big bench AMD Ryzen Threadripper 7980X 64-Cores testing with a ASUS Pro WS TRX50-SAGE WIFI (0217 BIOS) and AMD Radeon RX 7900 XT 20GB on Ubuntu 23.10 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2401079-PTS-BIGBENCH30&grw&rdt .
big bench Processor Motherboard Chipset Memory Disk Graphics Audio Monitor Network OS Kernel Desktop Display Server OpenGL Compiler File-System Screen Resolution a b c AMD Ryzen Threadripper 7980X 64-Cores @ 8.21GHz (64 Cores / 128 Threads) ASUS Pro WS TRX50-SAGE WIFI (0217 BIOS) AMD Device 14a4 128GB 2000GB Corsair MP700 PRO + 1000GB Western Digital WDS100T1X0E-00AFY0 AMD Radeon RX 7900 XT 20GB (2025/1249MHz) Realtek ALC1220 DELL U2723QE Aquantia Device 04c0 + Intel I226-LM + MEDIATEK MT7922 802.11ax PCI Ubuntu 23.10 6.7.0-060700rc2daily20231126-generic (x86_64) GNOME Shell 45.0 X Server 1.21.1.7 + Wayland 4.6 Mesa 23.2.1-1ubuntu3 (LLVM 15.0.7 DRM 3.56) GCC 13.2.0 ext4 3840x2160 OpenBenchmarking.org Kernel Details - Transparent Huge Pages: madvise Compiler Details - --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,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 Processor Details - Scaling Governor: amd-pstate-epp powersave (EPP: balance_performance) - CPU Microcode: 0xa108105 Python Details - Python 3.11.6 Security Details - gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Not affected + 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 Enhanced / Automatic IBRS IBPB: conditional STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected
big bench tensorflow: CPU - 1 - VGG-16 quicksilver: CORAL2 P2 tensorflow: CPU - 1 - AlexNet tensorflow: CPU - 16 - VGG-16 tensorflow: CPU - 32 - VGG-16 tensorflow: CPU - 64 - VGG-16 tensorflow: CPU - 16 - AlexNet tensorflow: CPU - 256 - VGG-16 tensorflow: CPU - 32 - AlexNet tensorflow: CPU - 512 - VGG-16 tensorflow: CPU - 64 - AlexNet tensorflow: CPU - 1 - GoogLeNet tensorflow: CPU - 1 - ResNet-50 tensorflow: CPU - 256 - AlexNet tensorflow: CPU - 512 - AlexNet quicksilver: CORAL2 P1 tensorflow: CPU - 16 - GoogLeNet tensorflow: CPU - 16 - ResNet-50 tensorflow: CPU - 32 - GoogLeNet tensorflow: CPU - 32 - ResNet-50 tensorflow: CPU - 64 - GoogLeNet tensorflow: CPU - 64 - ResNet-50 tensorflow: CPU - 256 - GoogLeNet tensorflow: CPU - 256 - ResNet-50 tensorflow: CPU - 512 - GoogLeNet tensorflow: CPU - 512 - ResNet-50 pytorch: CPU - 1 - ResNet-50 pytorch: CPU - 1 - ResNet-152 pytorch: CPU - 16 - ResNet-50 pytorch: CPU - 32 - ResNet-50 pytorch: CPU - 64 - ResNet-50 pytorch: CPU - 16 - ResNet-152 pytorch: CPU - 256 - ResNet-50 pytorch: CPU - 32 - ResNet-152 pytorch: CPU - 512 - ResNet-50 pytorch: CPU - 64 - ResNet-152 pytorch: CPU - 256 - ResNet-152 pytorch: CPU - 512 - ResNet-152 pytorch: CPU - 1 - Efficientnet_v2_l pytorch: CPU - 16 - Efficientnet_v2_l pytorch: CPU - 32 - Efficientnet_v2_l pytorch: CPU - 64 - Efficientnet_v2_l pytorch: CPU - 256 - Efficientnet_v2_l quicksilver: CTS2 pytorch: CPU - 512 - Efficientnet_v2_l a b c 9.74 19776667 25.72 44.41 48.89 52.95 311.97 57.14 510.66 57.97 740.46 21.82 7.19 1070.73 1145.66 26026667 187.92 54.70 226.85 69.43 272.16 80.0 311.60 91.12 315.49 95.05 59.04 21.94 47.47 47.47 47.57 18.68 47.43 18.76 47.54 18.69 18.99 18.96 12.19 7.52 7.49 7.46 7.47 20023333 7.47 9.73 