machine learning

AMD Ryzen 9 3900X 12-Core testing with a MSI X570-A PRO (MS-7C37) v3.0 (H.70 BIOS) and NVIDIA GeForce RTX 3060 12GB 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 2404285-VPA1-DESKTOP14
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mantic
February 23
  15 Hours, 54 Minutes
mantic-no-omit-framepointer
February 24
  19 Hours, 11 Minutes
Invert Behavior (Only Show Selected Data)
  17 Hours, 32 Minutes
Only show results matching title/arguments (delimit multiple options with a comma):
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mantic

Processor: AMD Ryzen 9 3900X 12-Core @ 3.80GHz (12 Cores / 24 Threads), Motherboard: MSI X570-A PRO (MS-7C37) v3.0 (H.70 BIOS), Chipset: AMD Starship/Matisse, Memory: 2 x 16GB DDR4-3200MT/s F4-3200C16-16GVK, Disk: 2000GB Seagate ST2000DM006-2DM1 + 2000GB Western Digital WD20EZAZ-00G + 500GB Samsung SSD 860 + 8002GB Seagate ST8000DM004-2CX1 + 1000GB CT1000BX500SSD1 + 512GB TS512GESD310C, Graphics: NVIDIA GeForce RTX 3060 12GB, Audio: NVIDIA GA104 HD Audio, Monitor: DELL P2314H, Network: Realtek RTL8111/8168/8411

OS: Ubuntu 23.10, Kernel: 6.5.0-9-generic (x86_64), Display Server: X Server 1.21.1.7, Display Driver: NVIDIA, OpenCL: OpenCL 3.0 CUDA 12.2.146, Compiler: GCC 13.2.0 + CUDA 12.2, 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-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 Notes: Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0x8701013
Python Notes: Python 3.11.6
Security Notes: 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

mantic-no-omit-framepointer

Processor: AMD Ryzen 9 3900X 12-Core @ 3.80GHz (12 Cores / 24 Threads), Motherboard: MSI X570-A PRO (MS-7C37) v3.0 (H.70 BIOS), Chipset: AMD Starship/Matisse, Memory: 2 x 16GB DDR4-3200MT/s F4-3200C16-16GVK, Disk: 2000GB Seagate ST2000DM006-2DM1 + 2000GB Western Digital WD20EZAZ-00G + 500GB Samsung SSD 860 + 8002GB Seagate ST8000DM004-2CX1 + 1000GB CT1000BX500SSD1 + 512GB TS512GESD310C, Graphics: NVIDIA GeForce RTX 3060, Audio: NVIDIA GA104 HD Audio, Monitor: DELL P2314H, Network: Realtek RTL8111/8168/8411

OS: Ubuntu 23.10, Kernel: 6.5.0-9-generic (x86_64), Display Server: X Server 1.21.1.7, Compiler: GCC 13.2.0 + CUDA 12.2, File-System: ext4, Screen Resolution: 1920x1080

Kernel Notes: Transparent Huge Pages: madvise
Environment Notes: CXXFLAGS=-fno-omit-frame-pointer QMAKE_CFLAGS=-fno-omit-frame-pointer CFLAGS=-fno-omit-frame-pointer CFLAGS_OVERRIDE=-fno-omit-frame-pointer QMAKE_CXXFLAGS=-fno-omit-frame-pointer FFLAGS=-fno-omit-frame-pointer
Compiler Notes: --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-b9QCDx/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-b9QCDx/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 Notes: Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0x8701013
Python Notes: Python 3.11.6
Security Notes: 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

machine learningOpenBenchmarking.orgPhoronix Test SuiteAMD Ryzen 9 3900X 12-Core @ 3.80GHz (12 Cores / 24 Threads)MSI X570-A PRO (MS-7C37) v3.0 (H.70 BIOS)AMD Starship/Matisse2 x 16GB DDR4-3200MT/s F4-3200C16-16GVK2000GB Seagate ST2000DM006-2DM1 + 2000GB Western Digital WD20EZAZ-00G + 500GB Samsung SSD 860 + 8002GB Seagate ST8000DM004-2CX1 + 1000GB CT1000BX500SSD1 + 512GB TS512GESD310CNVIDIA GeForce RTX 3060 12GBNVIDIA GeForce RTX 3060NVIDIA GA104 HD AudioDELL P2314HRealtek RTL8111/8168/8411Ubuntu 23.106.5.0-9-generic (x86_64)X Server 1.21.1.7NVIDIAOpenCL 3.0 CUDA 12.2.146GCC 13.2.0 + CUDA 12.2ext41920x1080ProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDisplay ServerDisplay DriverOpenCLCompilerFile-SystemScreen ResolutionMachine Learning BenchmarksSystem Logs- Transparent Huge Pages: madvise- mantic: --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 - mantic-no-omit-framepointer: --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-b9QCDx/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-b9QCDx/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: 0x8701013- 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 - mantic-no-omit-framepointer: CXXFLAGS=-fno-omit-frame-pointer QMAKE_CFLAGS=-fno-omit-frame-pointer CFLAGS=-fno-omit-frame-pointer CFLAGS_OVERRIDE=-fno-omit-frame-pointer QMAKE_CXXFLAGS=-fno-omit-frame-pointer FFLAGS=-fno-omit-frame-pointer

