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.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2405015-VPA1-DESKTOP46
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mantic
February 23
  15 Hours, 54 Minutes
mantic-no-omit-framepointer
February 24
  19 Hours, 11 Minutes
noble
April 30
  14 Hours, 21 Minutes
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  16 Hours, 28 Minutes

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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

noble

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 + U32J59x, Network: Realtek RTL8111/8168/8211/8411

OS: Ubuntu 24.04, Kernel: 6.8.0-31-generic (x86_64), Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1920x1080

Kernel Notes: Transparent Huge Pages: madvise
Environment Notes: 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"
Compiler Notes: --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 Notes: Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0x8701013
Python Notes: Python 3.12.3
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 + 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

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"

manticmantic-no-omit-framepointernobleResult OverviewPhoronix Test Suite100%133%165%198%231%PyPerformancePyBenchPyHPC BenchmarksScikit-LearnNumpy BenchmarkPyTorch

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

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: Lassomanticmantic-no-omit-framepointernoble110220330440550SE +/- 3.22, N = 3SE +/- 3.50, N = 3SE +/- 1.37, N = 3511.85509.54345.40-O21. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SGD Regressionmanticmantic-no-omit-framepointernoble20406080100SE +/- 1.06, N = 6SE +/- 0.49, N = 3SE +/- 0.05, N = 3106.32107.5378.88-O21. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot OMP vs. LARSmanticmantic-no-omit-framepointernoble20406080100SE +/- 0.08, N = 3SE +/- 0.44, N = 3SE +/- 0.03, N = 391.5092.5868.17-O21. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: TSNE MNIST Datasetmanticmantic-no-omit-framepointernoble60120180240300SE +/- 0.44, N = 3SE +/- 0.54, N = 3SE +/- 0.91, N = 3236.87236.79285.82-O21. (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: json_loadsmanticmantic-no-omit-framepointernoble510152025SE +/- 0.06, N = 3SE +/- 0.03, N = 3SE +/- 0.03, N = 319.520.822.8

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

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 / Logisticmanticmantic-no-omit-framepointernoble400800120016002000SE +/- 12.29, N = 3SE +/- 14.46, N = 3SE +/- 9.43, N = 31470.811471.831684.55-O21. (F9X) gfortran options: -O0

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

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Treemanticmantic-no-omit-framepointernoble1224364860SE +/- 0.59, N = 4SE +/- 0.48, N = 15SE +/- 0.52, N = 348.3452.9747.03-O21. (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: Isoneutral Mixingmanticmantic-no-omit-framepointernoble0.0020.0040.0060.0080.01SE +/- 0.000, N = 3SE +/- 0.000, N = 3SE +/- 0.000, N = 30.0090.0080.008

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 16384 - Benchmark: Isoneutral Mixingmanticmantic-no-omit-framepointernoble0.0020.0040.0060.0080.01SE +/- 0.000, N = 3SE +/- 0.000, N = 3SE +/- 0.000, N = 30.0090.0080.009

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: 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

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: GLMmanticmantic-no-omit-framepointernoble60120180240300SE +/- 1.06, N = 3SE +/- 1.07, N = 3SE +/- 0.93, N = 3293.60295.10269.81-O21. (F9X) gfortran options: -O0

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

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

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 Timesmanticmantic-no-omit-framepointernoble2004006008001000SE +/- 1.00, N = 3SE +/- 1.20, N = 3SE +/- 8.70, N = 4774790839

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: gomanticmantic-no-omit-framepointernoble306090120150SE +/- 0.00, N = 3SE +/- 0.33, N = 3SE +/- 0.00, N = 3129131121

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 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

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_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

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 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

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 Statemanticmantic-no-omit-framepointernoble0.0140.0280.0420.0560.07SE +/- 0.001, N = 3SE +/- 0.000, N = 3SE +/- 0.000, N = 30.0620.0580.061

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: 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

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: 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

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: GPU - Backend: Numpy - Project Size: 262144 - Benchmark: Isoneutral Mixingmanticmantic-no-omit-framepointernoble0.03060.06120.09180.12240.153SE +/- 0.000, N = 3SE +/- 0.001, N = 3SE +/- 0.001, N = 30.1310.1280.136