19733333 25.81 44.5 48.93 53 312.08 57.11 508.52 57.97 741.77 22.59 7.26 1073.52 1148.38 26010000 188.5 54.44 231.61 69.49 272.22 79.77 311.23 91.13 315.83 95.08 59.22 21.91 47.41 47.24 47.22 18.65 47.24 18.67 45.97 18.62 18.80 18.49 12.29 7.46 7.45 7.45 7.40 19973333 7.52 9.72 19820000 25.96 44.48 48.81 52.91 312.14 57.15 510.4 57.99 741.04 22.37 7.25 1075.09 1151.27 25920000 188.52 54.72 222.04 69.17 272.84 79.87 311.73 91.11 316.04 95.04 59.69 21.80 47.60 48.12 47.81 18.71 43.07 19.02 46.60 19.38 19.10 18.82 12.27 7.50 7.52 7.52 7.54 20090000 7.48 OpenBenchmarking.org
TensorFlow Device: CPU - Batch Size: 1 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 1 - Model: VGG-16 a b c 3 6 9 12 15 SE +/- 0.01, N = 3 9.74 9.73 9.72
Quicksilver Input: CORAL2 P2 OpenBenchmarking.org Figure Of Merit, More Is Better Quicksilver 20230818 Input: CORAL2 P2 a b c 4M 8M 12M 16M 20M SE +/- 23333.33, N = 3 SE +/- 57831.17, N = 3 19776667 19733333 19820000 1. (CXX) g++ options: -fopenmp -O3 -march=native
TensorFlow Device: CPU - Batch Size: 1 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 1 - Model: AlexNet a b c 6 12 18 24 30 SE +/- 0.03, N = 3 25.72 25.81 25.96
TensorFlow Device: CPU - Batch Size: 16 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 16 - Model: VGG-16 a b c 10 20 30 40 50 SE +/- 0.01, N = 3 44.41 44.50 44.48
TensorFlow Device: CPU - Batch Size: 32 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 32 - Model: VGG-16 a b c 11 22 33 44 55 SE +/- 0.07, N = 3 48.89 48.93 48.81
TensorFlow Device: CPU - Batch Size: 64 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 64 - Model: VGG-16 a b c 12 24 36 48 60 SE +/- 0.06, N = 3 52.95 53.00 52.91
TensorFlow Device: CPU - Batch Size: 16 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 16 - Model: AlexNet a b c 70 140 210 280 350 SE +/- 0.95, N = 3 311.97 312.08 312.14
TensorFlow Device: CPU - Batch Size: 256 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 256 - Model: VGG-16 a b c 13 26 39 52 65 SE +/- 0.02, N = 3 57.14 57.11 57.15
TensorFlow Device: CPU - Batch Size: 32 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 32 - Model: AlexNet a b c 110 220 330 440 550 SE +/- 0.43, N = 3 510.66 508.52 510.40
TensorFlow Device: CPU - Batch Size: 512 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 512 - Model: VGG-16 a b c 13 26 39 52 65 SE +/- 0.02, N = 3 57.97 57.97 57.99
TensorFlow Device: CPU - Batch Size: 64 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 64 - Model: AlexNet a b c 160 320 480 640 800 SE +/- 0.53, N = 3 740.46 741.77 741.04
TensorFlow Device: CPU - Batch Size: 1 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 1 - Model: GoogLeNet a b c 5 10 15 20 25 SE +/- 0.29, N = 15 21.82 22.59 22.37
TensorFlow Device: CPU - Batch Size: 1 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 1 - Model: ResNet-50 a b c 2 4 6 8 10 SE +/- 0.04, N = 3 7.19 7.26 7.25
TensorFlow Device: CPU - Batch Size: 256 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 256 - Model: AlexNet a b c 200 400 600 800 1000 SE +/- 2.11, N = 3 1070.73 1073.52 1075.09
TensorFlow Device: CPU - Batch Size: 512 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 512 - Model: AlexNet a b c 200 400 600 800 1000 SE +/- 0.89, N = 3 1145.66 1148.38 1151.27