mantic vs. mantic-no-omit-framepointer ComparisonPhoronix Test SuiteBaseline+12.5%+12.5%+25%+25%+37.5%+37.5%50%12.5%12.5%6.9%5.2%3.7%2.6%2.3%2.3%2%GPU - Numpy - 16384 - Equation of StateIsolation Forest16.2%GPU - Numpy - 16384 - Isoneutral MixingCPU - Numpy - 16384 - Isoneutral MixingTree9.6%NVIDIA CUDA GPU - 16 - Efficientnet_v2_l7.9%GPU - Numpy - 262144 - Equation of Statejson_loads6.7%LocalOutlierFactor6.2%NVIDIA CUDA GPU - 1 - Efficientnet_v2_l5.5%CPU - Numpy - 262144 - Equation of StateText Vectorizers5%raytrace4.6%Plot Neighborsdjango_template3.5%regex_compile3.4%S.R.P.1.I2.9%NVIDIA CUDA GPU - 64 - ResNet-152pathlib2.5%GPU - Numpy - 262144 - Isoneutral Mixingcrypto_pyaes2.3%NVIDIA CUDA GPU - 1 - ResNet-1522.3%NVIDIA CUDA GPU - 64 - ResNet-50I.P.L2.2%H.G.B.A2.1%NVIDIA CUDA GPU - 256 - Efficientnet_v2_l2.1%T.F.A.T.T2.1%S.W.R2%NVIDIA CUDA GPU - 512 - ResNet-152PyHPC BenchmarksScikit-LearnPyHPC BenchmarksPyHPC BenchmarksScikit-LearnPyTorchPyHPC BenchmarksPyPerformanceScikit-LearnPyTorchPyHPC BenchmarksScikit-LearnPyPerformanceScikit-LearnPyPerformancePyPerformanceScikit-LearnPyTorchPyPerformancePyHPC BenchmarksPyPerformancePyTorchPyTorchScikit-LearnScikit-LearnPyTorchPyBenchScikit-LearnPyTorchmanticmantic-no-omit-framepointer

machine learningpyhpc: GPU - Numpy - 16384 - Isoneutral Mixingpyhpc: CPU - Numpy - 16384 - Isoneutral Mixingscikit-learn: Treepytorch: NVIDIA CUDA GPU - 16 - Efficientnet_v2_lpyhpc: GPU - Numpy - 262144 - Equation of Statepyperformance: json_loadsscikit-learn: LocalOutlierFactorpytorch: NVIDIA CUDA GPU - 1 - Efficientnet_v2_lpyhpc: CPU - Numpy - 262144 - Equation of Statescikit-learn: Text Vectorizerspyperformance: raytracescikit-learn: Plot Neighborspyperformance: django_templatepyperformance: regex_compilescikit-learn: Sparse Rand Projections / 100 Iterationspytorch: NVIDIA CUDA GPU - 64 - ResNet-152pyperformance: pathlibpyhpc: GPU - Numpy - 262144 - Isoneutral Mixingpyperformance: crypto_pyaespytorch: NVIDIA CUDA GPU - 1 - ResNet-152pytorch: NVIDIA CUDA GPU - 64 - ResNet-50scikit-learn: Isotonic / Perturbed Logarithmscikit-learn: Hist Gradient Boosting Adultpytorch: NVIDIA CUDA GPU - 256 - Efficientnet_v2_lpybench: Total For Average Test Timesscikit-learn: Sample Without Replacementpytorch: NVIDIA CUDA GPU - 512 - ResNet-152scikit-learn: Kernel PCA Solvers / Time vs. N Componentspytorch: NVIDIA CUDA GPU - 64 - Efficientnet_v2_lpyhpc: CPU - Numpy - 4194304 - Isoneutral Mixingpytorch: NVIDIA CUDA GPU - 256 - ResNet-152pytorch: CPU - 32 - ResNet-152pytorch: NVIDIA CUDA GPU - 32 - ResNet-50pyhpc: GPU - Numpy - 4194304 - Isoneutral Mixingpyperformance: gopyperformance: pickle_pure_pythonscikit-learn: Hist Gradient Boosting Categorical Onlypytorch: NVIDIA CUDA GPU - 32 - Efficientnet_v2_lscikit-learn: Covertype Dataset Benchmarkscikit-learn: Sparsifypyhpc: GPU - Numpy - 1048576 - Isoneutral Mixingpytorch: CPU - 256 - ResNet-152scikit-learn: Plot Hierarchicalscikit-learn: Feature Expansionspyperformance: 2to3pyperformance: chaosscikit-learn: Plot OMP vs. LARSpyperformance: nbodyscikit-learn: Hist Gradient Boostingpyhpc: GPU - Numpy - 1048576 - Equation of Statescikit-learn: SGD Regressionpytorch: NVIDIA CUDA GPU - 32 - ResNet-152pytorch: NVIDIA CUDA GPU - 16 - ResNet-152pytorch: NVIDIA CUDA GPU - 512 - ResNet-50pyhpc: GPU - Numpy - 4194304 - Equation of Statepyhpc: CPU - Numpy - 262144 - Isoneutral Mixingscikit-learn: SGDOneClassSVMpyperformance: floatpytorch: CPU - 512 - ResNet-152pytorch: CPU - 512 - Efficientnet_v2_lscikit-learn: SAGApytorch: CPU - 64 - ResNet-50pytorch: CPU - 512 - ResNet-50pytorch: NVIDIA CUDA GPU - 512 - Efficientnet_v2_lpytorch: CPU - 1 - ResNet-50numpy: pytorch: CPU - 256 - Efficientnet_v2_lpytorch: CPU - 64 - Efficientnet_v2_lscikit-learn: GLMscikit-learn: Kernel PCA Solvers / Time vs. N Samplespytorch: CPU - 16 - ResNet-152scikit-learn: 20 Newsgroups / Logistic Regressionscikit-learn: Plot Wardpytorch: CPU - 1 - ResNet-152scikit-learn: Lassopytorch: CPU - 16 - ResNet-50pyperformance: python_startuppyhpc: CPU - Numpy - 1048576 - Equation of Statepytorch: CPU - 64 - ResNet-152pytorch: NVIDIA CUDA GPU - 1 - ResNet-50pytorch: CPU - 32 - ResNet-50pytorch: NVIDIA CUDA GPU - 256 - ResNet-50scikit-learn: Plot Polynomial Kernel Approximationpyhpc: CPU - Numpy - 4194304 - Equation of Statepytorch: CPU - 256 - ResNet-50pytorch: CPU - 32 - Efficientnet_v2_lpytorch: CPU - 16 - Efficientnet_v2_lscikit-learn: MNIST Datasetscikit-learn: Plot Incremental PCApyhpc: CPU - Numpy - 1048576 - Isoneutral Mixingscikit-learn: Hist Gradient Boosting Threadingpytorch: CPU - 1 - Efficientnet_v2_lscikit-learn: Isotonic / Logisticpytorch: NVIDIA CUDA GPU - 16 - ResNet-50scikit-learn: TSNE MNIST Datasetpyhpc: GPU - Numpy - 65536 - Isoneutral Mixingpyhpc: GPU - Numpy - 65536 - Equation of Statepyhpc: CPU - Numpy - 65536 - Isoneutral Mixingpyhpc: CPU - Numpy - 65536 - Equation of Statepyhpc: CPU - Numpy - 16384 - Equation of Statescikit-learn: Isolation Forestpyhpc: GPU - Numpy - 16384 - Equation of Statemanticmantic-no-omit-framepointer0.0090.00948.33838.950.06219.553.46439.350.06160.814262147.75228.5116613.54771.8119.70.13165.173.91201.411788.259103.49737.36774158.26272.3137.24237.882.67071.749.84199.462.66212925918.57937.71376.145127.2820.6319.77211.286131.27722162.891.49976.2109.9840.263106.31574.1573.01203.181.4220.131379.73967.49.875.61868.01824.2424.1337.4332.36426.285.615.62293.59872.5419.8841.51957.82412.72511.84824.287.610.2639.88210.8824.29202.72150.7321.40224.425.635.6365.76331.0060.619110.2157.311470.806200.30236.8650.0330.0150.0320.0150.003289.3710.0030.0080.00852.96936.100.05820.856.75437.290.05863.875274142.45129.5120631.07173.6520.20.12866.672.27205.951828.300105.64736.60790161.46073.7537.88937.242.62672.9110.00202.682.62013126318.86537.16370.694125.4420.6229.91208.391133.09222463.692.58277.1111.2550.260107.52773.3672.24201.141.4110.132382.61166.99.805.65873.82224.4024.2837.2232.54428.615.645.65295.09672.9099.9341.72857.54512.78509.53724.387.640.2629.91211.4624.35203.22150.3761.40524.375.645.6465.87731.0570.618110.3747.321471.834200.17236.7860.0330.0150.0320.0150.003336.3720.002OpenBenchmarking.org