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: LocalOutlierFactormanticmantic-no-omit-framepointernoble1326395265SE +/- 0.18, N = 3SE +/- 0.74, N = 15SE +/- 0.02, N = 353.4656.7554.29-O21. (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_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

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 Statemanticmantic-no-omit-framepointernoble0.01370.02740.04110.05480.0685SE +/- 0.001, N = 3SE +/- 0.000, N = 3SE +/- 0.000, N = 30.0610.0580.060

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: raytracemanticmantic-no-omit-framepointer60120180240300SE +/- 0.33, N = 3SE +/- 0.33, N = 3262274

Benchmark: raytrace

noble: The test quit with a non-zero exit status. E: ERROR: No benchmark was run

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 Samplesmanticmantic-no-omit-framepointernoble1632486480SE +/- 0.05, N = 3SE +/- 0.16, N = 3SE +/- 0.44, N = 372.5472.9170.02-O21. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Neighborsmanticmantic-no-omit-framepointernoble306090120150SE +/- 1.34, N = 7SE +/- 0.59, N = 3SE +/- 1.09, N = 3147.75142.45142.16-O21. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Polynomial Kernel Approximationmanticmantic-no-omit-framepointernoble306090120150SE +/- 1.22, N = 3SE +/- 1.20, N = 3SE +/- 1.46, N = 3150.73150.38145.36-O21. (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: Isoneutral Mixingmanticmantic-no-omit-framepointernoble0.6121.2241.8362.4483.06SE +/- 0.010, N = 3SE +/- 0.002, N = 3SE +/- 0.010, N = 32.6702.6262.720

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_templatemanticmantic-no-omit-framepointer714212835SE +/- 0.03, N = 3SE +/- 0.06, N = 328.529.5

Benchmark: django_template

noble: The test quit with a non-zero exit status. E: ERROR: No benchmark was run

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

Benchmark: regex_compile

noble: The test quit with a non-zero exit status. E: ERROR: No benchmark was run

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: 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

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

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 Wardmanticmantic-no-omit-framepointernoble1326395265SE +/- 0.21, N = 3SE +/- 0.22, N = 3SE +/- 0.20, N = 357.8257.5556.13-O21. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Covertype Dataset Benchmarkmanticmantic-no-omit-framepointernoble80160240320400SE +/- 4.88, N = 3SE +/- 3.40, N = 3SE +/- 2.58, N = 3376.15370.69381.45-O21. (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-152manticmantic-no-omit-framepointer1632486480SE +/- 0.44, N = 3SE +/- 0.66, N = 371.8173.65MIN: 67.31 / MAX: 72.89MIN: 68.88 / MAX: 75.03

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: pathlibmanticmantic-no-omit-framepointer510152025SE +/- 0.00, N = 3SE +/- 0.00, N = 319.720.2

Benchmark: pathlib

noble: The test quit with a non-zero exit status. E: ERROR: No benchmark was run

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 Statemanticmantic-no-omit-framepointernoble0.32540.65080.97621.30161.627SE +/- 0.004, N = 3SE +/- 0.001, N = 3SE +/- 0.006, N = 31.4221.4111.446

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

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_pyaesmanticmantic-no-omit-framepointer1530456075SE +/- 0.06, N = 3SE +/- 0.00, N = 365.166.6

Benchmark: crypto_pyaes

noble: The test quit with a non-zero exit status. E: ERROR: No benchmark was run

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-152manticmantic-no-omit-framepointer1632486480SE +/- 0.56, N = 3SE +/- 0.96, N = 373.9172.27MIN: 68.9 / MAX: 75.9MIN: 68.86 / MAX: 76.62

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50manticmantic-no-omit-framepointer50100150200250SE +/- 0.58, N = 3SE +/- 1.98, N = 3201.41205.95MIN: 184.02 / MAX: 203.68MIN: 186.96 / MAX: 210.21