Quicksilver Input: CORAL2 P1 OpenBenchmarking.org Figure Of Merit, More Is Better Quicksilver 20230818 Input: CORAL2 P1 a b c 6M 12M 18M 24M 30M SE +/- 92796.07, N = 3 SE +/- 79372.54, N = 3 26026667 26010000 25920000 1. (CXX) g++ options: -fopenmp -O3 -march=native
TensorFlow Device: CPU - Batch Size: 16 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 16 - Model: GoogLeNet a b c 40 80 120 160 200 SE +/- 0.56, N = 3 187.92 188.50 188.52
TensorFlow Device: CPU - Batch Size: 16 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 16 - Model: ResNet-50 a b c 12 24 36 48 60 SE +/- 0.07, N = 3 54.70 54.44 54.72
TensorFlow Device: CPU - Batch Size: 32 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 32 - Model: GoogLeNet a b c 50 100 150 200 250 SE +/- 0.94, N = 3 226.85 231.61 222.04
TensorFlow Device: CPU - Batch Size: 32 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 32 - Model: ResNet-50 a b c 15 30 45 60 75 SE +/- 0.03, N = 3 69.43 69.49 69.17
TensorFlow Device: CPU - Batch Size: 64 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 64 - Model: GoogLeNet a b c 60 120 180 240 300 SE +/- 0.93, N = 3 272.16 272.22 272.84
TensorFlow Device: CPU - Batch Size: 64 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 64 - Model: ResNet-50 a b c 20 40 60 80 100 SE +/- 0.06, N = 3 80.00 79.77 79.87
TensorFlow Device: CPU - Batch Size: 256 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 256 - Model: GoogLeNet a b c 70 140 210 280 350 SE +/- 0.26, N = 3 311.60 311.23 311.73
TensorFlow Device: CPU - Batch Size: 256 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 256 - Model: ResNet-50 a b c 20 40 60 80 100 SE +/- 0.03, N = 3 91.12 91.13 91.11
TensorFlow Device: CPU - Batch Size: 512 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 512 - Model: GoogLeNet a b c 70 140 210 280 350 SE +/- 0.17, N = 3 315.49 315.83 316.04
TensorFlow Device: CPU - Batch Size: 512 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 512 - Model: ResNet-50 a b c 20 40 60 80 100 SE +/- 0.01, N = 3 95.05 95.08 95.04
PyTorch Device: CPU - Batch Size: 1 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 1 - Model: ResNet-50 a b c 13 26 39 52 65 SE +/- 0.52, N = 3 SE +/- 0.39, N = 14 59.04 59.22 59.69 MIN: 49.45 / MAX: 62.07 MIN: 49.85 / MAX: 62.35 MIN: 53.84 / MAX: 61.85
PyTorch Device: CPU - Batch Size: 1 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 1 - Model: ResNet-152 a b c 5 10 15 20 25 SE +/- 0.08, N = 3 SE +/- 0.08, N = 3 21.94 21.91 21.80 MIN: 20.91 / MAX: 22.35 MIN: 20.97 / MAX: 22.39 MIN: 21.22 / MAX: 22.14
PyTorch Device: CPU - Batch Size: 16 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 16 - Model: ResNet-50 a b c 11 22 33 44 55 SE +/- 0.21, N = 3 SE +/- 0.15, N = 3 47.47 47.41 47.60 MIN: 43.56 / MAX: 48.81 MIN: 43.15 / MAX: 48.78 MIN: 43.77 / MAX: 48.91
PyTorch Device: CPU - Batch Size: 32 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 32 - Model: ResNet-50 a b c 11 22 33 44 55 SE +/- 0.12, N = 3 SE +/- 0.43, N = 13 47.47 47.24 48.12 MIN: 43.75 / MAX: 48.83 MIN: 40.13 / MAX: 49.58 MIN: 44.48 / MAX: 49.1
