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 - 256 - Efficientnet_v2_lpytorch: CPU - 512 - Efficientnet_v2_lpytorch: CPU - 32 - Efficientnet_v2_lpytorch: CPU - 64 - Efficientnet_v2_lpytorch: CPU - 16 - Efficientnet_v2_ltensorflow: CPU - 16 - VGG-16y-cruncher: 10Bpytorch: CPU - 256 - ResNet-152pytorch: CPU - 512 - ResNet-152pytorch: CPU - 64 - ResNet-152pytorch: CPU - 16 - ResNet-152pytorch: CPU - 32 - ResNet-152pytorch: CPU - 1 - Efficientnet_v2_lquicksilver: CTS2quicksilver: CORAL2 P2y-cruncher: 5Btensorflow: CPU - 16 - ResNet-50pytorch: CPU - 1 - ResNet-152pytorch: CPU - 512 - ResNet-50pytorch: CPU - 256 - ResNet-50pytorch: CPU - 16 - ResNet-50pytorch: CPU - 64 - ResNet-50pytorch: CPU - 32 - ResNet-50tensorflow: CPU - 1 - VGG-16tensorflow: CPU - 16 - GoogLeNetspeedb: Update Randspeedb: Read Rand Write Randspeedb: Read While Writingspeedb: Rand Readpytorch: CPU - 1 - ResNet-50quicksilver: CORAL2 P1y-cruncher: 1Btensorflow: CPU - 16 - AlexNettensorflow: CPU - 1 - ResNet-50tensorflow: CPU - 1 - AlexNettensorflow: CPU - 1 - GoogLeNety-cruncher: 500Mabcd2.922.912.952.942.903.64238.5606.886.987.096.967.034.342074000024610000111.0649.718.5817.7217.6417.3917.6917.371.8430.325996221939331281229218361607321.512565000019.00753.795.024.918.699.5292.923.002.892.933.033.64238.2266.826.886.847.046.924.312080000024600000110.6649.548.3616.4717.2717.7317.2617.851.8330.5325849421827491325674718412958220.542580000019.1154.835.034.898.79.5492.872.902.892.962.933.64238.4686.967.066.917.047.114.362068000024540000110.8589.738.4816.9016.5317.0317.4417.241.8330.2825842021948441304208518389968221.422554000019.12653.825.024.938.729.5322.882.912.992.912.953.64238.5686.996.826.996.877.014.342077000024550000110.6179.668.3817.0717.3817.4816.9417.901.8430.6725816822020681291785818306193620.792574000019.0854.294.994.898.699.542OpenBenchmarking.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: 256 - Model: Efficientnet_v2_ldcba0.6571.3141.9712.6283.2852.882.872.922.92MIN: 2.78 / MAX: 2.98MIN: 2.74 / MAX: 2.99MIN: 2.81 / MAX: 3.04MIN: 2.82 / MAX: 3.06

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_ldcba0.6751.352.0252.73.3752.912.903.002.91MIN: 2.8 / MAX: 3.01MIN: 2.77 / MAX: 3.01MIN: 2.91 / MAX: 3.1MIN: 2.81 / MAX: 3.01

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_ldcba0.67281.34562.01842.69123.3642.992.892.892.95MIN: 2.88 / MAX: 3.07MIN: 2.75 / MAX: 3.02MIN: 2.8 / MAX: 3MIN: 2.77 / MAX: 3.07

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_ldcba0.6661.3321.9982.6643.332.912.962.932.94MIN: 2.73 / MAX: 3.13MIN: 2.81 / MAX: 3.07MIN: 2.79 / MAX: 3.06MIN: 2.82 / MAX: 3.09

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_ldcba0.68181.36362.04542.72723.4092.952.933.032.90MIN: 2.85 / MAX: 3.06MIN: 2.83 / MAX: 3.05MIN: 2.92 / MAX: 3.15MIN: 2.8 / MAX: 3.01

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: 16 - Model: VGG-16dcba0.8191.6382.4573.2764.0953.643.643.643.64

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: 10Bdcba50100150200250SE +/- 0.08, N = 3238.57238.47238.23238.56

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: 256 - Model: ResNet-152dcba2468106.996.966.826.88MIN: 6.8 / MAX: 7.16MIN: 6.83 / MAX: 7.08MIN: 6.68 / MAX: 7.01MIN: 6.61 / MAX: 7.02

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: ResNet-152dcba2468106.827.066.886.98MIN: 6.57 / MAX: 7MIN: 6.92 / MAX: 7.17MIN: 6.75 / MAX: 7.02MIN: 6.83 / MAX: 7.16