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 Componentsmanticmantic-no-omit-framepointernoble918273645SE +/- 0.21, N = 3SE +/- 0.36, N = 3SE +/- 0.43, N = 337.2437.8937.11-O21. (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 Mixingmanticmantic-no-omit-framepointernoble0.1420.2840.4260.5680.71SE +/- 0.001, N = 3SE +/- 0.000, N = 3SE +/- 0.006, N = 30.6190.6180.631

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: 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

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 Hierarchicalmanticmantic-no-omit-framepointernoble50100150200250SE +/- 0.75, N = 3SE +/- 0.42, N = 3SE +/- 2.35, N = 3211.29208.39207.10-O21. (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-152manticmantic-no-omit-framepointer1632486480SE +/- 0.94, N = 3SE +/- 0.50, N = 372.3173.75MIN: 67.38 / MAX: 74.62MIN: 68.91 / MAX: 75.15

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-152manticmantic-no-omit-framepointernoble3691215SE +/- 0.05, N = 3SE +/- 0.09, N = 3SE +/- 0.04, N = 39.8410.009.81MIN: 9.6 / MAX: 9.98MIN: 8.09 / MAX: 10.27MIN: 9.42 / MAX: 9.93

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 Mixingmanticmantic-no-omit-framepointernoble0.60031.20061.80092.40123.0015SE +/- 0.006, N = 3SE +/- 0.006, N = 3SE +/- 0.006, N = 32.6622.6202.668

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: Sparsifymanticmantic-no-omit-framepointernoble306090120150SE +/- 1.36, N = 5SE +/- 1.28, N = 5SE +/- 0.65, N = 3127.28125.44125.07-O21. (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_lmanticmantic-no-omit-framepointer918273645SE +/- 0.30, N = 9SE +/- 0.31, N = 1537.8837.24MIN: 35.67 / MAX: 39.63MIN: 33.97 / MAX: 39.43

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-152manticmantic-no-omit-framepointer1632486480SE +/- 0.24, N = 3SE +/- 0.83, N = 371.7472.91MIN: 67.87 / MAX: 72.6MIN: 68 / MAX: 75.45

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50manticmantic-no-omit-framepointer4080120160200SE +/- 1.06, N = 3SE +/- 2.52, N = 4199.46202.68MIN: 182.77 / MAX: 206.03MIN: 182.69 / MAX: 211.53

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: pickle_pure_pythonmanticmantic-no-omit-framepointer60120180240300SE +/- 0.33, N = 3SE +/- 0.58, N = 3259263

Benchmark: pickle_pure_python

noble: The test quit with a non-zero exit status. E: ERROR: No benchmark was run

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: 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

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: 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

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_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

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 Mixingmanticmantic-no-omit-framepointernoble0.1420.2840.4260.5680.71SE +/- 0.002, N = 3SE +/- 0.007, N = 3SE +/- 0.004, N = 30.6310.6220.630

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 Incremental PCAmanticmantic-no-omit-framepointernoble714212835SE +/- 0.03, N = 3SE +/- 0.07, N = 3SE +/- 0.06, N = 331.0131.0630.62-O21. (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: 256 - Model: ResNet-152manticmantic-no-omit-framepointernoble3691215SE +/- 0.07, N = 3SE +/- 0.04, N = 3SE +/- 0.03, N = 39.779.919.86MIN: 9.17 / MAX: 10MIN: 9.19 / MAX: 10.05MIN: 8.69 / MAX: 9.99

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: 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

PyPerformance is the reference Python performance benchmark suite. Learn more via the OpenBenchmarking.org test page.

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

Benchmark: 2to3

noble: The test quit with a non-zero exit status. E: ERROR: No benchmark was run

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-152manticmantic-no-omit-framepointernoble3691215SE +/- 0.03, N = 3SE +/- 0.04, N = 3SE +/- 0.05, N = 312.7212.7812.89MIN: 11.99 / MAX: 12.8MIN: 11.9 / MAX: 12.9MIN: 12.36 / MAX: 13.05

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: chaosmanticmantic-no-omit-framepointer1428425670SE +/- 0.03, N = 3SE +/- 0.20, N = 362.863.6

Benchmark: chaos

noble: The test quit with a non-zero exit status. E: ERROR: No benchmark was run

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 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

PyPerformance is the reference Python performance benchmark suite. Learn more via the OpenBenchmarking.org test page.

