Desktop 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.

HTML result view exported from: https://openbenchmarking.org/result/2405015-VPA1-DESKTOP46&sor&grs.

Desktop machine learningProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDisplay ServerDisplay DriverOpenCLCompilerFile-SystemScreen Resolutionmanticmantic-no-omit-framepointernobleAMD 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 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 12.2ext41920x1080NVIDIA GeForce RTX 3060DELL P2314H + U32J59xRealtek RTL8111/8168/8211/8411Ubuntu 24.046.8.0-31-generic (x86_64)GCC 13.2.0OpenBenchmarking.orgKernel Details- Transparent Huge Pages: madviseCompiler Details- 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 - noble: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --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-backtrace --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-uJ7kn6/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-uJ7kn6/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-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: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0x8701013Python Details- mantic: Python 3.11.6- mantic-no-omit-framepointer: Python 3.11.6- noble: Python 3.12.3Security Details- mantic: 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: 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 - noble: gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + reg_file_data_sampling: 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; BHI: Not affected + srbds: Not affected + tsx_async_abort: Not affected Environment Details- 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 - noble: CXXFLAGS="-fno-omit-frame-pointer -frecord-gcc-switches -O2" QMAKE_CFLAGS="-fno-omit-frame-pointer -frecord-gcc-switches -O2" CFLAGS="-fno-omit-frame-pointer -frecord-gcc-switches -O2" CFLAGS_OVERRIDE="-fno-omit-frame-pointer -frecord-gcc-switches -O2" QMAKE_CXXFLAGS="-fno-omit-frame-pointer -frecord-gcc-switches -O2" FFLAGS="-fno-omit-frame-pointer -frecord-gcc-switches -O2"

