1000KSE +/- 1499.36, N = 38018481. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread
MinAvgMaxEPYC 7543 + S8036GM2NE179932923492OpenBenchmarking.orgMegahertz, More Is BetterFacebook RocksDB 6.22.1CPU Peak Freq (Highest CPU Core Frequency) Monitor8001600240032004000
MinAvgMaxEPYC 7543 + S8036GM2NE143.2191.5211.6OpenBenchmarking.orgWatts, Fewer Is BetterFacebook RocksDB 6.22.1CPU Power Consumption Monitor601201802403001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 638 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE51.564.570.3OpenBenchmarking.orgCelsius, Fewer Is BetterFacebook RocksDB 6.22.1CPU Temperature Monitor20406080100
MinAvgMaxEPYC 7543 + S8036GM2NE33.935.235.9OpenBenchmarking.orgCelsius, Fewer Is BetterFacebook RocksDB 6.22.1Drive Temperature (nvme0n1) Monitor1020304050
OpenBenchmarking.orgCelsius, Fewer Is BetterFacebook RocksDB 6.22.1Memory Temperature MonitorEPYC 7543 + S8036GM2NE81624324033.00
MinAvgMaxEPYC 7543 + S8036GM2NE304.0312.7320.0OpenBenchmarking.orgWatts, Fewer Is BetterFacebook RocksDB 6.22.1System Power Consumption Monitor801602403204001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 1147 Joules per run.

OpenBenchmarking.orgOp/s, More Is BetterFacebook RocksDB 6.22.1Test: Read Random Write RandomEPYC 7543 + S8036GM2NE700K1400K2100K2800K3500KSE +/- 23275.30, N = 330480071. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread
MinAvgMaxEPYC 7543 + S8036GM2NE239933713522OpenBenchmarking.orgMegahertz, More Is BetterFacebook RocksDB 6.22.1CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE105.4190.6215.6OpenBenchmarking.orgWatts, Fewer Is BetterFacebook RocksDB 6.22.1CPU Power Consumption Monitor601201802403001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 572 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE60.068.173.0OpenBenchmarking.orgCelsius, Fewer Is BetterFacebook RocksDB 6.22.1CPU Temperature Monitor20406080100
MinAvgMaxEPYC 7543 + S8036GM2NE32.933.833.9OpenBenchmarking.orgCelsius, Fewer Is BetterFacebook RocksDB 6.22.1Drive Temperature (nvme0n1) Monitor1020304050
MinAvgMaxEPYC 7543 + S8036GM2NE33.033.934.0OpenBenchmarking.orgCelsius, Fewer Is BetterFacebook RocksDB 6.22.1Memory Temperature Monitor1020304050
MinAvgMaxEPYC 7543 + S8036GM2NE152.0298.4336.0OpenBenchmarking.orgWatts, Fewer Is BetterFacebook RocksDB 6.22.1System Power Consumption Monitor801602403204001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 995 Joules per run.

OpenBenchmarking.orgOp/s, More Is BetterFacebook RocksDB 6.22.1Test: Random FillEPYC 7543 + S8036GM2NE200K400K600K800K1000KSE +/- 6995.49, N = 39880451. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread
MinAvgMaxEPYC 7543 + S8036GM2NE180033203601OpenBenchmarking.orgMegahertz, More Is BetterFacebook RocksDB 6.22.1CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE169.0194.4209.6OpenBenchmarking.orgWatts, Fewer Is BetterFacebook RocksDB 6.22.1CPU Power Consumption Monitor601201802403001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 648 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE55.066.876.5OpenBenchmarking.orgCelsius, Fewer Is BetterFacebook RocksDB 6.22.1CPU Temperature Monitor20406080100
MinAvgMaxEPYC 7543 + S8036GM2NE29.932.735.9OpenBenchmarking.orgCelsius, Fewer Is BetterFacebook RocksDB 6.22.1Drive Temperature (nvme0n1) Monitor1020304050
MinAvgMaxEPYC 7543 + S8036GM2NE32.033.436.0OpenBenchmarking.orgCelsius, Fewer Is BetterFacebook RocksDB 6.22.1Memory Temperature Monitor1020304050
MinAvgMaxEPYC 7543 + S8036GM2NE192.0310.5344.0OpenBenchmarking.orgWatts, Fewer Is BetterFacebook RocksDB 6.22.1System Power Consumption Monitor801602403204001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 1139 Joules per run.