PyTorch Device: CPU - Batch Size: 64 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 64 - Model: ResNet-50 a b c 11 22 33 44 55 SE +/- 0.18, N = 3 SE +/- 0.32, N = 3 47.57 47.22 47.81 MIN: 43.64 / MAX: 49.11 MIN: 43.76 / MAX: 48.92 MIN: 43.73 / MAX: 48.84
PyTorch Device: CPU - Batch Size: 16 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 16 - Model: ResNet-152 a b c 5 10 15 20 25 SE +/- 0.05, N = 3 SE +/- 0.17, N = 3 18.68 18.65 18.71 MIN: 18.15 / MAX: 18.95 MIN: 17.9 / MAX: 19.22 MIN: 18.25 / MAX: 18.96
PyTorch Device: CPU - Batch Size: 256 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 256 - Model: ResNet-50 a b c 11 22 33 44 55 SE +/- 0.44, N = 6 SE +/- 0.65, N = 3 47.43 47.24 43.07 MIN: 39.54 / MAX: 49.38 MIN: 39.04 / MAX: 49.14 MIN: 39.73 / MAX: 45.38
PyTorch Device: CPU - Batch Size: 32 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 32 - Model: ResNet-152 a b c 5 10 15 20 25 SE +/- 0.14, N = 3 18.76 18.67 19.02 MIN: 18.21 / MAX: 19.24 MIN: 17.72 / MAX: 18.87 MIN: 18.53 / MAX: 19.21
PyTorch Device: CPU - Batch Size: 512 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 512 - Model: ResNet-50 a b c 11 22 33 44 55 SE +/- 0.26, N = 3 47.54 45.97 46.60 MIN: 43.78 / MAX: 49.15 MIN: 43.46 / MAX: 48.21 MIN: 43.8 / MAX: 47.68
PyTorch Device: CPU - Batch Size: 64 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 64 - Model: ResNet-152 a b c 5 10 15 20 25 SE +/- 0.03, N = 3 18.69 18.62 19.38 MIN: 18.14 / MAX: 18.93 MIN: 18.09 / MAX: 18.8 MIN: 18.61 / MAX: 19.57
PyTorch Device: CPU - Batch Size: 256 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 256 - Model: ResNet-152 a b c 5 10 15 20 25 SE +/- 0.08, N = 3 18.99 18.80 19.10 MIN: 18.38 / MAX: 19.33 MIN: 18.29 / MAX: 19.02 MIN: 18.59 / MAX: 19.3
PyTorch Device: CPU - Batch Size: 512 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 512 - Model: ResNet-152 a b c 5 10 15 20 25 SE +/- 0.05, N = 3 18.96 18.49 18.82 MIN: 18.41 / MAX: 19.25 MIN: 18.01 / MAX: 18.69 MIN: 18.25 / MAX: 19.03
PyTorch Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l a b c 3 6 9 12 15 SE +/- 0.03, N = 3 12.19 12.29 12.27 MIN: 11.91 / MAX: 12.38 MIN: 12.13 / MAX: 12.42 MIN: 12 / MAX: 12.4
PyTorch Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l a b c 2 4 6 8 10 SE +/- 0.02, N = 3 7.52 7.46 7.50 MIN: 6.92 / MAX: 8.16 MIN: 6.82 / MAX: 8.06 MIN: 7.02 / MAX: 8.12
PyTorch Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l a b c 2 4 6 8 10 SE +/- 0.01, N = 3 7.49 7.45 7.52 MIN: 6.96 / MAX: 8.11 MIN: 6.99 / MAX: 8.1 MIN: 7.05 / MAX: 8.13
PyTorch Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l a b c 2 4 6 8 10 SE +/- 0.02, N = 3 7.46 7.45 7.52 MIN: 6.94 / MAX: 8.17 MIN: 6.98 / MAX: 8.07 MIN: 5.94 / MAX: 8.1
PyTorch Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l a b c 2 4 6 8 10 SE +/- 0.01, N = 3 7.47 7.40 7.54 MIN: 6.98 / MAX: 8.1 MIN: 6.92 / MAX: 8.06 MIN: 7.01 / MAX: 8.17
Quicksilver Input: CTS2 OpenBenchmarking.org Figure Of Merit, More Is Better Quicksilver 20230818 Input: CTS2 a b c 4M 8M 12M 16M 20M SE +/- 40960.69, N = 3 SE +/- 49103.07, N = 3 20023333 19973333 20090000 1. (CXX) g++ options: -fopenmp -O3 -march=native
PyTorch Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l a b c 2 4 6 8 10 SE +/- 0.01, N = 3 7.47 7.52 7.48 MIN: 6.97 / MAX: 8.13 MIN: 7.01 / MAX: 8.06 MIN: 7.03 / MAX: 8.13
Phoronix Test Suite v10.8.5