Desktop machine learningscikit-learn: Lassoscikit-learn: SGD Regressionscikit-learn: Plot OMP vs. LARSscikit-learn: TSNE MNIST Datasetpyperformance: json_loadspyperformance: python_startupscikit-learn: Isotonic / Logisticscikit-learn: Sample Without Replacementscikit-learn: Treepyhpc: GPU - Numpy - 16384 - Isoneutral Mixingpyhpc: CPU - Numpy - 16384 - Isoneutral Mixingscikit-learn: Isotonic / Perturbed Logarithmscikit-learn: GLMscikit-learn: Text Vectorizersscikit-learn: Hist Gradient Boosting Adultpybench: Total For Average Test Timespyperformance: goscikit-learn: Sparse Rand Projections / 100 Iterationspytorch: NVIDIA CUDA GPU - 16 - Efficientnet_v2_lscikit-learn: Hist Gradient Boosting Categorical Onlypyhpc: GPU - Numpy - 262144 - Equation of Statescikit-learn: Hist Gradient Boostingpyhpc: CPU - Numpy - 65536 - Equation of Statepyhpc: GPU - Numpy - 262144 - Isoneutral Mixingscikit-learn: LocalOutlierFactorpytorch: NVIDIA CUDA GPU - 1 - Efficientnet_v2_lpyhpc: CPU - Numpy - 262144 - Equation of Statepyperformance: raytracescikit-learn: Kernel PCA Solvers / Time vs. N Samplesscikit-learn: Plot Neighborsscikit-learn: Plot Polynomial Kernel Approximationpyhpc: CPU - Numpy - 4194304 - Isoneutral Mixingpyperformance: django_templatepyperformance: regex_compilepyhpc: CPU - Numpy - 65536 - Isoneutral Mixingpyhpc: GPU - Numpy - 65536 - Isoneutral Mixingscikit-learn: Plot Wardscikit-learn: Covertype Dataset Benchmarkpytorch: NVIDIA CUDA GPU - 64 - ResNet-152pyperformance: pathlibpyhpc: GPU - Numpy - 4194304 - Equation of Statepyhpc: CPU - Numpy - 4194304 - Equation of Statepyperformance: crypto_pyaespytorch: NVIDIA CUDA GPU - 1 - ResNet-152pytorch: NVIDIA CUDA GPU - 64 - ResNet-50scikit-learn: Kernel PCA Solvers / Time vs. N Componentspyhpc: CPU - Numpy - 1048576 - Isoneutral Mixingpytorch: NVIDIA CUDA GPU - 256 - Efficientnet_v2_lscikit-learn: Plot Hierarchicalpytorch: NVIDIA CUDA GPU - 512 - ResNet-152pytorch: CPU - 32 - ResNet-152pyhpc: GPU - Numpy - 4194304 - Isoneutral Mixingscikit-learn: Sparsifypytorch: NVIDIA CUDA GPU - 64 - Efficientnet_v2_lpytorch: NVIDIA CUDA GPU - 256 - ResNet-152pytorch: NVIDIA CUDA GPU - 32 - ResNet-50pyperformance: pickle_pure_pythonpyhpc: CPU - Numpy - 262144 - Isoneutral Mixingscikit-learn: SGDOneClassSVMpytorch: NVIDIA CUDA GPU - 32 - Efficientnet_v2_lpyhpc: GPU - Numpy - 1048576 - Isoneutral Mixingscikit-learn: Plot Incremental PCApytorch: CPU - 256 - ResNet-152scikit-learn: Feature Expansionspyperformance: 2to3pytorch: CPU - 1 - ResNet-152pyperformance: chaosscikit-learn: Hist Gradient Boosting Threadingpyperformance: nbodypyhpc: GPU - Numpy - 1048576 - Equation of Statepytorch: NVIDIA CUDA GPU - 32 - ResNet-152numpy: pytorch: NVIDIA CUDA GPU - 16 - ResNet-152pytorch: NVIDIA CUDA GPU - 512 - ResNet-50pytorch: CPU - 32 - ResNet-50scikit-learn: 20 Newsgroups / Logistic Regressionpytorch: CPU - 32 - Efficientnet_v2_lpytorch: CPU - 16 - Efficientnet_v2_lpytorch: CPU - 512 - Efficientnet_v2_lpytorch: CPU - 64 - Efficientnet_v2_lpytorch: CPU - 64 - ResNet-50pyhpc: CPU - Numpy - 1048576 - Equation of Statepyperformance: floatpytorch: CPU - 512 - ResNet-152scikit-learn: MNIST Datasetpytorch: CPU - 512 - ResNet-50scikit-learn: SAGApytorch: CPU - 1 - ResNet-50pytorch: CPU - 16 - ResNet-50pytorch: NVIDIA CUDA GPU - 512 - Efficientnet_v2_lpytorch: CPU - 256 - Efficientnet_v2_lpytorch: CPU - 16 - ResNet-152pytorch: CPU - 64 - ResNet-152pytorch: CPU - 256 - ResNet-50pytorch: NVIDIA CUDA GPU - 1 - ResNet-50pytorch: NVIDIA CUDA GPU - 256 - ResNet-50pytorch: CPU - 1 - Efficientnet_v2_lpytorch: NVIDIA CUDA GPU - 16 - ResNet-50pyhpc: GPU - Numpy - 65536 - Equation of Statepyhpc: CPU - Numpy - 16384 - Equation of Statescikit-learn: Isolation Forestpyhpc: GPU - Numpy - 16384 - Equation of Statemanticmantic-no-omit-framepointernoble511.848106.31591.499236.86519.57.611470.806158.26248.3380.0090.0091788.259293.59860.814103.497774129613.54738.9518.5790.062109.9840.0150.13153.46439.350.06126272.541147.752150.7322.67028.51160.0320.03357.824376.14571.8119.71.4221.40265.173.91201.4137.2420.61937.36211.28672.319.842.662127.28237.8871.74199.462590.131379.73937.710.63131.0069.77131.27722112.7262.8110.21576.20.26374.15426.2873.01203.1824.2941.5195.635.635.615.6224.240.26367.49.8765.76324.13868.01832.3624.2837.435.619.889.8824.42210.88202.727.31200.300.0150.003289.3710.003509.537107.52792.582236.78620.87.641471.834161.46052.9690.0080.0081828.300295.09663.875105.647790131631.07136.1018.8650.058111.2550.0150.12856.75437.290.05827472.909142.451150.3762.62629.51200.0320.03357.545370.69473.6520.21.4111.40566.672.27205.9537.8890.61836.60208.39173.7510.002.620125.44237.2472.91202.682630.132382.61137.160.62231.0579.91133.09222412.7863.6110.37477.10.26073.36428.6172.24201.1424.3541.7285.645.645.655.6524.400.26266.99.8065.87724.28873.82232.5424.3837.225.649.939.9124.37211.46203.227.32200.170.0150.003336.3720.002345.40078.88068.172285.82322.88.761684.546179.63847.0330.0080.0091963.772269.80666.393112.713839121663.95319.9320.061117.4070.0160.13654.2880.06070.022142.159145.3632.7200.0330.03456.132381.4471.4461.43637.1070.631207.1049.812.668125.0690.133385.3830.63030.6179.86133.14412.89111.5540.262430.8324.1241.9145.595.595.605.6024.190.2619.8765.41624.30869.36932.3424.435.619.889.8724.337.310.0150.003314.0340.003OpenBenchmarking.org

Scikit-Learn

Benchmark: Lasso

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Lassonoblemantic-no-omit-framepointermantic110220330440550SE +/- 1.37, N = 3SE +/- 3.50, N = 3SE +/- 3.22, N = 3345.40509.54511.85-O21. (F9X) gfortran options: -O0