OpenBenchmarking.orgOp/s, More Is BetterFacebook RocksDB 6.22.1Test: Read While WritingEPYC 7543 + S8036GM2NE1.3M2.6M3.9M5.2M6.5MSE +/- 80315.14, N = 358966181. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread
MinAvgMaxEPYC 7543 + S8036GM2NE180031563416OpenBenchmarking.orgMegahertz, More Is BetterFacebook RocksDB 6.22.1CPU Peak Freq (Highest CPU Core Frequency) Monitor8001600240032004000
MinAvgMaxEPYC 7543 + S8036GM2NE163.7196.9213.4OpenBenchmarking.orgWatts, Fewer Is BetterFacebook RocksDB 6.22.1CPU Power Consumption Monitor601201802403001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 525 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE48.565.172.5OpenBenchmarking.orgCelsius, Fewer Is BetterFacebook RocksDB 6.22.1CPU Temperature Monitor20406080100
MinAvgMaxEPYC 7543 + S8036GM2NE33.935.036.9OpenBenchmarking.orgCelsius, Fewer Is BetterFacebook RocksDB 6.22.1Drive Temperature (nvme0n1) Monitor1020304050
MinAvgMaxEPYC 7543 + S8036GM2NE33.033.834.0OpenBenchmarking.orgCelsius, Fewer Is BetterFacebook RocksDB 6.22.1Memory Temperature Monitor1020304050
MinAvgMaxEPYC 7543 + S8036GM2NE296.0300.0304.0OpenBenchmarking.orgWatts, Fewer Is BetterFacebook RocksDB 6.22.1System Power Consumption Monitor801602403204001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 800 Joules per run.

AOM AV1

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

OpenBenchmarking.orgFrames Per Second, More Is BetterAOM AV1 3.1Encoder Mode: Speed 6 Realtime - Input: Bosphorus 4KEPYC 7543 + S8036GM2NE3691215SE +/- 0.09, N = 310.511. (CXX) g++ options: -O3 -std=c++11 -U_FORTIFY_SOURCE -lm -lpthread
MinAvgMaxEPYC 7543 + S8036GM2NE179723713609OpenBenchmarking.orgMegahertz, More Is BetterAOM AV1 3.1CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE99.6108.1113.0OpenBenchmarking.orgWatts, Fewer Is BetterAOM AV1 3.1CPU Power Consumption Monitor40801201602001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 396 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE62.364.969.0OpenBenchmarking.orgCelsius, Fewer Is BetterAOM AV1 3.1CPU Temperature Monitor20406080100
OpenBenchmarking.orgCelsius, Fewer Is BetterAOM AV1 3.1Drive Temperature (nvme0n1) MonitorEPYC 7543 + S8036GM2NE71421283528.85
OpenBenchmarking.orgCelsius, Fewer Is BetterAOM AV1 3.1Memory Temperature MonitorEPYC 7543 + S8036GM2NE91827364538.00
MinAvgMaxEPYC 7543 + S8036GM2NE152.0160.0168.0OpenBenchmarking.orgWatts, Fewer Is BetterAOM AV1 3.1System Power Consumption Monitor501001502002501. EPYC 7543 + S8036GM2NE: Approximate power consumption of 587 Joules per run.

Timed Godot Game Engine Compilation

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

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed Godot Game Engine Compilation 3.2.3Time To CompileEPYC 7543 + S8036GM2NE1224364860SE +/- 0.32, N = 354.07
MinAvgMaxEPYC 7543 + S8036GM2NE326435663725OpenBenchmarking.orgMegahertz, More Is BetterTimed Godot Game Engine Compilation 3.2.3CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE98.6157.1192.8OpenBenchmarking.orgWatts, Fewer Is BetterTimed Godot Game Engine Compilation 3.2.3CPU Power Consumption Monitor501001502002501. EPYC 7543 + S8036GM2NE: Approximate power consumption of 471 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE54.559.863.8OpenBenchmarking.orgCelsius, Fewer Is BetterTimed Godot Game Engine Compilation 3.2.3CPU Temperature Monitor20406080100
MinAvgMaxEPYC 7543 + S8036GM2NE31.933.033.9OpenBenchmarking.orgCelsius, Fewer Is BetterTimed Godot Game Engine Compilation 3.2.3Drive Temperature (nvme0n1) Monitor1020304050
MinAvgMaxEPYC 7543 + S8036GM2NE34.035.236.0OpenBenchmarking.orgCelsius, Fewer Is BetterTimed Godot Game Engine Compilation 3.2.3Memory Temperature Monitor1020304050
MinAvgMaxEPYC 7543 + S8036GM2NE136.0191.1248.0OpenBenchmarking.orgWatts, Fewer Is BetterTimed Godot Game Engine Compilation 3.2.3System Power Consumption Monitor601201802403001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 573 Joules per run.