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: GPU - Backend: Numpy - Project Size: 16384 - Benchmark: Isoneutral Mixingmantic-no-omit-framepointermantic0.0020.0040.0060.0080.01SE +/- 0.000, N = 3SE +/- 0.000, N = 30.0080.009

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 16384 - Benchmark: Isoneutral Mixingmantic-no-omit-framepointermantic0.0020.0040.0060.0080.01SE +/- 0.000, N = 3SE +/- 0.000, N = 30.0080.009

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Treemantic-no-omit-framepointermantic1224364860SE +/- 0.48, N = 15SE +/- 0.59, N = 452.9748.341. (F9X) gfortran options: -O0

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: NVIDIA CUDA GPU - Batch Size: 16 - Model: Efficientnet_v2_lmantic-no-omit-framepointermantic918273645SE +/- 0.02, N = 3SE +/- 0.08, N = 336.1038.95MIN: 34.25 / MAX: 38.01MIN: 37.12 / MAX: 39.27

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: GPU - Backend: Numpy - Project Size: 262144 - Benchmark: Equation of Statemantic-no-omit-framepointermantic0.0140.0280.0420.0560.07SE +/- 0.000, N = 3SE +/- 0.001, N = 30.0580.062

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_loadsmantic-no-omit-framepointermantic510152025SE +/- 0.03, N = 3SE +/- 0.06, N = 320.819.5

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: LocalOutlierFactormantic-no-omit-framepointermantic1326395265SE +/- 0.74, N = 15SE +/- 0.18, N = 356.7553.461. (F9X) gfortran options: -O0

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: NVIDIA CUDA GPU - Batch Size: 1 - Model: Efficientnet_v2_lmantic-no-omit-framepointermantic918273645SE +/- 0.26, N = 3SE +/- 0.47, N = 337.2939.35MIN: 35.83 / MAX: 39.17MIN: 36.65 / MAX: 40.42

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 262144 - Benchmark: Equation of Statemantic-no-omit-framepointermantic0.01370.02740.04110.05480.0685SE +/- 0.000, N = 3SE +/- 0.001, N = 30.0580.061

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Text Vectorizersmantic-no-omit-framepointermantic1428425670SE +/- 0.08, N = 3SE +/- 0.19, N = 363.8860.811. (F9X) gfortran options: -O0

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: raytracemantic-no-omit-framepointermantic60120180240300SE +/- 0.33, N = 3SE +/- 0.33, N = 3274262

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Neighborsmantic-no-omit-framepointermantic306090120150SE +/- 0.59, N = 3SE +/- 1.34, N = 7142.45147.751. (F9X) gfortran options: -O0

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_templatemantic-no-omit-framepointermantic714212835SE +/- 0.06, N = 3SE +/- 0.03, N = 329.528.5

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: regex_compilemantic-no-omit-framepointermantic306090120150SE +/- 0.33, N = 3SE +/- 0.00, N = 3120116

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sparse Random Projections / 100 Iterationsmantic-no-omit-framepointermantic140280420560700SE +/- 7.06, N = 4SE +/- 3.80, N = 3631.07613.551. (F9X) gfortran options: -O0

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: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-152mantic-no-omit-framepointermantic1632486480SE +/- 0.66, N = 3SE +/- 0.44, N = 373.6571.81MIN: 68.88 / MAX: 75.03MIN: 67.31 / MAX: 72.89

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: pathlibmantic-no-omit-framepointermantic510152025SE +/- 0.00, N = 3SE +/- 0.00, N = 320.219.7