Scikit-Learn

Benchmark: SGD Regression

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SGD Regressionnoblemanticmantic-no-omit-framepointer20406080100SE +/- 0.05, N = 3SE +/- 1.06, N = 6SE +/- 0.49, N = 378.88106.32107.53-O21. (F9X) gfortran options: -O0

Scikit-Learn

Benchmark: Plot OMP vs. LARS

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot OMP vs. LARSnoblemanticmantic-no-omit-framepointer20406080100SE +/- 0.03, N = 3SE +/- 0.08, N = 3SE +/- 0.44, N = 368.1791.5092.58-O21. (F9X) gfortran options: -O0

Scikit-Learn

Benchmark: TSNE MNIST Dataset

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: TSNE MNIST Datasetmantic-no-omit-framepointermanticnoble60120180240300SE +/- 0.54, N = 3SE +/- 0.44, N = 3SE +/- 0.91, N = 3236.79236.87285.82-O21. (F9X) gfortran options: -O0

PyPerformance

Benchmark: json_loads

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: json_loadsmanticmantic-no-omit-framepointernoble510152025SE +/- 0.06, N = 3SE +/- 0.03, N = 3SE +/- 0.03, N = 319.520.822.8

PyPerformance

Benchmark: python_startup

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: python_startupmanticmantic-no-omit-framepointernoble246810SE +/- 0.01, N = 3SE +/- 0.01, N = 3SE +/- 0.01, N = 37.617.648.76

Scikit-Learn

Benchmark: Isotonic / Logistic

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Isotonic / Logisticmanticmantic-no-omit-framepointernoble400800120016002000SE +/- 12.29, N = 3SE +/- 14.46, N = 3SE +/- 9.43, N = 31470.811471.831684.55-O21. (F9X) gfortran options: -O0

Scikit-Learn

Benchmark: Sample Without Replacement

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sample Without Replacementmanticmantic-no-omit-framepointernoble4080120160200SE +/- 0.60, N = 3SE +/- 0.62, N = 3SE +/- 2.21, N = 3158.26161.46179.64-O21. (F9X) gfortran options: -O0

Scikit-Learn

Benchmark: Tree

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Treenoblemanticmantic-no-omit-framepointer1224364860SE +/- 0.52, N = 3SE +/- 0.59, N = 4SE +/- 0.48, N = 1547.0348.3452.97-O21. (F9X) gfortran options: -O0

PyHPC Benchmarks

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

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: GPU - Backend: Numpy - Project Size: 16384 - Benchmark: Isoneutral Mixingmantic-no-omit-framepointernoblemantic0.0020.0040.0060.0080.01SE +/- 0.000, N = 3SE +/- 0.000, N = 3SE +/- 0.000, N = 30.0080.0080.009

PyHPC Benchmarks

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

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 16384 - Benchmark: Isoneutral Mixingmantic-no-omit-framepointermanticnoble0.0020.0040.0060.0080.01SE +/- 0.000, N = 3SE +/- 0.000, N = 3SE +/- 0.000, N = 30.0080.0090.009

Scikit-Learn

Benchmark: Isotonic / Perturbed Logarithm

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Isotonic / Perturbed Logarithmmanticmantic-no-omit-framepointernoble400800120016002000SE +/- 24.41, N = 3SE +/- 16.46, N = 3SE +/- 1.48, N = 31788.261828.301963.77-O21. (F9X) gfortran options: -O0

Scikit-Learn

Benchmark: GLM

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: GLMnoblemanticmantic-no-omit-framepointer60120180240300SE +/- 0.93, N = 3SE +/- 1.06, N = 3SE +/- 1.07, N = 3269.81293.60295.10-O21. (F9X) gfortran options: -O0

Scikit-Learn

Benchmark: Text Vectorizers

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Text Vectorizersmanticmantic-no-omit-framepointernoble1530456075SE +/- 0.19, N = 3SE +/- 0.08, N = 3SE +/- 0.32, N = 360.8163.8866.39-O21. (F9X) gfortran options: -O0

Scikit-Learn

Benchmark: Hist Gradient Boosting Adult

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Adultmanticmantic-no-omit-framepointernoble306090120150SE +/- 0.70, N = 3SE +/- 0.59, N = 3SE +/- 0.52, N = 3103.50105.65112.71-O21. (F9X) gfortran options: -O0

PyBench

Total For Average Test Times

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyBench 2018-02-16Total For Average Test Timesmanticmantic-no-omit-framepointernoble2004006008001000SE +/- 1.00, N = 3SE +/- 1.20, N = 3SE +/- 8.70, N = 4774790839

PyPerformance

Benchmark: go

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: gonoblemanticmantic-no-omit-framepointer306090120150SE +/- 0.00, N = 3SE +/- 0.00, N = 3SE +/- 0.33, N = 3121129131