NAS Parallel Benchmarks

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

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: SP.CEPYC 7543 + S8036GM2NE7K14K21K28K35KSE +/- 42.93, N = 332713.751. (F9X) gfortran options: -O3 -march=native -pthread -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -ldl -levent_core -levent_pthreads -lutil -lm -lrt -lz 2. Open MPI 4.1.0
MinAvgMaxEPYC 7543 + S8036GM2NE345134583474OpenBenchmarking.orgMegahertz, More Is BetterNAS Parallel Benchmarks 3.4CPU Peak Freq (Highest CPU Core Frequency) Monitor8001600240032004000
MinAvgMaxEPYC 7543 + S8036GM2NE145.2166.5186.2OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor501001502002501. EPYC 7543 + S8036GM2NE: Approximate power consumption of 444 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE47.565.175.5OpenBenchmarking.orgCelsius, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Temperature Monitor20406080100
OpenBenchmarking.orgCelsius, Fewer Is BetterNAS Parallel Benchmarks 3.4Drive Temperature (nvme0n1) MonitorEPYC 7543 + S8036GM2NE71421283530.85
MinAvgMaxEPYC 7543 + S8036GM2NE33.035.737.0OpenBenchmarking.orgCelsius, Fewer Is BetterNAS Parallel Benchmarks 3.4Memory Temperature Monitor1122334455
MinAvgMaxEPYC 7543 + S8036GM2NE248.0267.6312.0OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4System Power Consumption Monitor801602403204001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 803 Joules per run.

VP9 libvpx Encoding

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

OpenBenchmarking.orgFrames Per Second, More Is BetterVP9 libvpx Encoding 1.10.0Speed: Speed 5 - Input: Bosphorus 4KEPYC 7543 + S8036GM2NE48121620SE +/- 0.19, N = 314.761. (CXX) g++ options: -m64 -lm -lpthread -O3 -fPIC -U_FORTIFY_SOURCE -std=gnu++11
MinAvgMaxEPYC 7543 + S8036GM2NE364236763728OpenBenchmarking.orgMegahertz, More Is BetterVP9 libvpx Encoding 1.10.0CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE87.7119.4130.4OpenBenchmarking.orgWatts, Fewer Is BetterVP9 libvpx Encoding 1.10.0CPU Power Consumption Monitor40801201602001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 358 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE64.570.273.3OpenBenchmarking.orgCelsius, Fewer Is BetterVP9 libvpx Encoding 1.10.0CPU Temperature Monitor20406080100
MinAvgMaxEPYC 7543 + S8036GM2NE32.934.735.9OpenBenchmarking.orgCelsius, Fewer Is BetterVP9 libvpx Encoding 1.10.0Drive Temperature (nvme0n1) Monitor1020304050
OpenBenchmarking.orgCelsius, Fewer Is BetterVP9 libvpx Encoding 1.10.0Memory Temperature MonitorEPYC 7543 + S8036GM2NE91827364537.00
MinAvgMaxEPYC 7543 + S8036GM2NE136.0173.3184.0OpenBenchmarking.orgWatts, Fewer Is BetterVP9 libvpx Encoding 1.10.0System Power Consumption Monitor501001502002501. EPYC 7543 + S8036GM2NE: Approximate power consumption of 520 Joules per run.