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: GPU - Backend: Numpy - Project Size: 262144 - Benchmark: Isoneutral Mixingmantic-no-omit-framepointermantic0.02950.0590.08850.1180.1475SE +/- 0.001, N = 3SE +/- 0.000, N = 30.1280.131

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_pyaesmantic-no-omit-framepointermantic1530456075SE +/- 0.00, N = 3SE +/- 0.06, N = 366.665.1

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: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-152mantic-no-omit-framepointermantic1632486480SE +/- 0.96, N = 3SE +/- 0.56, N = 372.2773.91MIN: 68.86 / MAX: 76.62MIN: 68.9 / MAX: 75.9

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50mantic-no-omit-framepointermantic50100150200250SE +/- 1.98, N = 3SE +/- 0.58, N = 3205.95201.41MIN: 186.96 / MAX: 210.21MIN: 184.02 / MAX: 203.68

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Isotonic / Perturbed Logarithmmantic-no-omit-framepointermantic400800120016002000SE +/- 16.46, N = 3SE +/- 24.41, N = 31828.301788.261. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Adultmantic-no-omit-framepointermantic20406080100SE +/- 0.59, N = 3SE +/- 0.70, N = 3105.65103.501. (F9X) gfortran options: -O0

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: NVIDIA CUDA GPU - Batch Size: 256 - Model: Efficientnet_v2_lmantic-no-omit-framepointermantic918273645SE +/- 0.30, N = 15SE +/- 0.15, N = 336.6037.36MIN: 33.07 / MAX: 39.53MIN: 35.47 / MAX: 37.85

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 Timesmantic-no-omit-framepointermantic2004006008001000SE +/- 1.20, N = 3SE +/- 1.00, N = 3790774

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sample Without Replacementmantic-no-omit-framepointermantic4080120160200SE +/- 0.62, N = 3SE +/- 0.60, N = 3161.46158.261. (F9X) gfortran options: -O0

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: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-152mantic-no-omit-framepointermantic1632486480SE +/- 0.50, N = 3SE +/- 0.94, N = 373.7572.31MIN: 68.91 / MAX: 75.15MIN: 67.38 / MAX: 74.62

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Kernel PCA Solvers / Time vs. N Componentsmantic-no-omit-framepointermantic918273645SE +/- 0.36, N = 3SE +/- 0.21, N = 337.8937.241. (F9X) gfortran options: -O0

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: NVIDIA CUDA GPU - Batch Size: 64 - Model: Efficientnet_v2_lmantic-no-omit-framepointermantic918273645SE +/- 0.31, N = 15SE +/- 0.30, N = 937.2437.88MIN: 33.97 / MAX: 39.43MIN: 35.67 / MAX: 39.63

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Isoneutral Mixingmantic-no-omit-framepointermantic0.60081.20161.80242.40323.004SE +/- 0.002, N = 3SE +/- 0.010, N = 32.6262.670

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: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-152mantic-no-omit-framepointermantic1632486480SE +/- 0.83, N = 3SE +/- 0.24, N = 372.9171.74MIN: 68 / MAX: 75.45MIN: 67.87 / MAX: 72.6

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-152mantic-no-omit-framepointermantic3691215SE +/- 0.09, N = 3SE +/- 0.05, N = 310.009.84MIN: 8.09 / MAX: 10.27MIN: 9.6 / MAX: 9.98

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50mantic-no-omit-framepointermantic4080120160200SE +/- 2.52, N = 4SE +/- 1.06, N = 3202.68199.46MIN: 182.69 / MAX: 211.53MIN: 182.77 / MAX: 206.03

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: GPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Isoneutral Mixingmantic-no-omit-framepointermantic0.5991.1981.7972.3962.995SE +/- 0.006, N = 3SE +/- 0.006, N = 32.6202.662

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: gomantic-no-omit-framepointermantic306090120150SE +/- 0.33, N = 3SE +/- 0.00, N = 3131129

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: pickle_pure_pythonmantic-no-omit-framepointermantic60120180240300SE +/- 0.58, N = 3SE +/- 0.33, N = 3263259

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Categorical Onlymantic-no-omit-framepointermantic510152025SE +/- 0.12, N = 3SE +/- 0.06, N = 318.8718.581. (F9X) gfortran options: -O0

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: NVIDIA CUDA GPU - Batch Size: 32 - Model: Efficientnet_v2_lmantic-no-omit-framepointermantic918273645SE +/- 0.30, N = 15SE +/- 0.24, N = 337.1637.71MIN: 34.12 / MAX: 39.48MIN: 35.52 / MAX: 38.25

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Covertype Dataset Benchmarkmantic-no-omit-framepointermantic80160240320400SE +/- 3.40, N = 3SE +/- 4.88, N = 3370.69376.151. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sparsifymantic-no-omit-framepointermantic306090120150SE +/- 1.28, N = 5SE +/- 1.36, N = 5125.44127.281. (F9X) gfortran options: -O0

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: GPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Isoneutral Mixingmantic-no-omit-framepointermantic0.1420.2840.4260.5680.71SE +/- 0.007, N = 3SE +/- 0.002, N = 30.6220.631

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-152mantic-no-omit-framepointermantic3691215SE +/- 0.04, N = 3SE +/- 0.07, N = 39.919.77MIN: 9.19 / MAX: 10.05MIN: 9.17 / MAX: 10

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Hierarchicalmantic-no-omit-framepointermantic50100150200250SE +/- 0.42, N = 3SE +/- 0.75, N = 3208.39211.291. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Feature Expansionsmantic-no-omit-framepointermantic306090120150SE +/- 1.22, N = 3SE +/- 0.86, N = 3133.09131.281. (F9X) gfortran options: -O0

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: 2to3mantic-no-omit-framepointermantic50100150200250SE +/- 0.33, N = 3SE +/- 0.00, N = 3224221