Scikit-Learn

Benchmark: Sparse Random Projections / 100 Iterations

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sparse Random Projections / 100 Iterationsmanticmantic-no-omit-framepointernoble140280420560700SE +/- 3.80, N = 3SE +/- 7.06, N = 4SE +/- 4.34, N = 3613.55631.07663.95-O21. (F9X) gfortran options: -O0

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: Efficientnet_v2_lmanticmantic-no-omit-framepointer918273645SE +/- 0.08, N = 3SE +/- 0.02, N = 338.9536.10MIN: 37.12 / MAX: 39.27MIN: 34.25 / MAX: 38.01

Scikit-Learn

Benchmark: Hist Gradient Boosting Categorical Only

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Categorical Onlymanticmantic-no-omit-framepointernoble510152025SE +/- 0.06, N = 3SE +/- 0.12, N = 3SE +/- 0.10, N = 318.5818.8719.93-O21. (F9X) gfortran options: -O0

PyHPC Benchmarks

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

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: GPU - Backend: Numpy - Project Size: 262144 - Benchmark: Equation of Statemantic-no-omit-framepointernoblemantic0.0140.0280.0420.0560.07SE +/- 0.000, N = 3SE +/- 0.000, N = 3SE +/- 0.001, N = 30.0580.0610.062

Scikit-Learn

Benchmark: Hist Gradient Boosting

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boostingmanticmantic-no-omit-framepointernoble306090120150SE +/- 0.22, N = 3SE +/- 0.25, N = 3SE +/- 0.17, N = 3109.98111.26117.41-O21. (F9X) gfortran options: -O0

PyHPC Benchmarks

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

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 65536 - Benchmark: Equation of Statemanticmantic-no-omit-framepointernoble0.00360.00720.01080.01440.018SE +/- 0.000, N = 3SE +/- 0.000, N = 3SE +/- 0.000, N = 150.0150.0150.016

PyHPC Benchmarks

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

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: GPU - Backend: Numpy - Project Size: 262144 - Benchmark: Isoneutral Mixingmantic-no-omit-framepointermanticnoble0.03060.06120.09180.12240.153SE +/- 0.001, N = 3SE +/- 0.000, N = 3SE +/- 0.001, N = 30.1280.1310.136

Scikit-Learn

Benchmark: LocalOutlierFactor

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: LocalOutlierFactormanticnoblemantic-no-omit-framepointer1326395265SE +/- 0.18, N = 3SE +/- 0.02, N = 3SE +/- 0.74, N = 1553.4654.2956.75-O21. (F9X) gfortran options: -O0

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: Efficientnet_v2_lmanticmantic-no-omit-framepointer918273645SE +/- 0.47, N = 3SE +/- 0.26, N = 339.3537.29MIN: 36.65 / MAX: 40.42MIN: 35.83 / MAX: 39.17

PyHPC Benchmarks

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

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 262144 - Benchmark: Equation of Statemantic-no-omit-framepointernoblemantic0.01370.02740.04110.05480.0685SE +/- 0.000, N = 3SE +/- 0.000, N = 3SE +/- 0.001, N = 30.0580.0600.061

PyPerformance

Benchmark: raytrace

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: raytracemanticmantic-no-omit-framepointer60120180240300SE +/- 0.33, N = 3SE +/- 0.33, N = 3262274

Scikit-Learn

Benchmark: Kernel PCA Solvers / Time vs. N Samples

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Kernel PCA Solvers / Time vs. N Samplesnoblemanticmantic-no-omit-framepointer1632486480SE +/- 0.44, N = 3SE +/- 0.05, N = 3SE +/- 0.16, N = 370.0272.5472.91-O21. (F9X) gfortran options: -O0

Scikit-Learn

Benchmark: Plot Neighbors

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Neighborsnoblemantic-no-omit-framepointermantic306090120150SE +/- 1.09, N = 3SE +/- 0.59, N = 3SE +/- 1.34, N = 7142.16142.45147.75-O21. (F9X) gfortran options: -O0

Scikit-Learn

Benchmark: Plot Polynomial Kernel Approximation

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Polynomial Kernel Approximationnoblemantic-no-omit-framepointermantic306090120150SE +/- 1.46, N = 3SE +/- 1.20, N = 3SE +/- 1.22, N = 3145.36150.38150.73-O21. (F9X) gfortran options: -O0

PyHPC Benchmarks

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

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Isoneutral Mixingmantic-no-omit-framepointermanticnoble0.6121.2241.8362.4483.06SE +/- 0.002, N = 3SE +/- 0.010, N = 3SE +/- 0.010, N = 32.6262.6702.720

PyPerformance

Benchmark: django_template

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: django_templatemanticmantic-no-omit-framepointer714212835SE +/- 0.03, N = 3SE +/- 0.06, N = 328.529.5

