dghhg

AMD Ryzen Threadripper 3990X 64-Core testing with a Gigabyte TRX40 AORUS PRO WIFI (F6 BIOS) and AMD Radeon RX 5700 8GB on Ubuntu 23.10 via the Phoronix Test Suite.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2401086-PTS-DGHHG38612
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January 07
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dghhgOpenBenchmarking.orgPhoronix Test SuiteAMD Ryzen Threadripper 3990X 64-Core @ 2.90GHz (64 Cores / 128 Threads)Gigabyte TRX40 AORUS PRO WIFI (F6 BIOS)AMD Starship/Matisse128GBSamsung SSD 970 EVO Plus 500GBAMD Radeon RX 5700 8GB (1750/875MHz)AMD Navi 10 HDMI AudioDELL P2415QIntel I211 + Intel Wi-Fi 6 AX200Ubuntu 23.106.5.0-14-generic (x86_64)GNOME Shell 45.0X Server + Wayland4.6 Mesa 23.2.1-1ubuntu3 (LLVM 15.0.7 DRM 3.54)GCC 13.2.0ext43840x2160ProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDesktopDisplay ServerOpenGLCompilerFile-SystemScreen ResolutionDghhg BenchmarksSystem Logs- Transparent Huge Pages: madvise- --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,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-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-XYspKM/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-XYspKM/gcc-13-13.2.0/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 - Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0x830107a- Python 3.11.6- gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Mitigation of untrained return thunk; SMT enabled with STIBP protection + spec_rstack_overflow: Mitigation of safe RET + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

abcdResult OverviewPhoronix Test Suite100%100%101%101%101%PyTorchSpeedbQuicksilverY-CruncherTensorFlow

dghhgpytorch: CPU - 1 - ResNet-50pytorch: CPU - 1 - ResNet-152pytorch: CPU - 16 - ResNet-50pytorch: CPU - 32 - ResNet-50pytorch: CPU - 64 - ResNet-50pytorch: CPU - 16 - ResNet-152pytorch: CPU - 256 - ResNet-50pytorch: CPU - 32 - ResNet-152pytorch: CPU - 512 - ResNet-50pytorch: CPU - 64 - ResNet-152pytorch: CPU - 256 - ResNet-152pytorch: CPU - 512 - ResNet-152pytorch: CPU - 1 - Efficientnet_v2_lpytorch: CPU - 16 - Efficientnet_v2_lpytorch: CPU - 32 - Efficientnet_v2_lpytorch: CPU - 64 - Efficientnet_v2_lpytorch: CPU - 256 - Efficientnet_v2_lpytorch: CPU - 512 - Efficientnet_v2_lquicksilver: CTS2quicksilver: CORAL2 P1quicksilver: CORAL2 P2tensorflow: CPU - 1 - VGG-16tensorflow: CPU - 1 - AlexNettensorflow: CPU - 16 - VGG-16tensorflow: CPU - 16 - AlexNettensorflow: CPU - 1 - GoogLeNettensorflow: CPU - 1 - ResNet-50tensorflow: CPU - 16 - GoogLeNettensorflow: CPU - 16 - ResNet-50speedb: Rand Readspeedb: Update Randspeedb: Read While Writingspeedb: Read Rand Write Randy-cruncher: 1By-cruncher: 5By-cruncher: 10By-cruncher: 500Mabcd21.518.5817.3917.3717.696.9617.647.0317.727.096.886.984.342.902.952.942.922.912074000025650000246100001.844.913.6453.798.695.0230.39.7118361607325996212812292219393319.007111.064238.5609.52920.548.3617.7317.8517.267.0417.276.9216.476.846.826.884.313.032.892.932.923.002080000025800000246000001.834.893.6454.838.75.0330.539.5418412958225849413256747218274919.11110.664238.2269.54921.428.4817.0317.2417.447.0416.537.1116.906.916.967.064.362.932.892.962.872.902068000025540000245400001.834.933.6453.828.725.0230.289.7318389968225842013042085219484419.126110.858238.4689.53220.798.3817.4817.9016.946.8717.387.0117.076.996.996.824.342.952.992.912.882.912077000025740000245500001.844.893.6454.298.694.9930.679.6618306193625816812917858220206819.08110.617238.5689.542OpenBenchmarking.org