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: chaosmantic-no-omit-framepointermantic1428425670SE +/- 0.20, N = 3SE +/- 0.03, N = 363.662.8

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot OMP vs. LARSmantic-no-omit-framepointermantic20406080100SE +/- 0.44, N = 3SE +/- 0.08, N = 392.5891.501. (F9X) gfortran options: -O0

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: nbodymantic-no-omit-framepointermantic20406080100SE +/- 0.07, N = 3SE +/- 0.06, N = 377.176.2

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boostingmantic-no-omit-framepointermantic20406080100SE +/- 0.25, N = 3SE +/- 0.22, N = 3111.26109.981. (F9X) gfortran options: -O0

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: GPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Equation of Statemantic-no-omit-framepointermantic0.05920.11840.17760.23680.296SE +/- 0.001, N = 3SE +/- 0.002, N = 30.2600.263

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SGD Regressionmantic-no-omit-framepointermantic20406080100SE +/- 0.49, N = 3SE +/- 1.06, N = 6107.53106.321. (F9X) gfortran options: -O0

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: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-152mantic-no-omit-framepointermantic1632486480SE +/- 0.74, N = 3SE +/- 0.96, N = 373.3674.15MIN: 68.19 / MAX: 74.63MIN: 68.27 / MAX: 75.61

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-152mantic-no-omit-framepointermantic1632486480SE +/- 0.20, N = 3SE +/- 0.96, N = 372.2473.01MIN: 68.36 / MAX: 73.14MIN: 68.06 / MAX: 75.3

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-50mantic-no-omit-framepointermantic4080120160200SE +/- 0.33, N = 3SE +/- 1.69, N = 3201.14203.18MIN: 183.61 / MAX: 202.73MIN: 183.76 / MAX: 207.98

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: GPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Equation of Statemantic-no-omit-framepointermantic0.320.640.961.281.6SE +/- 0.001, N = 3SE +/- 0.004, N = 31.4111.422

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 262144 - Benchmark: Isoneutral Mixingmantic-no-omit-framepointermantic0.02970.05940.08910.11880.1485SE +/- 0.000, N = 3SE +/- 0.001, N = 30.1320.131

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SGDOneClassSVMmantic-no-omit-framepointermantic80160240320400SE +/- 3.48, N = 7SE +/- 4.18, N = 3382.61379.741. (F9X) gfortran options: -O0

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: floatmantic-no-omit-framepointermantic1530456075SE +/- 0.10, N = 3SE +/- 0.03, N = 366.967.4

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: 512 - Model: ResNet-152mantic-no-omit-framepointermantic3691215SE +/- 0.07, N = 3SE +/- 0.02, N = 39.809.87MIN: 9.12 / MAX: 9.98MIN: 9.09 / MAX: 9.96

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_lmantic-no-omit-framepointermantic1.27132.54263.81395.08526.3565SE +/- 0.02, N = 3SE +/- 0.01, N = 35.655.61MIN: 5.36 / MAX: 5.93MIN: 5.45 / MAX: 5.66

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SAGAmantic-no-omit-framepointermantic2004006008001000SE +/- 5.60, N = 3SE +/- 8.69, N = 6873.82868.021. (F9X) gfortran options: -O0

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-50mantic-no-omit-framepointermantic612182430SE +/- 0.15, N = 3SE +/- 0.04, N = 324.4024.24MIN: 21.6 / MAX: 24.8MIN: 23.59 / MAX: 24.49

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: ResNet-50mantic-no-omit-framepointermantic612182430SE +/- 0.08, N = 3SE +/- 0.02, N = 324.2824.13MIN: 22.31 / MAX: 24.53MIN: 23.58 / MAX: 24.41

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: Efficientnet_v2_lmantic-no-omit-framepointermantic918273645SE +/- 0.33, N = 8SE +/- 0.03, N = 337.2237.43MIN: 34.99 / MAX: 39.08MIN: 35.81 / MAX: 38.02

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-50mantic-no-omit-framepointermantic816243240SE +/- 0.16, N = 3SE +/- 0.11, N = 332.5432.36MIN: 31.64 / MAX: 32.94MIN: 31.89 / MAX: 32.7

Numpy Benchmark

This is a test to obtain the general Numpy performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgScore, More Is BetterNumpy Benchmarkmantic-no-omit-framepointermantic90180270360450SE +/- 0.90, N = 3SE +/- 1.20, N = 3428.61426.28

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: 256 - Model: Efficientnet_v2_lmantic-no-omit-framepointermantic1.2692.5383.8075.0766.345SE +/- 0.01, N = 3SE +/- 0.02, N = 35.645.61MIN: 5.29 / MAX: 5.68MIN: 5.44 / MAX: 5.65

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_lmantic-no-omit-framepointermantic1.27132.54263.81395.08526.3565SE +/- 0.01, N = 3SE +/- 0.01, N = 35.655.62MIN: 5.45 / MAX: 5.7MIN: 5.35 / MAX: 5.66

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: GLMmantic-no-omit-framepointermantic60120180240300SE +/- 1.07, N = 3SE +/- 1.06, N = 3295.10293.601. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Kernel PCA Solvers / Time vs. N Samplesmantic-no-omit-framepointermantic1632486480SE +/- 0.16, N = 3SE +/- 0.05, N = 372.9172.541. (F9X) gfortran options: -O0

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: 16 - Model: ResNet-152mantic-no-omit-framepointermantic3691215SE +/- 0.01, N = 3SE +/- 0.04, N = 39.939.88MIN: 9.39 / MAX: 10.01MIN: 9.31 / MAX: 10.01

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: 20 Newsgroups / Logistic Regressionmantic-no-omit-framepointermantic1020304050SE +/- 0.24, N = 3SE +/- 0.19, N = 341.7341.521. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Wardmantic-no-omit-framepointermantic1326395265SE +/- 0.22, N = 3SE +/- 0.21, N = 357.5557.821. (F9X) gfortran options: -O0