PyPerformance

Benchmark: regex_compile

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: regex_compilemanticmantic-no-omit-framepointer306090120150SE +/- 0.00, N = 3SE +/- 0.33, N = 3116120

PyHPC Benchmarks

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

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 65536 - Benchmark: Isoneutral Mixingmanticmantic-no-omit-framepointernoble0.00740.01480.02220.02960.037SE +/- 0.000, N = 3SE +/- 0.000, N = 3SE +/- 0.000, N = 30.0320.0320.033

PyHPC Benchmarks

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

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: GPU - Backend: Numpy - Project Size: 65536 - Benchmark: Isoneutral Mixingmanticmantic-no-omit-framepointernoble0.00770.01540.02310.03080.0385SE +/- 0.000, N = 3SE +/- 0.000, N = 3SE +/- 0.000, N = 30.0330.0330.034

Scikit-Learn

Benchmark: Plot Ward

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Wardnoblemantic-no-omit-framepointermantic1326395265SE +/- 0.20, N = 3SE +/- 0.22, N = 3SE +/- 0.21, N = 356.1357.5557.82-O21. (F9X) gfortran options: -O0

Scikit-Learn

Benchmark: Covertype Dataset Benchmark

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Covertype Dataset Benchmarkmantic-no-omit-framepointermanticnoble80160240320400SE +/- 3.40, N = 3SE +/- 4.88, N = 3SE +/- 2.58, N = 3370.69376.15381.45-O21. (F9X) gfortran options: -O0

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-152

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

Benchmark: pathlib

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: pathlibmanticmantic-no-omit-framepointer510152025SE +/- 0.00, N = 3SE +/- 0.00, N = 319.720.2

PyHPC Benchmarks

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

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: GPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Equation of Statemantic-no-omit-framepointermanticnoble0.32540.65080.97621.30161.627SE +/- 0.001, N = 3SE +/- 0.004, N = 3SE +/- 0.006, N = 31.4111.4221.446

PyHPC Benchmarks

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

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Equation of Statemanticmantic-no-omit-framepointernoble0.32310.64620.96931.29241.6155SE +/- 0.003, N = 3SE +/- 0.004, N = 3SE +/- 0.003, N = 31.4021.4051.436

PyPerformance

Benchmark: crypto_pyaes

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: crypto_pyaesmanticmantic-no-omit-framepointer1530456075SE +/- 0.06, N = 3SE +/- 0.00, N = 365.166.6

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-152manticmantic-no-omit-framepointer1632486480SE +/- 0.56, N = 3SE +/- 0.96, N = 373.9172.27MIN: 68.9 / MAX: 75.9MIN: 68.86 / MAX: 76.62

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50

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

Benchmark: Kernel PCA Solvers / Time vs. N Components

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Kernel PCA Solvers / Time vs. N Componentsnoblemanticmantic-no-omit-framepointer918273645SE +/- 0.43, N = 3SE +/- 0.21, N = 3SE +/- 0.36, N = 337.1137.2437.89-O21. (F9X) gfortran options: -O0

PyHPC Benchmarks

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

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Isoneutral Mixingmantic-no-omit-framepointermanticnoble0.1420.2840.4260.5680.71SE +/- 0.000, N = 3SE +/- 0.001, N = 3SE +/- 0.006, N = 30.6180.6190.631

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: Efficientnet_v2_lmanticmantic-no-omit-framepointer918273645SE +/- 0.15, N = 3SE +/- 0.30, N = 1537.3636.60MIN: 35.47 / MAX: 37.85MIN: 33.07 / MAX: 39.53

Scikit-Learn

Benchmark: Plot Hierarchical

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Hierarchicalnoblemantic-no-omit-framepointermantic50100150200250SE +/- 2.35, N = 3SE +/- 0.42, N = 3SE +/- 0.75, N = 3207.10208.39211.29-O21. (F9X) gfortran options: -O0

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-152

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

PyTorch

Device: CPU - Batch Size: 32 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-152mantic-no-omit-framepointermanticnoble3691215SE +/- 0.09, N = 3SE +/- 0.05, N = 3SE +/- 0.04, N = 310.009.849.81MIN: 8.09 / MAX: 10.27MIN: 9.6 / MAX: 9.98MIN: 9.42 / MAX: 9.93

PyHPC Benchmarks

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

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: GPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Isoneutral Mixingmantic-no-omit-framepointermanticnoble0.60031.20061.80092.40123.0015SE +/- 0.006, N = 3SE +/- 0.006, N = 3SE +/- 0.006, N = 32.6202.6622.668