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

Benchmark: nbody

noble: The test quit with a non-zero exit status. E: ERROR: No benchmark was run

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 Statemanticmantic-no-omit-framepointernoble0.05920.11840.17760.23680.296SE +/- 0.002, N = 3SE +/- 0.001, N = 3SE +/- 0.002, N = 30.2630.2600.262

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-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

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

OpenBenchmarking.orgScore, More Is BetterNumpy Benchmarkmanticmantic-no-omit-framepointernoble90180270360450SE +/- 1.20, N = 3SE +/- 0.90, N = 3SE +/- 1.01, N = 3426.28428.61430.83

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-152manticmantic-no-omit-framepointer1632486480SE +/- 0.96, N = 3SE +/- 0.20, N = 373.0172.24MIN: 68.06 / MAX: 75.3MIN: 68.36 / MAX: 73.14

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-50manticmantic-no-omit-framepointernoble612182430SE +/- 0.10, N = 3SE +/- 0.16, N = 3SE +/- 0.06, N = 324.2924.3524.12MIN: 22.24 / MAX: 24.66MIN: 23.67 / MAX: 24.87MIN: 22.33 / MAX: 24.46

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 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

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: 32 - Model: Efficientnet_v2_lmanticmantic-no-omit-framepointernoble1.2692.5383.8075.0766.345SE +/- 0.01, N = 3SE +/- 0.01, N = 3SE +/- 0.00, N = 35.635.645.59MIN: 5.31 / MAX: 5.68MIN: 5.52 / MAX: 5.69MIN: 5.46 / MAX: 5.64

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_lmanticmantic-no-omit-framepointernoble1.2692.5383.8075.0766.345SE +/- 0.02, N = 3SE +/- 0.01, N = 3SE +/- 0.02, N = 35.635.645.59MIN: 5.39 / MAX: 5.71MIN: 5.45 / MAX: 5.68MIN: 5.31 / MAX: 5.65

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_lmanticmantic-no-omit-framepointernoble1.27132.54263.81395.08526.3565SE +/- 0.01, N = 3SE +/- 0.02, N = 3SE +/- 0.01, N = 35.615.655.60MIN: 5.45 / MAX: 5.66MIN: 5.36 / MAX: 5.93MIN: 5.37 / MAX: 5.66

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_lmanticmantic-no-omit-framepointernoble1.27132.54263.81395.08526.3565SE +/- 0.01, N = 3SE +/- 0.01, N = 3SE +/- 0.01, N = 35.625.655.60MIN: 5.35 / MAX: 5.66MIN: 5.45 / MAX: 5.7MIN: 5.32 / MAX: 5.64

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: ResNet-50manticmantic-no-omit-framepointernoble612182430SE +/- 0.04, N = 3SE +/- 0.15, N = 3SE +/- 0.11, N = 324.2424.4024.19MIN: 23.59 / MAX: 24.49MIN: 21.6 / MAX: 24.8MIN: 22.75 / MAX: 24.73

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 Statemanticmantic-no-omit-framepointernoble0.05920.11840.17760.23680.296SE +/- 0.002, N = 3SE +/- 0.000, N = 3SE +/- 0.002, N = 30.2630.2620.261

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: floatmanticmantic-no-omit-framepointer1530456075SE +/- 0.03, N = 3SE +/- 0.10, N = 367.466.9