PyPerformance

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

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: goEPYC 7543 + S8036GM2NE50100150200250234
MinAvgMaxEPYC 7543 + S8036GM2NE179734853729OpenBenchmarking.orgMegahertz, More Is BetterPyPerformance 1.0.0CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE86.496.3101.1OpenBenchmarking.orgWatts, Fewer Is BetterPyPerformance 1.0.0CPU Power Consumption Monitor20406080100
MinAvgMaxEPYC 7543 + S8036GM2NE57.065.571.5OpenBenchmarking.orgCelsius, Fewer Is BetterPyPerformance 1.0.0CPU Temperature Monitor20406080100
OpenBenchmarking.orgCelsius, Fewer Is BetterPyPerformance 1.0.0Drive Temperature (nvme0n1) MonitorEPYC 7543 + S8036GM2NE71421283527.85
MinAvgMaxEPYC 7543 + S8036GM2NE36.036.437.0OpenBenchmarking.orgCelsius, Fewer Is BetterPyPerformance 1.0.0Memory Temperature Monitor1122334455
MinAvgMaxEPYC 7543 + S8036GM2NE136.0150.0152.0OpenBenchmarking.orgWatts, Fewer Is BetterPyPerformance 1.0.0System Power Consumption Monitor4080120160200

Timed GDB GNU Debugger Compilation

This test times how long it takes to build the GNU Debugger (GDB) in a default configuration. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed GDB GNU Debugger Compilation 10.2Time To CompileEPYC 7543 + S8036GM2NE918273645SE +/- 0.01, N = 339.62
MinAvgMaxEPYC 7543 + S8036GM2NE334936483725OpenBenchmarking.orgMegahertz, More Is BetterTimed GDB GNU Debugger Compilation 10.2CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE34.034.936.0OpenBenchmarking.orgCelsius, Fewer Is BetterTimed GDB GNU Debugger Compilation 10.2Memory Temperature Monitor1020304050
MinAvgMaxEPYC 7543 + S8036GM2NE144.0193.1296.0OpenBenchmarking.orgWatts, Fewer Is BetterTimed GDB GNU Debugger Compilation 10.2System Power Consumption Monitor701402102803501. EPYC 7543 + S8036GM2NE: Approximate power consumption of 451 Joules per run.

PyPerformance

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

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: django_templateEPYC 7543 + S8036GM2NE1020304050SE +/- 0.00, N = 344.7
MinAvgMaxEPYC 7543 + S8036GM2NE371537233730OpenBenchmarking.orgMegahertz, More Is BetterPyPerformance 1.0.0CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE56.364.370.3OpenBenchmarking.orgCelsius, Fewer Is BetterPyPerformance 1.0.0CPU Temperature Monitor20406080100
OpenBenchmarking.orgCelsius, Fewer Is BetterPyPerformance 1.0.0Drive Temperature (nvme0n1) MonitorEPYC 7543 + S8036GM2NE71421283527.85
OpenBenchmarking.orgCelsius, Fewer Is BetterPyPerformance 1.0.0Memory Temperature MonitorEPYC 7543 + S8036GM2NE81624324036.00
MinAvgMaxEPYC 7543 + S8036GM2NE136.0149.7152.0OpenBenchmarking.orgWatts, Fewer Is BetterPyPerformance 1.0.0System Power Consumption Monitor40801201602001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 349 Joules per run.

NAS Parallel Benchmarks

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

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: BT.CEPYC 7543 + S8036GM2NE20K40K60K80K100KSE +/- 27.84, N = 387355.311. (F9X) gfortran options: -O3 -march=native -pthread -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -ldl -levent_core -levent_pthreads -lutil -lm -lrt -lz 2. Open MPI 4.1.0
MinAvgMaxEPYC 7543 + S8036GM2NE344734843659OpenBenchmarking.orgMegahertz, More Is BetterNAS Parallel Benchmarks 3.4CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE86.7174.1201.0OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Power Consumption Monitor501001502002501. EPYC 7543 + S8036GM2NE: Approximate power consumption of 406 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE54.567.876.5OpenBenchmarking.orgCelsius, Fewer Is BetterNAS Parallel Benchmarks 3.4CPU Temperature Monitor20406080100
OpenBenchmarking.orgCelsius, Fewer Is BetterNAS Parallel Benchmarks 3.4Drive Temperature (nvme0n1) MonitorEPYC 7543 + S8036GM2NE71421283528.85
MinAvgMaxEPYC 7543 + S8036GM2NE35.036.037.0OpenBenchmarking.orgCelsius, Fewer Is BetterNAS Parallel Benchmarks 3.4Memory Temperature Monitor1122334455
MinAvgMaxEPYC 7543 + S8036GM2NE136.0281.1336.0OpenBenchmarking.orgWatts, Fewer Is BetterNAS Parallel Benchmarks 3.4System Power Consumption Monitor801602403204001. EPYC 7543 + S8036GM2NE: Approximate power consumption of 656 Joules per run.