Scikit-Learn

Benchmark: Sparsify

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sparsifynoblemantic-no-omit-framepointermantic306090120150SE +/- 0.65, N = 3SE +/- 1.28, N = 5SE +/- 1.36, N = 5125.07125.44127.28-O21. (F9X) gfortran options: -O0

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: Efficientnet_v2_lmanticmantic-no-omit-framepointer918273645SE +/- 0.30, N = 9SE +/- 0.31, N = 1537.8837.24MIN: 35.67 / MAX: 39.63MIN: 33.97 / MAX: 39.43

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-152

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

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50

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

PyPerformance

Benchmark: pickle_pure_python

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: pickle_pure_pythonmanticmantic-no-omit-framepointer60120180240300SE +/- 0.33, N = 3SE +/- 0.58, N = 3259263

PyHPC Benchmarks

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

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 262144 - Benchmark: Isoneutral Mixingmanticmantic-no-omit-framepointernoble0.02990.05980.08970.11960.1495SE +/- 0.001, N = 3SE +/- 0.000, N = 3SE +/- 0.002, N = 30.1310.1320.133

Scikit-Learn

Benchmark: SGDOneClassSVM

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SGDOneClassSVMmanticmantic-no-omit-framepointernoble80160240320400SE +/- 4.18, N = 3SE +/- 3.48, N = 7SE +/- 3.55, N = 3379.74382.61385.38-O21. (F9X) gfortran options: -O0

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: Efficientnet_v2_lmanticmantic-no-omit-framepointer918273645SE +/- 0.24, N = 3SE +/- 0.30, N = 1537.7137.16MIN: 35.52 / MAX: 38.25MIN: 34.12 / MAX: 39.48

PyHPC Benchmarks

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

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: GPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Isoneutral Mixingmantic-no-omit-framepointernoblemantic0.1420.2840.4260.5680.71SE +/- 0.007, N = 3SE +/- 0.004, N = 3SE +/- 0.002, N = 30.6220.6300.631

Scikit-Learn

Benchmark: Plot Incremental PCA

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Incremental PCAnoblemanticmantic-no-omit-framepointer714212835SE +/- 0.06, N = 3SE +/- 0.03, N = 3SE +/- 0.07, N = 330.6231.0131.06-O21. (F9X) gfortran options: -O0

PyTorch

Device: CPU - Batch Size: 256 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: ResNet-152mantic-no-omit-framepointernoblemantic3691215SE +/- 0.04, N = 3SE +/- 0.03, N = 3SE +/- 0.07, N = 39.919.869.77MIN: 9.19 / MAX: 10.05MIN: 8.69 / MAX: 9.99MIN: 9.17 / MAX: 10

Scikit-Learn

Benchmark: Feature Expansions

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Feature Expansionsmanticmantic-no-omit-framepointernoble306090120150SE +/- 0.86, N = 3SE +/- 1.22, N = 3SE +/- 1.21, N = 3131.28133.09133.14-O21. (F9X) gfortran options: -O0

PyPerformance

Benchmark: 2to3

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: 2to3manticmantic-no-omit-framepointer50100150200250SE +/- 0.00, N = 3SE +/- 0.33, N = 3221224

PyTorch

Device: CPU - Batch Size: 1 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-152noblemantic-no-omit-framepointermantic3691215SE +/- 0.05, N = 3SE +/- 0.04, N = 3SE +/- 0.03, N = 312.8912.7812.72MIN: 12.36 / MAX: 13.05MIN: 11.9 / MAX: 12.9MIN: 11.99 / MAX: 12.8

PyPerformance

Benchmark: chaos

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: chaosmanticmantic-no-omit-framepointer1428425670SE +/- 0.03, N = 3SE +/- 0.20, N = 362.863.6

Scikit-Learn

Benchmark: Hist Gradient Boosting Threading

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Threadingmanticmantic-no-omit-framepointernoble20406080100SE +/- 0.13, N = 3SE +/- 0.15, N = 3SE +/- 0.13, N = 3110.22110.37111.55-O21. (F9X) gfortran options: -O0

PyPerformance

Benchmark: nbody

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: nbodymanticmantic-no-omit-framepointer20406080100SE +/- 0.06, N = 3SE +/- 0.07, N = 376.277.1

PyHPC Benchmarks

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

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: GPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Equation of Statemantic-no-omit-framepointernoblemantic0.05920.11840.17760.23680.296SE +/- 0.001, N = 3SE +/- 0.002, N = 3SE +/- 0.002, N = 30.2600.2620.263

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-152manticmantic-no-omit-framepointer1632486480SE +/- 0.96, N = 3SE +/- 0.74, N = 374.1573.36MIN: 68.27 / MAX: 75.61MIN: 68.19 / MAX: 74.63