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-152mantic-no-omit-framepointermantic3691215SE +/- 0.04, N = 3SE +/- 0.03, N = 312.7812.72MIN: 11.9 / MAX: 12.9MIN: 11.99 / MAX: 12.8

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Lassomantic-no-omit-framepointermantic110220330440550SE +/- 3.50, N = 3SE +/- 3.22, N = 3509.54511.851. (F9X) gfortran options: -O0

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: 16 - Model: ResNet-50mantic-no-omit-framepointermantic612182430SE +/- 0.16, N = 3SE +/- 0.05, N = 324.3824.28MIN: 22.2 / MAX: 24.87MIN: 20.22 / MAX: 24.56

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: python_startupmantic-no-omit-framepointermantic246810SE +/- 0.01, N = 3SE +/- 0.01, N = 37.647.61

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Equation of Statemantic-no-omit-framepointermantic0.05920.11840.17760.23680.296SE +/- 0.000, N = 3SE +/- 0.002, N = 30.2620.263

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-152mantic-no-omit-framepointermantic3691215SE +/- 0.02, N = 3SE +/- 0.03, N = 39.919.88MIN: 8.69 / MAX: 10.08MIN: 8.8 / MAX: 9.98

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-50mantic-no-omit-framepointermantic50100150200250SE +/- 1.46, N = 15SE +/- 2.67, N = 3211.46210.88MIN: 192.13 / MAX: 223.01MIN: 195.21 / MAX: 218.16

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-50mantic-no-omit-framepointermantic612182430SE +/- 0.16, N = 3SE +/- 0.10, N = 324.3524.29MIN: 23.67 / MAX: 24.87MIN: 22.24 / MAX: 24.66

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50mantic-no-omit-framepointermantic4080120160200SE +/- 1.21, N = 3SE +/- 1.76, N = 3203.22202.72MIN: 185.88 / MAX: 206.71MIN: 183.1 / MAX: 207.93

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Polynomial Kernel Approximationmantic-no-omit-framepointermantic306090120150SE +/- 1.20, N = 3SE +/- 1.22, N = 3150.38150.731. (F9X) gfortran options: -O0

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Equation of Statemantic-no-omit-framepointermantic0.31610.63220.94831.26441.5805SE +/- 0.004, N = 3SE +/- 0.003, N = 31.4051.402

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-50mantic-no-omit-framepointermantic612182430SE +/- 0.11, N = 3SE +/- 0.03, N = 324.3724.42MIN: 23.76 / MAX: 24.81MIN: 20.15 / MAX: 24.74

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_lmantic-no-omit-framepointermantic1.2692.5383.8075.0766.345SE +/- 0.01, N = 3SE +/- 0.01, N = 35.645.63MIN: 5.52 / MAX: 5.69MIN: 5.31 / MAX: 5.68

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_lmantic-no-omit-framepointermantic1.2692.5383.8075.0766.345SE +/- 0.01, N = 3SE +/- 0.02, N = 35.645.63MIN: 5.45 / MAX: 5.68MIN: 5.39 / MAX: 5.71

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: MNIST Datasetmantic-no-omit-framepointermantic1530456075SE +/- 0.47, N = 3SE +/- 0.82, N = 465.8865.761. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Incremental PCAmantic-no-omit-framepointermantic714212835SE +/- 0.07, N = 3SE +/- 0.03, N = 331.0631.011. (F9X) gfortran options: -O0

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Isoneutral Mixingmantic-no-omit-framepointermantic0.13930.27860.41790.55720.6965SE +/- 0.000, N = 3SE +/- 0.001, N = 30.6180.619

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Threadingmantic-no-omit-framepointermantic20406080100SE +/- 0.15, N = 3SE +/- 0.13, N = 3110.37110.221. (F9X) gfortran options: -O0

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_lmantic-no-omit-framepointermantic246810SE +/- 0.02, N = 3SE +/- 0.00, N = 37.327.31MIN: 7.23 / MAX: 7.38MIN: 7.16 / MAX: 7.34

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Isotonic / Logisticmantic-no-omit-framepointermantic30060090012001500SE +/- 14.46, N = 3SE +/- 12.29, N = 31471.831470.811. (F9X) gfortran options: -O0

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: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50mantic-no-omit-framepointermantic4080120160200SE +/- 0.96, N = 3SE +/- 0.25, N = 3200.17200.30MIN: 183.43 / MAX: 203.55MIN: 182.88 / MAX: 202.36

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: TSNE MNIST Datasetmantic-no-omit-framepointermantic50100150200250SE +/- 0.54, N = 3SE +/- 0.44, N = 3236.79236.871. (F9X) gfortran options: -O0

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: GPU - Backend: Numpy - Project Size: 65536 - Benchmark: Isoneutral Mixingmantic-no-omit-framepointermantic0.00740.01480.02220.02960.037SE +/- 0.000, N = 3SE +/- 0.000, N = 30.0330.033

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: GPU - Backend: Numpy - Project Size: 65536 - Benchmark: Equation of Statemantic-no-omit-framepointermantic0.00340.00680.01020.01360.017SE +/- 0.000, N = 3SE +/- 0.000, N = 30.0150.015

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 65536 - Benchmark: Isoneutral Mixingmantic-no-omit-framepointermantic0.00720.01440.02160.02880.036SE +/- 0.000, N = 3SE +/- 0.000, N = 30.0320.032

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 65536 - Benchmark: Equation of Statemantic-no-omit-framepointermantic0.00340.00680.01020.01360.017SE +/- 0.000, N = 3SE +/- 0.000, N = 30.0150.015

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 16384 - Benchmark: Equation of Statemantic-no-omit-framepointermantic0.00070.00140.00210.00280.0035SE +/- 0.000, N = 3SE +/- 0.000, N = 30.0030.003

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

Benchmark: Plot Singular Value Decomposition

mantic-no-omit-framepointer: The test quit with a non-zero exit status. E: AttributeError: type object 'Axis' has no attribute '_set_ticklabels'. Did you mean: 'set_ticklabels'?