Timed Linux Kernel Compilation

This test times how long it takes to build the Linux kernel in a default configuration (defconfig) for the architecture being tested. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed Linux Kernel Compilation 5.10.20Time To CompileEPYC 7543 + S8036GM2NE816243240SE +/- 0.37, N = 332.56

PyPerformance

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

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: regex_compileEPYC 7543 + S8036GM2NE306090120150155
MinAvgMaxEPYC 7543 + S8036GM2NE179931743725OpenBenchmarking.orgMegahertz, More Is BetterPyPerformance 1.0.0CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE83.894.7100.4OpenBenchmarking.orgWatts, Fewer Is BetterPyPerformance 1.0.0CPU Power Consumption Monitor20406080100
MinAvgMaxEPYC 7543 + S8036GM2NE36.036.137.0OpenBenchmarking.orgCelsius, Fewer Is BetterPyPerformance 1.0.0Memory Temperature Monitor1122334455
MinAvgMaxEPYC 7543 + S8036GM2NE136.0149.7152.0OpenBenchmarking.orgWatts, Fewer Is BetterPyPerformance 1.0.0System Power Consumption Monitor4080120160200

Intel Open Image Denoise

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

OpenBenchmarking.orgImages / Sec, More Is BetterIntel Open Image Denoise 1.4.0Run: RT.hdr_alb_nrm.3840x2160EPYC 7543 + S8036GM2NE0.21380.42760.64140.85521.069SE +/- 0.00, N = 30.95
MinAvgMaxEPYC 7543 + S8036GM2NE179928473723OpenBenchmarking.orgMegahertz, More Is BetterIntel Open Image Denoise 1.4.0CPU Peak Freq (Highest CPU Core Frequency) Monitor10002000300040005000
MinAvgMaxEPYC 7543 + S8036GM2NE88.4153.1203.0OpenBenchmarking.orgWatts, Fewer Is BetterIntel Open Image Denoise 1.4.0CPU Power Consumption Monitor501001502002501. EPYC 7543 + S8036GM2NE: Approximate power consumption of 357 Joules per run.
MinAvgMaxEPYC 7543 + S8036GM2NE35.035.637.0OpenBenchmarking.orgCelsius, Fewer Is BetterIntel Open Image Denoise 1.4.0Memory Temperature Monitor1122334455
MinAvgMaxEPYC 7543 + S8036GM2NE144.0206.9280.0OpenBenchmarking.orgWatts, Fewer Is BetterIntel Open Image Denoise 1.4.0System Power Consumption Monitor701402102803501. EPYC 7543 + S8036GM2NE: Approximate power consumption of 483 Joules per run.

PHPBench

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

OpenBenchmarking.orgScore, More Is BetterPHPBench 0.8.1PHP Benchmark SuiteEPYC 7543 + S8036GM2NE140K280K420K560K700KSE +/- 5410.67, N = 3655577

PyPerformance

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

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: crypto_pyaesEPYC 7543 + S8036GM2NE20406080100SE +/- 0.06, N = 399.6

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.orgSeconds, Fewer Is BetterASTC Encoder 3.0Preset: ExhaustiveEPYC 7543 + S8036GM2NE612182430SE +/- 0.00, N = 323.471. (CXX) g++ options: -O3 -flto -pthread

PyPerformance

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

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: pathlibEPYC 7543 + S8036GM2NE4812162016

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: pickle_pure_pythonEPYC 7543 + S8036GM2NE90180270360450407

Stockfish

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

OpenBenchmarking.orgNodes Per Second, More Is BetterStockfish 13Total TimeEPYC 7543 + S8036GM2NE20M40M60M80M100MSE +/- 993882.73, N = 3892083481. (CXX) g++ options: -lgcov -m64 -lpthread -fno-exceptions -std=c++17 -fprofile-use -fno-peel-loops -fno-tracer -pedantic -O3 -msse -msse3 -mpopcnt -mavx2 -msse4.1 -mssse3 -msse2 -mbmi2 -flto -flto=jobserver

NAS Parallel Benchmarks

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

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: IS.DEPYC 7543 + S8036GM2NE400800120016002000SE +/- 15.04, N = 31760.851. (F9X) gfortran options: -O3 -march=native -pthread -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -ldl -levent_core -levent_pthreads -lutil -lm -lrt -lz 2. Open MPI 4.1.0