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: ResNet-152dcba2468106.996.916.847.09MIN: 6.81 / MAX: 7.11MIN: 6.75 / MAX: 7.03MIN: 6.7 / MAX: 6.97MIN: 6.94 / MAX: 7.22

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-152dcba2468106.877.047.046.96MIN: 6.73 / MAX: 7MIN: 6.57 / MAX: 7.18MIN: 6.9 / MAX: 7.17MIN: 6.82 / MAX: 7.1

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-152dcba2468107.017.116.927.03MIN: 6.82 / MAX: 7.14MIN: 6.98 / MAX: 7.26MIN: 6.79 / MAX: 7.07MIN: 6.9 / MAX: 7.16

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_ldcba0.9811.9622.9433.9244.9054.344.364.314.34MIN: 4.03 / MAX: 4.53MIN: 4.13 / MAX: 4.51MIN: 3.99 / MAX: 4.46MIN: 4.15 / MAX: 4.53

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: CTS2dcba4M8M12M16M20M207700002068000020800000207400001. (CXX) g++ options: -fopenmp -O3 -march=native

OpenBenchmarking.orgFigure Of Merit, More Is BetterQuicksilver 20230818Input: CORAL2 P2dcba5M10M15M20M25M245500002454000024600000246100001. (CXX) g++ options: -fopenmp -O3 -march=native

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: 5Bdcba20406080100SE +/- 0.11, N = 3110.62110.86110.66111.06

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: 16 - Model: ResNet-50dcba36912159.669.739.549.71

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-152dcba2468108.388.488.368.58MIN: 8.19 / MAX: 8.58MIN: 8.26 / MAX: 8.67MIN: 8.21 / MAX: 8.57MIN: 8.38 / MAX: 8.76

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: ResNet-50dcba4812162017.0716.9016.4717.72MIN: 16.08 / MAX: 17.92MIN: 16.19 / MAX: 17.59MIN: 15.78 / MAX: 17.3MIN: 17.17 / MAX: 18.5

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: ResNet-50dcba4812162017.3816.5317.2717.64MIN: 16.76 / MAX: 18MIN: 15.99 / MAX: 17.32MIN: 16.5 / MAX: 17.89MIN: 16.6 / MAX: 18.41

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-50dcba4812162017.4817.0317.7317.39MIN: 16.71 / MAX: 18.01MIN: 16.02 / MAX: 17.78MIN: 17.04 / MAX: 18.52MIN: 16.36 / MAX: 18.06

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: ResNet-50dcba4812162016.9417.4417.2617.69MIN: 16.24 / MAX: 17.77MIN: 15.61 / MAX: 18.15MIN: 16.19 / MAX: 17.95MIN: 16.96 / MAX: 18.46

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-50dcba4812162017.9017.2417.8517.37MIN: 17.25 / MAX: 18.79MIN: 16.41 / MAX: 17.86MIN: 17.23 / MAX: 18.44MIN: 16.47 / MAX: 17.93

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-16dcba0.4140.8281.2421.6562.071.841.831.831.84

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 16 - Model: GoogLeNetdcba71421283530.6730.2830.5330.30

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: Update Randomdcba60K120K180K240K300K2581682584202584942599621. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread

OpenBenchmarking.orgOp/s, More Is BetterSpeedb 2.7Test: Read Random Write Randomdcba500K1000K1500K2000K2500K22020682194844218274921939331. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread

OpenBenchmarking.orgOp/s, More Is BetterSpeedb 2.7Test: Read While Writingdcba3M6M9M12M15M129178581304208513256747128122921. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread

OpenBenchmarking.orgOp/s, More Is BetterSpeedb 2.7Test: Random Readdcba40M80M120M160M200M1830619361838996821841295821836160731. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread

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-50dcba51015202520.7921.4220.5421.51MIN: 19.68 / MAX: 21.6MIN: 20.69 / MAX: 22.59MIN: 19.65 / MAX: 21.6MIN: 20.69 / MAX: 22.56

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: CORAL2 P1dcba6M12M18M24M30M257400002554000025800000256500001. (CXX) g++ options: -fopenmp -O3 -march=native

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: 1Bdcba510152025SE +/- 0.01, N = 319.0819.1319.1119.01

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: 16 - Model: AlexNetdcba122436486054.2953.8254.8353.79

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 1 - Model: ResNet-50dcba1.13182.26363.39544.52725.6594.995.025.035.02

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 1 - Model: AlexNetdcba1.10932.21863.32794.43725.54654.894.934.894.91

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 1 - Model: GoogLeNetdcba2468108.698.728.708.69

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: 500Mdcba36912159.5429.5329.5499.529