Benchmark: RCV1 Logreg Convergencet

mantic-no-omit-framepointer: The test quit with a non-zero exit status. E: IndexError: list index out of range

Benchmark: Isotonic / Pathological

mantic-no-omit-framepointer: The test quit with a non-zero exit status.

Benchmark: Plot Parallel Pairwise

mantic-no-omit-framepointer: The test quit with a non-zero exit status. E: numpy.core._exceptions._ArrayMemoryError: Unable to allocate 74.5 GiB for an array with shape (100000, 100000) and data type float64

Benchmark: Plot Fast KMeans

mantic-no-omit-framepointer: The test quit with a non-zero exit status. E: AttributeError: type object 'Axis' has no attribute '_set_ticklabels'. Did you mean: 'set_ticklabels'?

Benchmark: Plot Lasso Path

mantic-no-omit-framepointer: The test quit with a non-zero exit status. E: AttributeError: type object 'Axis' has no attribute '_set_ticklabels'. Did you mean: 'set_ticklabels'?

Benchmark: Glmnet

mantic-no-omit-framepointer: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'glmnet'

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

Device: GPU - Backend: TensorFlow - Project Size: 4194304 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: TensorFlow - Project Size: 4194304 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: TensorFlow - Project Size: 1048576 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: TensorFlow - Project Size: 1048576 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 4194304 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 4194304 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 1048576 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 1048576 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: TensorFlow - Project Size: 262144 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: TensorFlow - Project Size: 262144 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 262144 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 262144 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: TensorFlow - Project Size: 65536 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: TensorFlow - Project Size: 65536 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: TensorFlow - Project Size: 16384 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: TensorFlow - Project Size: 16384 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 65536 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 65536 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 16384 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 16384 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: PyTorch - Project Size: 4194304 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: PyTorch - Project Size: 4194304 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: PyTorch - Project Size: 1048576 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: PyTorch - Project Size: 1048576 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 4194304 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 4194304 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 1048576 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 1048576 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: PyTorch - Project Size: 262144 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: PyTorch - Project Size: 262144 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: Aesara - Project Size: 4194304 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: Aesara - Project Size: 4194304 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: Aesara - Project Size: 1048576 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: Aesara - Project Size: 1048576 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 262144 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 262144 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 4194304 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 4194304 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 1048576 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 1048576 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: PyTorch - Project Size: 65536 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: PyTorch - Project Size: 65536 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: PyTorch - Project Size: 16384 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: PyTorch - Project Size: 16384 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: Numba - Project Size: 4194304 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: Numba - Project Size: 4194304 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: Numba - Project Size: 1048576 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: Numba - Project Size: 1048576 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: Aesara - Project Size: 262144 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: Aesara - Project Size: 262144 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 65536 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 65536 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 16384 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 16384 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: Numba - Project Size: 4194304 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: Numba - Project Size: 4194304 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: Numba - Project Size: 1048576 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: Numba - Project Size: 1048576 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 262144 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 262144 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: Numba - Project Size: 262144 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: Numba - Project Size: 262144 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: Aesara - Project Size: 65536 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: Aesara - Project Size: 65536 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: Aesara - Project Size: 16384 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: Aesara - Project Size: 16384 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: Numba - Project Size: 262144 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: Numba - Project Size: 262144 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 65536 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 65536 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 16384 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 16384 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: Numba - Project Size: 65536 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: Numba - Project Size: 65536 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: Numba - Project Size: 16384 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: Numba - Project Size: 16384 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: JAX - Project Size: 4194304 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: JAX - Project Size: 4194304 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: JAX - Project Size: 1048576 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: JAX - Project Size: 1048576 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: Numba - Project Size: 65536 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: Numba - Project Size: 65536 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: Numba - Project Size: 16384 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: Numba - Project Size: 16384 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 4194304 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 4194304 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 1048576 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 1048576 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: JAX - Project Size: 262144 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: JAX - Project Size: 262144 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 262144 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 262144 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: JAX - Project Size: 65536 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: JAX - Project Size: 65536 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: JAX - Project Size: 16384 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: GPU - Backend: JAX - Project Size: 16384 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 65536 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 65536 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 16384 - Benchmark: Isoneutral Mixing

mantic-no-omit-framepointer: The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 16384 - Benchmark: Equation of State

mantic-no-omit-framepointer: The test run did not produce a result.

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Isolation Forestmantic-no-omit-framepointermantic70140210280350SE +/- 51.04, N = 9SE +/- 1.30, N = 3336.37289.371. (F9X) gfortran options: -O0

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: GPU - Backend: Numpy - Project Size: 16384 - Benchmark: Equation of Statemantic-no-omit-framepointermantic0.00070.00140.00210.00280.0035SE +/- 0.000, N = 15SE +/- 0.000, N = 30.0020.003