Benchmark: float

noble: The test quit with a non-zero exit status. E: ERROR: No benchmark was run

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-152manticmantic-no-omit-framepointernoble3691215SE +/- 0.02, N = 3SE +/- 0.07, N = 3SE +/- 0.03, N = 39.879.809.87MIN: 9.09 / MAX: 9.96MIN: 9.12 / MAX: 9.98MIN: 9.21 / 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: MNIST Datasetmanticmantic-no-omit-framepointernoble1530456075SE +/- 0.82, N = 4SE +/- 0.47, N = 3SE +/- 0.67, N = 365.7665.8865.42-O21. (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: 512 - Model: ResNet-50manticmantic-no-omit-framepointernoble612182430SE +/- 0.02, N = 3SE +/- 0.08, N = 3SE +/- 0.14, N = 324.1324.2824.30MIN: 23.58 / MAX: 24.41MIN: 22.31 / MAX: 24.53MIN: 22.45 / MAX: 24.75

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: SAGAmanticmantic-no-omit-framepointernoble2004006008001000SE +/- 8.69, N = 6SE +/- 5.60, N = 3SE +/- 10.35, N = 3868.02873.82869.37-O21. (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-50manticmantic-no-omit-framepointernoble816243240SE +/- 0.11, N = 3SE +/- 0.16, N = 3SE +/- 0.17, N = 332.3632.5432.34MIN: 31.89 / MAX: 32.7MIN: 31.64 / MAX: 32.94MIN: 28.9 / MAX: 32.83

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-50manticmantic-no-omit-framepointernoble612182430SE +/- 0.05, N = 3SE +/- 0.16, N = 3SE +/- 0.01, N = 324.2824.3824.43MIN: 20.22 / MAX: 24.56MIN: 22.2 / MAX: 24.87MIN: 22.57 / MAX: 24.72

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_lmanticmantic-no-omit-framepointernoble1.2692.5383.8075.0766.345SE +/- 0.02, N = 3SE +/- 0.01, N = 3SE +/- 0.02, N = 35.615.645.61MIN: 5.44 / MAX: 5.65MIN: 5.29 / MAX: 5.68MIN: 5.46 / MAX: 5.67

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-152manticmantic-no-omit-framepointernoble3691215SE +/- 0.04, N = 3SE +/- 0.01, N = 3SE +/- 0.02, N = 39.889.939.88MIN: 9.31 / MAX: 10.01MIN: 9.39 / MAX: 10.01MIN: 9.15 / MAX: 9.98

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: ResNet-152manticmantic-no-omit-framepointernoble3691215SE +/- 0.03, N = 3SE +/- 0.02, N = 3SE +/- 0.01, N = 39.889.919.87MIN: 8.8 / MAX: 9.98MIN: 8.69 / MAX: 10.08MIN: 8.61 / MAX: 9.96

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-50manticmantic-no-omit-framepointer50100150200250SE +/- 2.67, N = 3SE +/- 1.46, N = 15210.88211.46MIN: 195.21 / MAX: 218.16MIN: 192.13 / MAX: 223.01

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50manticmantic-no-omit-framepointer4080120160200SE +/- 1.76, N = 3SE +/- 1.21, N = 3202.72203.22MIN: 183.1 / MAX: 207.93MIN: 185.88 / MAX: 206.71

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_lmanticmantic-no-omit-framepointernoble246810SE +/- 0.00, N = 3SE +/- 0.02, N = 3SE +/- 0.00, N = 37.317.327.31MIN: 7.16 / MAX: 7.34MIN: 7.23 / MAX: 7.38MIN: 7.07 / MAX: 7.36

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

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: 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

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

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'?

noble: 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

noble: 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.

noble: 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

noble: 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'?

noble: 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'?

noble: 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'

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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.

noble: 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 Forestmanticmantic-no-omit-framepointernoble70140210280350SE +/- 1.30, N = 3SE +/- 51.04, N = 9SE +/- 2.83, N = 3289.37336.37314.03-O21. (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 Statemanticmantic-no-omit-framepointernoble0.00070.00140.00210.00280.0035SE +/- 0.000, N = 3SE +/- 0.000, N = 15SE +/- 0.000, N = 120.0030.0020.003