PyTorch

This is a benchmark of PyTorch making use of pytorch-benchmark [https://github.com/LukasHedegaard/pytorch-benchmark]. Currently this test profile is catered to CPU-based testing. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-50abcd51015202521.5120.5421.4220.79MIN: 20.69 / MAX: 22.56MIN: 19.65 / MAX: 21.6MIN: 20.69 / MAX: 22.59MIN: 19.68 / MAX: 21.6

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-152abcd2468108.588.368.488.38MIN: 8.38 / MAX: 8.76MIN: 8.21 / MAX: 8.57MIN: 8.26 / MAX: 8.67MIN: 8.19 / MAX: 8.58

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-50abcd4812162017.3917.7317.0317.48MIN: 16.36 / MAX: 18.06MIN: 17.04 / MAX: 18.52MIN: 16.02 / MAX: 17.78MIN: 16.71 / MAX: 18.01

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-50abcd4812162017.3717.8517.2417.90MIN: 16.47 / MAX: 17.93MIN: 17.23 / MAX: 18.44MIN: 16.41 / MAX: 17.86MIN: 17.25 / MAX: 18.79

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: ResNet-50abcd4812162017.6917.2617.4416.94MIN: 16.96 / MAX: 18.46MIN: 16.19 / MAX: 17.95MIN: 15.61 / MAX: 18.15MIN: 16.24 / MAX: 17.77

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-152abcd2468106.967.047.046.87MIN: 6.82 / MAX: 7.1MIN: 6.9 / MAX: 7.17MIN: 6.57 / MAX: 7.18MIN: 6.73 / MAX: 7

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: ResNet-50abcd4812162017.6417.2716.5317.38MIN: 16.6 / MAX: 18.41MIN: 16.5 / MAX: 17.89MIN: 15.99 / MAX: 17.32MIN: 16.76 / MAX: 18

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-152abcd2468107.036.927.117.01MIN: 6.9 / MAX: 7.16MIN: 6.79 / MAX: 7.07MIN: 6.98 / MAX: 7.26MIN: 6.82 / MAX: 7.14

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: ResNet-50abcd4812162017.7216.4716.9017.07MIN: 17.17 / MAX: 18.5MIN: 15.78 / MAX: 17.3MIN: 16.19 / MAX: 17.59MIN: 16.08 / MAX: 17.92

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: ResNet-152abcd2468107.096.846.916.99MIN: 6.94 / MAX: 7.22MIN: 6.7 / MAX: 6.97MIN: 6.75 / MAX: 7.03MIN: 6.81 / MAX: 7.11

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: ResNet-152abcd2468106.886.826.966.99MIN: 6.61 / MAX: 7.02MIN: 6.68 / MAX: 7.01MIN: 6.83 / MAX: 7.08MIN: 6.8 / MAX: 7.16

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: ResNet-152abcd2468106.986.887.066.82MIN: 6.83 / MAX: 7.16MIN: 6.75 / MAX: 7.02MIN: 6.92 / MAX: 7.17MIN: 6.57 / MAX: 7

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_labcd0.9811.9622.9433.9244.9054.344.314.364.34MIN: 4.15 / MAX: 4.53MIN: 3.99 / MAX: 4.46MIN: 4.13 / MAX: 4.51MIN: 4.03 / MAX: 4.53

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_labcd0.68181.36362.04542.72723.4092.903.032.932.95MIN: 2.8 / MAX: 3.01MIN: 2.92 / MAX: 3.15MIN: 2.83 / MAX: 3.05MIN: 2.85 / MAX: 3.06

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_labcd0.67281.34562.01842.69123.3642.952.892.892.99MIN: 2.77 / MAX: 3.07MIN: 2.8 / MAX: 3MIN: 2.75 / MAX: 3.02MIN: 2.88 / MAX: 3.07

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_labcd0.6661.3321.9982.6643.332.942.932.962.91MIN: 2.82 / MAX: 3.09MIN: 2.79 / MAX: 3.06MIN: 2.81 / MAX: 3.07MIN: 2.73 / MAX: 3.13

