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AMD Ryzen 5 5500U testing with a NB01 NL5xNU (1.07.11RTR1 BIOS) and AMD Lucienne 512MB on Tuxedo 22.04 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 2401075-NE-FHD99334640
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fhdOpenBenchmarking.orgPhoronix Test SuiteAMD Ryzen 5 5500U @ 4.06GHz (6 Cores / 12 Threads)NB01 NL5xNU (1.07.11RTR1 BIOS)AMD Renoir/Cezanne16GBSamsung SSD 970 EVO Plus 500GBAMD Lucienne 512MB (1800/400MHz)AMD Renoir Radeon HD AudioRealtek RTL8111/8168/8411 + Intel Wi-Fi 6 AX200Tuxedo 22.046.0.0-1010-oem (x86_64)KDE Plasma 5.26.5X Server 1.21.1.34.6 Mesa 22.3.7 (LLVM 14.0.0 DRM 3.48)1.3.230GCC 11.3.0ext41920x1080ProcessorMotherboardChipsetMemoryDiskGraphicsAudioNetworkOSKernelDesktopDisplay ServerOpenGLVulkanCompilerFile-SystemScreen ResolutionFhd 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,brig,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-11-xKiWfi/gcc-11-11.3.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-xKiWfi/gcc-11-11.3.0/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: amd-pstate ondemand (Boost: Enabled) - CPU Microcode: 0x8608103 - Python 3.10.6- 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_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

sbcResult OverviewPhoronix Test Suite100%100%101%101%102%PyTorchTensorFlowQuicksilver

fhdpytorch: CPU - 1 - ResNet-50pytorch: CPU - 32 - ResNet-50pytorch: CPU - 1 - ResNet-152pytorch: CPU - 64 - ResNet-152pytorch: CPU - 16 - ResNet-152pytorch: CPU - 16 - ResNet-50pytorch: CPU - 64 - Efficientnet_v2_ltensorflow: CPU - 1 - ResNet-50pytorch: CPU - 1 - Efficientnet_v2_lpytorch: CPU - 32 - ResNet-152pytorch: CPU - 16 - Efficientnet_v2_lpytorch: CPU - 32 - Efficientnet_v2_ltensorflow: CPU - 16 - ResNet-50pytorch: CPU - 64 - ResNet-50quicksilver: CTS2tensorflow: CPU - 32 - ResNet-50quicksilver: CORAL2 P1quicksilver: CORAL2 P2tensorflow: CPU - 64 - ResNet-50sbc18.089.937.344.214.249.883.014.354.884.242.962.986.5510.0058016676.666085667113866676.6718.6110.387.574.344.3510.132.974.284.954.283.002.976.4910.1058150006.646090000113900006.6817.6810.107.484.334.2710.053.044.284.924.223.003.016.4710.0557700006.636071000113700006.68OpenBenchmarking.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-50scb510152025SE +/- 0.14, N = 318.0817.6818.61MIN: 15.84 / MAX: 21.76MIN: 12.28 / MAX: 21.23MIN: 16.39 / MAX: 21.61

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-50scb3691215SE +/- 0.04, N = 39.9310.1010.38MIN: 6.31 / MAX: 12.54MIN: 6.02 / MAX: 12.08MIN: 7.09 / MAX: 12.44

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-152scb246810SE +/- 0.05, N = 37.347.487.57MIN: 4.67 / MAX: 9.47MIN: 4.76 / MAX: 9.16MIN: 4.43 / MAX: 9.03

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: ResNet-152scb0.97651.9532.92953.9064.8825SE +/- 0.05, N = 34.214.334.34MIN: 2.71 / MAX: 5.3MIN: 2.76 / MAX: 5.39MIN: 2.83 / MAX: 5.5

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-152scb0.97881.95762.93643.91524.894SE +/- 0.03, N = 34.244.274.35MIN: 2.77 / MAX: 5.36MIN: 2.71 / MAX: 5.35MIN: 2.67 / MAX: 5.28

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-50scb3691215SE +/- 0.06, N = 39.8810.0510.13MIN: 5.98 / MAX: 12.22MIN: 5.43 / MAX: 11.83MIN: 6.6 / MAX: 12.34

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_lscb0.6841.3682.0522.7363.42SE +/- 0.00, N = 33.013.042.97MIN: 2 / MAX: 3.67MIN: 2 / MAX: 3.59MIN: 2 / MAX: 3.54

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: ResNet-50scb0.97881.95762.93643.91524.894SE +/- 0.00, N = 34.354.284.28

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: Efficientnet_v2_lscb1.11382.22763.34144.45525.569SE +/- 0.03, N = 34.884.924.95MIN: 3.43 / MAX: 5.79MIN: 4.35 / MAX: 5.62MIN: 4.2 / MAX: 5.61

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-152scb0.9631.9262.8893.8524.815SE +/- 0.00, N = 34.244.224.28MIN: 2.69 / MAX: 5.49MIN: 2.82 / MAX: 5.35MIN: 2.77 / MAX: 5.34

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_lscb0.6751.352.0252.73.375SE +/- 0.03, N = 32.963.003.00MIN: 2 / MAX: 3.69MIN: 2.06 / MAX: 3.49MIN: 2.07 / MAX: 3.61

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_lscb0.67731.35462.03192.70923.3865SE +/- 0.01, N = 32.983.012.97MIN: 1.88 / MAX: 3.59MIN: 2.1 / MAX: 3.55MIN: 2.06 / MAX: 3.53

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-50scb246810SE +/- 0.00, N = 36.556.476.49

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: 64 - Model: ResNet-50scb3691215SE +/- 0.11, N = 510.0010.0510.10MIN: 5.78 / MAX: 12.3MIN: 5.43 / MAX: 12.02MIN: 6.91 / MAX: 12.22

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: CTS2scb1.2M2.4M3.6M4.8M6MSE +/- 13169.83, N = 35801667577000058150001. (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: 32 - Model: ResNet-50scb246810SE +/- 0.01, N = 36.666.636.64

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 P1scb1.3M2.6M3.9M5.2M6.5MSE +/- 13920.41, N = 36085667607100060900001. (CXX) g++ options: -fopenmp -O3 -march=native

OpenBenchmarking.orgFigure Of Merit, More Is BetterQuicksilver 20230818Input: CORAL2 P2scb2M4M6M8M10MSE +/- 29059.33, N = 31138666711370000113900001. (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: 64 - Model: ResNet-50scb246810SE +/- 0.01, N = 36.676.686.68