102 Results Shown

Scikit-Learn:
  Lasso
  SGD Regression
  Plot OMP vs. LARS
  TSNE MNIST Dataset
PyPerformance:
  json_loads
  python_startup
Scikit-Learn:
  Isotonic / Logistic
  Sample Without Replacement
  Tree
PyHPC Benchmarks:
  GPU - Numpy - 16384 - Isoneutral Mixing
  CPU - Numpy - 16384 - Isoneutral Mixing
Scikit-Learn:
  Isotonic / Perturbed Logarithm
  GLM
  Text Vectorizers
  Hist Gradient Boosting Adult
PyBench
PyPerformance
Scikit-Learn
PyTorch
Scikit-Learn
PyHPC Benchmarks
Scikit-Learn
PyHPC Benchmarks:
  CPU - Numpy - 65536 - Equation of State
  GPU - Numpy - 262144 - Isoneutral Mixing
Scikit-Learn
PyTorch
PyHPC Benchmarks
PyPerformance
Scikit-Learn:
  Kernel PCA Solvers / Time vs. N Samples
  Plot Neighbors
  Plot Polynomial Kernel Approximation
PyHPC Benchmarks
PyPerformance:
  django_template
  regex_compile
PyHPC Benchmarks:
  CPU - Numpy - 65536 - Isoneutral Mixing
  GPU - Numpy - 65536 - Isoneutral Mixing
Scikit-Learn:
  Plot Ward
  Covertype Dataset Benchmark
PyTorch
PyPerformance
PyHPC Benchmarks:
  GPU - Numpy - 4194304 - Equation of State
  CPU - Numpy - 4194304 - Equation of State
PyPerformance
PyTorch:
  NVIDIA CUDA GPU - 1 - ResNet-152
  NVIDIA CUDA GPU - 64 - ResNet-50
Scikit-Learn
PyHPC Benchmarks
PyTorch
Scikit-Learn
PyTorch:
  NVIDIA CUDA GPU - 512 - ResNet-152
  CPU - 32 - ResNet-152
PyHPC Benchmarks
Scikit-Learn
PyTorch:
  NVIDIA CUDA GPU - 64 - Efficientnet_v2_l
  NVIDIA CUDA GPU - 256 - ResNet-152
  NVIDIA CUDA GPU - 32 - ResNet-50
PyPerformance
PyHPC Benchmarks
Scikit-Learn
PyTorch
PyHPC Benchmarks
Scikit-Learn
PyTorch
Scikit-Learn
PyPerformance
PyTorch
PyPerformance
Scikit-Learn
PyPerformance
PyHPC Benchmarks
PyTorch
Numpy Benchmark
PyTorch:
  NVIDIA CUDA GPU - 16 - ResNet-152
  NVIDIA CUDA GPU - 512 - ResNet-50
  CPU - 32 - ResNet-50
Scikit-Learn
PyTorch:
  CPU - 32 - Efficientnet_v2_l
  CPU - 16 - Efficientnet_v2_l
  CPU - 512 - Efficientnet_v2_l
  CPU - 64 - Efficientnet_v2_l
  CPU - 64 - ResNet-50
PyHPC Benchmarks
PyPerformance
PyTorch
Scikit-Learn
PyTorch
Scikit-Learn
PyTorch:
  CPU - 1 - ResNet-50
  CPU - 16 - ResNet-50
  NVIDIA CUDA GPU - 512 - Efficientnet_v2_l
  CPU - 256 - Efficientnet_v2_l
  CPU - 16 - ResNet-152
  CPU - 64 - ResNet-152
  CPU - 256 - ResNet-50
  NVIDIA CUDA GPU - 1 - ResNet-50
  NVIDIA CUDA GPU - 256 - ResNet-50
  CPU - 1 - Efficientnet_v2_l
  NVIDIA CUDA GPU - 16 - ResNet-50
PyHPC Benchmarks:
  GPU - 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.

noble

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 + U32J59x, Network: Realtek RTL8111/8168/8211/8411

OS: Ubuntu 24.04, Kernel: 6.8.0-31-generic (x86_64), Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1920x1080

Kernel Notes: Transparent Huge Pages: madvise
Environment Notes: 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"
Compiler Notes: --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 Notes: Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0x8701013
Python Notes: Python 3.12.3
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 + 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

Testing initiated at 30 April 2024 00:19 by user ubuntu.