102 Results Shown

PyHPC Benchmarks:
  GPU - Numpy - 16384 - Isoneutral Mixing
  CPU - Numpy - 16384 - Isoneutral Mixing
Scikit-Learn
PyTorch
PyHPC Benchmarks
PyPerformance
Scikit-Learn
PyTorch
PyHPC Benchmarks
Scikit-Learn
PyPerformance
Scikit-Learn
PyPerformance:
  django_template
  regex_compile
Scikit-Learn
PyTorch
PyPerformance
PyHPC Benchmarks
PyPerformance
PyTorch:
  NVIDIA CUDA GPU - 1 - ResNet-152
  NVIDIA CUDA GPU - 64 - ResNet-50
Scikit-Learn:
  Isotonic / Perturbed Logarithm
  Hist Gradient Boosting Adult
PyTorch
PyBench
Scikit-Learn
PyTorch
Scikit-Learn
PyTorch
PyHPC Benchmarks
PyTorch:
  NVIDIA CUDA GPU - 256 - ResNet-152
  CPU - 32 - ResNet-152
  NVIDIA CUDA GPU - 32 - ResNet-50
PyHPC Benchmarks
PyPerformance:
  go
  pickle_pure_python
Scikit-Learn
PyTorch
Scikit-Learn:
  Covertype Dataset Benchmark
  Sparsify
PyHPC Benchmarks
PyTorch
Scikit-Learn:
  Plot Hierarchical
  Feature Expansions
PyPerformance:
  2to3
  chaos
Scikit-Learn
PyPerformance
Scikit-Learn
PyHPC Benchmarks
Scikit-Learn
PyTorch:
  NVIDIA CUDA GPU - 32 - ResNet-152
  NVIDIA CUDA GPU - 16 - ResNet-152
  NVIDIA CUDA GPU - 512 - ResNet-50
PyHPC Benchmarks:
  GPU - Numpy - 4194304 - Equation of State
  CPU - Numpy - 262144 - Isoneutral Mixing
Scikit-Learn
PyPerformance
PyTorch:
  CPU - 512 - ResNet-152
  CPU - 512 - Efficientnet_v2_l
Scikit-Learn
PyTorch:
  CPU - 64 - ResNet-50
  CPU - 512 - ResNet-50
  NVIDIA CUDA GPU - 512 - Efficientnet_v2_l
  CPU - 1 - ResNet-50
Numpy Benchmark
PyTorch:
  CPU - 256 - Efficientnet_v2_l
  CPU - 64 - Efficientnet_v2_l
Scikit-Learn:
  GLM
  Kernel PCA Solvers / Time vs. N Samples
PyTorch
Scikit-Learn:
  20 Newsgroups / Logistic Regression
  Plot Ward
PyTorch
Scikit-Learn
PyTorch
PyPerformance
PyHPC Benchmarks
PyTorch:
  CPU - 64 - ResNet-152
  NVIDIA CUDA GPU - 1 - ResNet-50
  CPU - 32 - ResNet-50
  NVIDIA CUDA GPU - 256 - ResNet-50
Scikit-Learn
PyHPC Benchmarks
PyTorch:
  CPU - 256 - ResNet-50
  CPU - 32 - Efficientnet_v2_l
  CPU - 16 - Efficientnet_v2_l
Scikit-Learn:
  MNIST Dataset
  Plot Incremental PCA
PyHPC Benchmarks
Scikit-Learn
PyTorch
Scikit-Learn
PyTorch
Scikit-Learn
PyHPC Benchmarks:
  GPU - Numpy - 65536 - Isoneutral Mixing
  GPU - Numpy - 65536 - Equation of State
  CPU - Numpy - 65536 - Isoneutral Mixing
  CPU - Numpy - 65536 - Equation of State
  CPU - Numpy - 16384 - Equation of State
Scikit-Learn
PyHPC Benchmarks

mantic

Processor: AMD Ryzen 9 3900X 12-Core @ 3.80GHz (12 Cores / 24 Threads), Motherboard: MSI X570-A PRO (MS-7C37) v3.0 (H.70 BIOS), Chipset: AMD Starship/Matisse, Memory: 2 x 16GB DDR4-3200MT/s F4-3200C16-16GVK, Disk: 2000GB Seagate ST2000DM006-2DM1 + 2000GB Western Digital WD20EZAZ-00G + 500GB Samsung SSD 860 + 8002GB Seagate ST8000DM004-2CX1 + 1000GB CT1000BX500SSD1 + 512GB TS512GESD310C, Graphics: NVIDIA GeForce RTX 3060 12GB, Audio: NVIDIA GA104 HD Audio, Monitor: DELL P2314H, Network: Realtek RTL8111/8168/8411

OS: Ubuntu 23.10, Kernel: 6.5.0-9-generic (x86_64), Display Server: X Server 1.21.1.7, Display Driver: NVIDIA, OpenCL: OpenCL 3.0 CUDA 12.2.146, Compiler: GCC 13.2.0 + CUDA 12.2, 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-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 Notes: Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0x8701013
Python Notes: Python 3.11.6
Security Notes: 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

Testing initiated at 23 February 2024 06:44 by user ubuntu.

mantic-no-omit-framepointer

Processor: AMD Ryzen 9 3900X 12-Core @ 3.80GHz (12 Cores / 24 Threads), Motherboard: MSI X570-A PRO (MS-7C37) v3.0 (H.70 BIOS), Chipset: AMD Starship/Matisse, Memory: 2 x 16GB DDR4-3200MT/s F4-3200C16-16GVK, Disk: 2000GB Seagate ST2000DM006-2DM1 + 2000GB Western Digital WD20EZAZ-00G + 500GB Samsung SSD 860 + 8002GB Seagate ST8000DM004-2CX1 + 1000GB CT1000BX500SSD1 + 512GB TS512GESD310C, Graphics: NVIDIA GeForce RTX 3060, Audio: NVIDIA GA104 HD Audio, Monitor: DELL P2314H, Network: Realtek RTL8111/8168/8411

OS: Ubuntu 23.10, Kernel: 6.5.0-9-generic (x86_64), Display Server: X Server 1.21.1.7, Compiler: GCC 13.2.0 + CUDA 12.2, File-System: ext4, Screen Resolution: 1920x1080

Kernel Notes: Transparent Huge Pages: madvise
Environment Notes: CXXFLAGS=-fno-omit-frame-pointer QMAKE_CFLAGS=-fno-omit-frame-pointer CFLAGS=-fno-omit-frame-pointer CFLAGS_OVERRIDE=-fno-omit-frame-pointer QMAKE_CXXFLAGS=-fno-omit-frame-pointer FFLAGS=-fno-omit-frame-pointer
Compiler Notes: --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-b9QCDx/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-b9QCDx/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 Notes: Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0x8701013
Python Notes: Python 3.11.6
Security Notes: 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

Testing initiated at 24 February 2024 09:36 by user ubuntu.