Numpy Benchmark

OpenBenchmarking.orgScore, More Is BetterNumpy Benchmarknoblemantic-no-omit-framepointermantic90180270360450SE +/- 1.01, N = 3SE +/- 0.90, N = 3SE +/- 1.20, N = 3430.83428.61426.28

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-152manticmantic-no-omit-framepointer1632486480SE +/- 0.96, N = 3SE +/- 0.20, N = 373.0172.24MIN: 68.06 / MAX: 75.3MIN: 68.36 / MAX: 73.14

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-50manticmantic-no-omit-framepointer4080120160200SE +/- 1.69, N = 3SE +/- 0.33, N = 3203.18201.14MIN: 183.76 / MAX: 207.98MIN: 183.61 / MAX: 202.73

PyTorch

Device: CPU - Batch Size: 32 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-50mantic-no-omit-framepointermanticnoble612182430SE +/- 0.16, N = 3SE +/- 0.10, N = 3SE +/- 0.06, N = 324.3524.2924.12MIN: 23.67 / MAX: 24.87MIN: 22.24 / MAX: 24.66MIN: 22.33 / MAX: 24.46

Scikit-Learn

Benchmark: 20 Newsgroups / Logistic Regression

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: 20 Newsgroups / Logistic Regressionmanticmantic-no-omit-framepointernoble1020304050SE +/- 0.19, N = 3SE +/- 0.24, N = 3SE +/- 0.12, N = 341.5241.7341.91-O21. (F9X) gfortran options: -O0

PyTorch

Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_lmantic-no-omit-framepointermanticnoble1.2692.5383.8075.0766.345SE +/- 0.01, N = 3SE +/- 0.01, N = 3SE +/- 0.00, N = 35.645.635.59MIN: 5.52 / MAX: 5.69MIN: 5.31 / MAX: 5.68MIN: 5.46 / MAX: 5.64

PyTorch

Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_lmantic-no-omit-framepointermanticnoble1.2692.5383.8075.0766.345SE +/- 0.01, N = 3SE +/- 0.02, N = 3SE +/- 0.02, N = 35.645.635.59MIN: 5.45 / MAX: 5.68MIN: 5.39 / MAX: 5.71MIN: 5.31 / MAX: 5.65

PyTorch

Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_lmantic-no-omit-framepointermanticnoble1.27132.54263.81395.08526.3565SE +/- 0.02, N = 3SE +/- 0.01, N = 3SE +/- 0.01, N = 35.655.615.60MIN: 5.36 / MAX: 5.93MIN: 5.45 / MAX: 5.66MIN: 5.37 / MAX: 5.66

PyTorch

Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_lmantic-no-omit-framepointermanticnoble1.27132.54263.81395.08526.3565SE +/- 0.01, N = 3SE +/- 0.01, N = 3SE +/- 0.01, N = 35.655.625.60MIN: 5.45 / MAX: 5.7MIN: 5.35 / MAX: 5.66MIN: 5.32 / MAX: 5.64

PyTorch

Device: CPU - Batch Size: 64 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: ResNet-50mantic-no-omit-framepointermanticnoble612182430SE +/- 0.15, N = 3SE +/- 0.04, N = 3SE +/- 0.11, N = 324.4024.2424.19MIN: 21.6 / MAX: 24.8MIN: 23.59 / MAX: 24.49MIN: 22.75 / MAX: 24.73

PyHPC Benchmarks

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

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Equation of Statenoblemantic-no-omit-framepointermantic0.05920.11840.17760.23680.296SE +/- 0.002, N = 3SE +/- 0.000, N = 3SE +/- 0.002, N = 30.2610.2620.263

PyPerformance

Benchmark: float

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: floatmantic-no-omit-framepointermantic1530456075SE +/- 0.10, N = 3SE +/- 0.03, N = 366.967.4

PyTorch

Device: CPU - Batch Size: 512 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: ResNet-152manticnoblemantic-no-omit-framepointer3691215SE +/- 0.02, N = 3SE +/- 0.03, N = 3SE +/- 0.07, N = 39.879.879.80MIN: 9.09 / MAX: 9.96MIN: 9.21 / MAX: 10MIN: 9.12 / MAX: 9.98

Scikit-Learn

Benchmark: MNIST Dataset

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: MNIST Datasetnoblemanticmantic-no-omit-framepointer1530456075SE +/- 0.67, N = 3SE +/- 0.82, N = 4SE +/- 0.47, N = 365.4265.7665.88-O21. (F9X) gfortran options: -O0

PyTorch

Device: CPU - Batch Size: 512 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: ResNet-50noblemantic-no-omit-framepointermantic612182430SE +/- 0.14, N = 3SE +/- 0.08, N = 3SE +/- 0.02, N = 324.3024.2824.13MIN: 22.45 / MAX: 24.75MIN: 22.31 / MAX: 24.53MIN: 23.58 / MAX: 24.41