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_labcd0.6571.3141.9712.6283.2852.922.922.872.88MIN: 2.82 / MAX: 3.06MIN: 2.81 / MAX: 3.04MIN: 2.74 / MAX: 2.99MIN: 2.78 / MAX: 2.98

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_labcd0.6751.352.0252.73.3752.913.002.902.91MIN: 2.81 / MAX: 3.01MIN: 2.91 / MAX: 3.1MIN: 2.77 / MAX: 3.01MIN: 2.8 / MAX: 3.01

Quicksilver

Quicksilver is a proxy application that represents some elements of the Mercury workload by solving a simplified dynamic Monte Carlo particle transport problem. Quicksilver is developed by Lawrence Livermore National Laboratory (LLNL) and this test profile currently makes use of the OpenMP CPU threaded code path. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFigure Of Merit, More Is BetterQuicksilver 20230818Input: CTS2abcd4M8M12M16M20M207400002080000020680000207700001. (CXX) g++ options: -fopenmp -O3 -march=native

OpenBenchmarking.orgFigure Of Merit, More Is BetterQuicksilver 20230818Input: CORAL2 P1abcd6M12M18M24M30M256500002580000025540000257400001. (CXX) g++ options: -fopenmp -O3 -march=native

OpenBenchmarking.orgFigure Of Merit, More Is BetterQuicksilver 20230818Input: CORAL2 P2abcd5M10M15M20M25M246100002460000024540000245500001. (CXX) g++ options: -fopenmp -O3 -march=native

TensorFlow

This is a benchmark of the TensorFlow deep learning framework using the TensorFlow reference benchmarks (tensorflow/benchmarks with tf_cnn_benchmarks.py). Note with the Phoronix Test Suite there is also pts/tensorflow-lite for benchmarking the TensorFlow Lite binaries if desired for complementary metrics. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 1 - Model: VGG-16abcd0.4140.8281.2421.6562.071.841.831.831.84

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 1 - Model: AlexNetabcd1.10932.21863.32794.43725.54654.914.894.934.89

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 16 - Model: VGG-16abcd0.8191.6382.4573.2764.0953.643.643.643.64

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 16 - Model: AlexNetabcd122436486053.7954.8353.8254.29

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 1 - Model: GoogLeNetabcd2468108.698.708.728.69

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 1 - Model: ResNet-50abcd1.13182.26363.39544.52725.6595.025.035.024.99

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 16 - Model: GoogLeNetabcd71421283530.3030.5330.2830.67

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 16 - Model: ResNet-50abcd36912159.719.549.739.66

Speedb

Speedb is a next-generation key value storage engine that is RocksDB compatible and aiming for stability, efficiency, and performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgOp/s, More Is BetterSpeedb 2.7Test: Random Readabcd40M80M120M160M200M1836160731841295821838996821830619361. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread

OpenBenchmarking.orgOp/s, More Is BetterSpeedb 2.7Test: Update Randomabcd60K120K180K240K300K2599622584942584202581681. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread

OpenBenchmarking.orgOp/s, More Is BetterSpeedb 2.7Test: Read While Writingabcd3M6M9M12M15M128122921325674713042085129178581. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread

OpenBenchmarking.orgOp/s, More Is BetterSpeedb 2.7Test: Read Random Write Randomabcd500K1000K1500K2000K2500K21939332182749219484422020681. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread

Y-Cruncher

Y-Cruncher is a multi-threaded Pi benchmark capable of computing Pi to trillions of digits. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterY-Cruncher 0.8.3Pi Digits To Calculate: 1Babcd510152025SE +/- 0.01, N = 319.0119.1119.1319.08

OpenBenchmarking.orgSeconds, Fewer Is BetterY-Cruncher 0.8.3Pi Digits To Calculate: 5Babcd20406080100SE +/- 0.11, N = 3111.06110.66110.86110.62

OpenBenchmarking.orgSeconds, Fewer Is BetterY-Cruncher 0.8.3Pi Digits To Calculate: 10Babcd50100150200250SE +/- 0.08, N = 3238.56238.23238.47238.57

OpenBenchmarking.orgSeconds, Fewer Is BetterY-Cruncher 0.8.3Pi Digits To Calculate: 500Mabcd36912159.5299.5499.5329.542