PyPerformance

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

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: json_loadsEPYC 7543 + S8036GM2NE510152025SE +/- 0.00, N = 322.5

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: nbodyEPYC 7543 + S8036GM2NE306090120150115

Timed FFmpeg Compilation

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

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed FFmpeg Compilation 4.4Time To CompileEPYC 7543 + S8036GM2NE510152025SE +/- 0.05, N = 321.59

PyBench

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

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyBench 2018-02-16Total For Average Test TimesEPYC 7543 + S8036GM2NE2004006008001000SE +/- 4.33, N = 3932

PyPerformance

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

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: chaosEPYC 7543 + S8036GM2NE20406080100104

TNN

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

OpenBenchmarking.orgms, Fewer Is BetterTNN 0.3Target: CPU - Model: MobileNet v2EPYC 7543 + S8036GM2NE60120180240300SE +/- 0.86, N = 3293.17MIN: 286.01 / MAX: 355.61. (CXX) g++ options: -fopenmp -pthread -fvisibility=hidden -fvisibility=default -O3 -rdynamic -ldl

AOM AV1

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

OpenBenchmarking.orgFrames Per Second, More Is BetterAOM AV1 3.1Encoder Mode: Speed 9 Realtime - Input: Bosphorus 4KEPYC 7543 + S8036GM2NE1020304050SE +/- 0.47, N = 445.741. (CXX) g++ options: -O3 -std=c++11 -U_FORTIFY_SOURCE -lm -lpthread

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 7543 + S8036GM2NE20K40K60K80K100KSE +/- 329.14, N = 3106020.341. (F9X) gfortran options: -O3 -march=native -pthread -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -ldl -levent_core -levent_pthreads -lutil -lm -lrt -lz 2. Open MPI 4.1.0

Redis

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

OpenBenchmarking.orgRequests Per Second, More Is BetterRedis 6.0.9Test: SETEPYC 7543 + S8036GM2NE400K800K1200K1600K2000KSE +/- 16224.31, N = 42039663.441. (CXX) g++ options: -MM -MT -g3 -fvisibility=hidden -O3

PyPerformance

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

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: floatEPYC 7543 + S8036GM2NE20406080100104

TNN

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

OpenBenchmarking.orgms, Fewer Is BetterTNN 0.3Target: CPU - Model: SqueezeNet v1.1EPYC 7543 + S8036GM2NE60120180240300SE +/- 0.13, N = 3271.11MIN: 270.69 / MAX: 272.931. (CXX) g++ options: -fopenmp -pthread -fvisibility=hidden -fvisibility=default -O3 -rdynamic -ldl

Redis

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

OpenBenchmarking.orgRequests Per Second, More Is BetterRedis 6.0.9Test: GETEPYC 7543 + S8036GM2NE500K1000K1500K2000K2500KSE +/- 25082.91, N = 42496247.631. (CXX) g++ options: -MM -MT -g3 -fvisibility=hidden -O3

SVT-VP9

This is a test of the Intel Open Visual Cloud Scalable Video Technology SVT-VP9 CPU-based multi-threaded video encoder for the VP9 video format with a sample YUV input video file. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-VP9 0.3Tuning: Visual Quality Optimized - Input: Bosphorus 1080pEPYC 7543 + S8036GM2NE50100150200250SE +/- 1.50, N = 15231.661. (CC) gcc options: -O3 -fcommon -fPIE -fPIC -fvisibility=hidden -pie -rdynamic -lpthread -lrt -lm

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 7543 + S8036GM2NE10K20K30K40K50KSE +/- 261.66, N = 548624.011. (F9X) gfortran options: -O3 -march=native -pthread -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -ldl -levent_core -levent_pthreads -lutil -lm -lrt -lz 2. Open MPI 4.1.0

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: SP.BEPYC 7543 + S8036GM2NE14K28K42K56K70KSE +/- 133.16, N = 767693.591. (F9X) gfortran options: -O3 -march=native -pthread -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -ldl -levent_core -levent_pthreads -lutil -lm -lrt -lz 2. Open MPI 4.1.0

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: CG.CEPYC 7543 + S8036GM2NE6K12K18K24K30KSE +/- 270.24, N = 730198.411. (F9X) gfortran options: -O3 -march=native -pthread -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -ldl -levent_core -levent_pthreads -lutil -lm -lrt -lz 2. Open MPI 4.1.0