Scikit-Learn

Benchmark: SAGA

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SAGAmanticnoblemantic-no-omit-framepointer2004006008001000SE +/- 8.69, N = 6SE +/- 10.35, N = 3SE +/- 5.60, N = 3868.02869.37873.82-O21. (F9X) gfortran options: -O0

PyTorch

Device: CPU - Batch Size: 1 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-50mantic-no-omit-framepointermanticnoble816243240SE +/- 0.16, N = 3SE +/- 0.11, N = 3SE +/- 0.17, N = 332.5432.3632.34MIN: 31.64 / MAX: 32.94MIN: 31.89 / MAX: 32.7MIN: 28.9 / MAX: 32.83

PyTorch

Device: CPU - Batch Size: 16 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-50noblemantic-no-omit-framepointermantic612182430SE +/- 0.01, N = 3SE +/- 0.16, N = 3SE +/- 0.05, N = 324.4324.3824.28MIN: 22.57 / MAX: 24.72MIN: 22.2 / MAX: 24.87MIN: 20.22 / MAX: 24.56

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: Efficientnet_v2_lmanticmantic-no-omit-framepointer918273645SE +/- 0.03, N = 3SE +/- 0.33, N = 837.4337.22MIN: 35.81 / MAX: 38.02MIN: 34.99 / MAX: 39.08

PyTorch

Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_lmantic-no-omit-framepointermanticnoble1.2692.5383.8075.0766.345SE +/- 0.01, N = 3SE +/- 0.02, N = 3SE +/- 0.02, N = 35.645.615.61MIN: 5.29 / MAX: 5.68MIN: 5.44 / MAX: 5.65MIN: 5.46 / MAX: 5.67

PyTorch

Device: CPU - Batch Size: 16 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-152mantic-no-omit-framepointermanticnoble3691215SE +/- 0.01, N = 3SE +/- 0.04, N = 3SE +/- 0.02, N = 39.939.889.88MIN: 9.39 / MAX: 10.01MIN: 9.31 / MAX: 10.01MIN: 9.15 / MAX: 9.98

PyTorch

Device: CPU - Batch Size: 64 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: ResNet-152mantic-no-omit-framepointermanticnoble3691215SE +/- 0.02, N = 3SE +/- 0.03, N = 3SE +/- 0.01, N = 39.919.889.87MIN: 8.69 / MAX: 10.08MIN: 8.8 / MAX: 9.98MIN: 8.61 / MAX: 9.96

PyTorch

Device: CPU - Batch Size: 256 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: ResNet-50manticmantic-no-omit-framepointernoble612182430SE +/- 0.03, N = 3SE +/- 0.11, N = 3SE +/- 0.06, N = 324.4224.3724.33MIN: 20.15 / MAX: 24.74MIN: 23.76 / MAX: 24.81MIN: 22.79 / MAX: 24.66

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-50

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

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50

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

PyTorch

Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_lmantic-no-omit-framepointermanticnoble246810SE +/- 0.02, N = 3SE +/- 0.00, N = 3SE +/- 0.00, N = 37.327.317.31MIN: 7.23 / MAX: 7.38MIN: 7.16 / MAX: 7.34MIN: 7.07 / MAX: 7.36

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50manticmantic-no-omit-framepointer4080120160200SE +/- 0.25, N = 3SE +/- 0.96, N = 3200.30200.17MIN: 182.88 / MAX: 202.36MIN: 183.43 / MAX: 203.55

PyHPC Benchmarks

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

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: GPU - Backend: Numpy - Project Size: 65536 - Benchmark: Equation of Statemanticmantic-no-omit-framepointernoble0.00340.00680.01020.01360.017SE +/- 0.000, N = 3SE +/- 0.000, N = 3SE +/- 0.000, N = 70.0150.0150.015

PyHPC Benchmarks

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

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 16384 - Benchmark: Equation of Statemanticmantic-no-omit-framepointernoble0.00070.00140.00210.00280.0035SE +/- 0.000, N = 3SE +/- 0.000, N = 3SE +/- 0.000, N = 30.0030.0030.003

Scikit-Learn

Benchmark: Isolation Forest

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Isolation Forestmanticnoblemantic-no-omit-framepointer70140210280350SE +/- 1.30, N = 3SE +/- 2.83, N = 3SE +/- 51.04, N = 9289.37314.03336.37-O21. (F9X) gfortran options: -O0

PyHPC Benchmarks

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

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: GPU - Backend: Numpy - Project Size: 16384 - Benchmark: Equation of Statemantic-no-omit-framepointermanticnoble0.00070.00140.00210.00280.0035SE +/- 0.000, N = 15SE +/- 0.000, N = 3SE +/- 0.000, N = 120.0020.0030.003


Phoronix Test Suite v10.8.5