SVT-HEVC

This is a test of the Intel Open Visual Cloud Scalable Video Technology SVT-HEVC CPU-based multi-threaded video encoder for the HEVC / H.265 video format with a sample 1080p YUV video file. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-HEVC 1.5.0Tuning: 7 - Input: Bosphorus 1080pEPYC 7543 + S8036GM2NE60120180240300SE +/- 4.70, N = 15292.351. (CC) gcc options: -fPIE -fPIC -O3 -O2 -pie -rdynamic -lpthread -lrt

TNN

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

OpenBenchmarking.orgms, Fewer Is BetterTNN 0.3Target: CPU - Model: SqueezeNet v2EPYC 7543 + S8036GM2NE1428425670SE +/- 0.06, N = 864.85MIN: 64.52 / MAX: 65.61. (CXX) g++ options: -fopenmp -pthread -fvisibility=hidden -fvisibility=default -O3 -rdynamic -ldl

NAS Parallel Benchmarks

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

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: MG.CEPYC 7543 + S8036GM2NE11K22K33K44K55KSE +/- 34.22, N = 849178.151. (F9X) gfortran options: -O3 -march=native -pthread -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -ldl -levent_core -levent_pthreads -lutil -lm -lrt -lz 2. Open MPI 4.1.0

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: EP.CEPYC 7543 + S8036GM2NE6001200180024003000SE +/- 6.64, N = 92962.421. (F9X) gfortran options: -O3 -march=native -pthread -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -ldl -levent_core -levent_pthreads -lutil -lm -lrt -lz 2. Open MPI 4.1.0

SVT-VP9

This is a test of the Intel Open Visual Cloud Scalable Video Technology SVT-VP9 CPU-based multi-threaded video encoder for the VP9 video format with a sample YUV input video file. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-VP9 0.3Tuning: VMAF Optimized - Input: Bosphorus 1080pEPYC 7543 + S8036GM2NE50100150200250SE +/- 0.68, N = 9240.661. (CC) gcc options: -O3 -fcommon -fPIE -fPIC -fvisibility=hidden -pie -rdynamic -lpthread -lrt -lm

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-VP9 0.3Tuning: PSNR/SSIM Optimized - Input: Bosphorus 1080pEPYC 7543 + S8036GM2NE50100150200250SE +/- 0.64, N = 9244.201. (CC) gcc options: -O3 -fcommon -fPIE -fPIC -fvisibility=hidden -pie -rdynamic -lpthread -lrt -lm

SVT-HEVC

This is a test of the Intel Open Visual Cloud Scalable Video Technology SVT-HEVC CPU-based multi-threaded video encoder for the HEVC / H.265 video format with a sample 1080p YUV video file. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-HEVC 1.5.0Tuning: 10 - Input: Bosphorus 1080pEPYC 7543 + S8036GM2NE100200300400500SE +/- 5.33, N = 15468.151. (CC) gcc options: -fPIE -fPIC -O3 -O2 -pie -rdynamic -lpthread -lrt

System Power Consumption Monitor

MinAvgMaxEPYC 7543 + S8036GM2NE128.0209.9352.0OpenBenchmarking.orgWattsSystem Power Consumption MonitorPhoronix Test Suite System Monitoring100200300400500

Memory Temperature Monitor

MinAvgMaxEPYC 7543 + S8036GM2NE32.035.238.0OpenBenchmarking.orgCelsiusMemory Temperature MonitorPhoronix Test Suite System Monitoring1122334455

Drive Temperature (nvme0n1) Monitor

MinAvgMaxEPYC 7543 + S8036GM2NE27.932.339.9OpenBenchmarking.orgCelsiusDrive Temperature (nvme0n1) MonitorPhoronix Test Suite System Monitoring1122334455

CPU Temperature Monitor

MinAvgMaxEPYC 7543 + S8036GM2NE45.063.481.8OpenBenchmarking.orgCelsiusCPU Temperature MonitorPhoronix Test Suite System Monitoring20406080100

CPU Power Consumption Monitor

MinAvgMaxEPYC 7543 + S8036GM2NE69.7137.2223.7OpenBenchmarking.orgWattsCPU Power Consumption MonitorPhoronix Test Suite System Monitoring60120180240300

CPU Peak Freq (Highest CPU Core Frequency) Monitor

MinAvgMaxEPYC 7543 + S8036GM2NE149932283732OpenBenchmarking.orgMegahertzCPU Peak Freq (Highest CPU Core Frequency) MonitorPhoronix Test Suite System Monitoring10002000300040005000

93 Results Shown

Timed GCC Compilation
Quantum ESPRESSO
PostgreSQL pgbench:
  100 - 250 - Read Write - Average Latency
  100 - 250 - Read Write
OpenVKL
Timed LLVM Compilation
TNN
Timed LLVM Compilation
Blender
BRL-CAD
Timed Node.js Compilation
Apache Cassandra
AOM AV1
SVT-AV1:
  Preset 4 - Bosphorus 4K
  Preset 8 - Bosphorus 4K
AOM AV1
NCNN:
  CPU - regnety_400m
  CPU - squeezenet_ssd
  CPU - yolov4-tiny
  CPU - resnet50
  CPU - alexnet
  CPU - resnet18
  CPU - vgg16
  CPU - googlenet
  CPU - blazeface
  CPU - efficientnet-b0
  CPU - mnasnet
  CPU - shufflenet-v2
  CPU-v3-v3 - mobilenet-v3
  CPU-v2-v2 - mobilenet-v2
  CPU - mobilenet
Mobile Neural Network:
  inception-v3
  mobilenet-v1-1.0
  MobileNetV2_224
  SqueezeNetV1.0
  resnet-v2-50
  squeezenetv1.1
  mobilenetV3
PyPerformance:
  raytrace
  python_startup
Intel Open Image Denoise
PyPerformance
Facebook RocksDB:
  Update Rand
  Read Rand Write Rand
  Rand Fill
  Read While Writing
AOM AV1
Timed Godot Game Engine Compilation
NAS Parallel Benchmarks
VP9 libvpx Encoding
PyPerformance
Timed GDB GNU Debugger Compilation
PyPerformance
NAS Parallel Benchmarks
Timed Linux Kernel Compilation
PyPerformance
Intel Open Image Denoise
PHPBench
PyPerformance
ASTC Encoder
PyPerformance:
  pathlib
  pickle_pure_python
Stockfish
NAS Parallel Benchmarks
PyPerformance:
  json_loads
  nbody
Timed FFmpeg Compilation
PyBench
PyPerformance
TNN
AOM AV1
NAS Parallel Benchmarks
Redis
PyPerformance
TNN
Redis
SVT-VP9
NAS Parallel Benchmarks:
  FT.C
  SP.B
  CG.C
SVT-HEVC
TNN
NAS Parallel Benchmarks:
  MG.C
  EP.C
SVT-VP9:
  VMAF Optimized - Bosphorus 1080p
  PSNR/SSIM Optimized - Bosphorus 1080p
SVT-HEVC
System Power Consumption Monitor:
  Phoronix Test Suite System Monitoring:
    Watts
    Celsius
    Celsius
    Celsius
    Watts
    Megahertz

EPYC 7543 + S8036GM2NE

Processor: AMD EPYC 7543 32-Core @ 2.80GHz (32 Cores / 64 Threads), Motherboard: TYAN S8036GM2NE-LE (V2.00.B21 BIOS), Chipset: AMD Starship/Matisse, Memory: 8 x GB DDR4-3200MT/s HMA81GR7CJR8N-XN, Disk: 1000GB Corsair Force MP600, Graphics: ASPEED, Monitor: VE228, Network: 2 x Broadcom NetXtreme BCM5720 2-port PCIe

OS: Ubuntu 21.04, Kernel: 5.11.0-25-generic (x86_64), Desktop: GNOME Shell 3.38.4, Display Server: X Server, Vulkan: 1.0.2, Compiler: GCC 10.3.0, 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-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-mutex --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-10-gDeRY6/gcc-10-10.3.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-10-gDeRY6/gcc-10-10.3.0/debian/tmp-gcn/usr,hsa --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 schedutil (Boost: Enabled) - CPU Microcode: 0xa001119
Java Notes: OpenJDK Runtime Environment (build 11.0.11+9-Ubuntu-0ubuntu2)
Python Notes: Python 3.9.5
Security Notes: itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: 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 Full AMD retpoline IBPB: conditional IBRS_FW STIBP: always-on RSB filling + srbds: Not affected + tsx_async_abort: Not affected

Testing initiated at 28 July 2021 09:19 by user root.