machine learning

AMD Ryzen 9 3900X 12-Core testing with a MSI X570-A PRO (MS-7C37) v3.0 (H.70 BIOS) and NVIDIA GeForce RTX 3060 12GB on Ubuntu 23.10 via the Phoronix Test Suite.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2404285-VPA1-DESKTOP14
Jump To Table - Results

View

Do Not Show Noisy Results
Do Not Show Results With Incomplete Data
Do Not Show Results With Little Change/Spread
List Notable Results
Show Result Confidence Charts

Limit displaying results to tests within:

CPU Massive 2 Tests
HPC - High Performance Computing 4 Tests
Machine Learning 3 Tests
Programmer / Developer System Benchmarks 2 Tests
Python 5 Tests
Server CPU Tests 3 Tests
Single-Threaded 2 Tests

Statistics

Show Overall Harmonic Mean(s)
Show Overall Geometric Mean
Show Geometric Means Per-Suite/Category
Show Wins / Losses Counts (Pie Chart)
Normalize Results
Remove Outliers Before Calculating Averages

Graph Settings

Force Line Graphs Where Applicable
Convert To Scalar Where Applicable
Prefer Vertical Bar Graphs

Multi-Way Comparison

Condense Multi-Option Tests Into Single Result Graphs

Table

Show Detailed System Result Table

Run Management

Highlight
Result
Hide
Result
Result
Identifier
Performance Per
Dollar
Date
Run
  Test
  Duration
mantic
February 23
  15 Hours, 54 Minutes
mantic-no-omit-framepointer
February 24
  19 Hours, 11 Minutes
Invert Hiding All Results Option
  17 Hours, 32 Minutes
Only show results matching title/arguments (delimit multiple options with a comma):
Do not show results matching title/arguments (delimit multiple options with a comma):

mantic

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

OS: Ubuntu 23.10, Kernel: 6.5.0-9-generic (x86_64), Display Server: X Server 1.21.1.7, Display Driver: NVIDIA, OpenCL: OpenCL 3.0 CUDA 12.2.146, Compiler: GCC 13.2.0 + CUDA 12.2, File-System: ext4, Screen Resolution: 1920x1080

Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-XYspKM/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-XYspKM/gcc-13-13.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-build-config=bootstrap-lto-lean --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v
Processor Notes: Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0x8701013
Python Notes: Python 3.11.6
Security Notes: gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Mitigation of untrained return thunk; SMT enabled with STIBP protection + spec_rstack_overflow: Mitigation of safe RET + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

mantic-no-omit-framepointer

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

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

Kernel Notes: Transparent Huge Pages: madvise
Environment Notes: CXXFLAGS=-fno-omit-frame-pointer QMAKE_CFLAGS=-fno-omit-frame-pointer CFLAGS=-fno-omit-frame-pointer CFLAGS_OVERRIDE=-fno-omit-frame-pointer QMAKE_CXXFLAGS=-fno-omit-frame-pointer FFLAGS=-fno-omit-frame-pointer
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-b9QCDx/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-b9QCDx/gcc-13-13.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-build-config=bootstrap-lto-lean --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v
Processor Notes: Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0x8701013
Python Notes: Python 3.11.6
Security Notes: gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Mitigation of untrained return thunk; SMT enabled with STIBP protection + spec_rstack_overflow: Mitigation of safe RET + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

machine learningOpenBenchmarking.orgPhoronix Test SuiteAMD Ryzen 9 3900X 12-Core @ 3.80GHz (12 Cores / 24 Threads)MSI X570-A PRO (MS-7C37) v3.0 (H.70 BIOS)AMD Starship/Matisse2 x 16GB DDR4-3200MT/s F4-3200C16-16GVK2000GB Seagate ST2000DM006-2DM1 + 2000GB Western Digital WD20EZAZ-00G + 500GB Samsung SSD 860 + 8002GB Seagate ST8000DM004-2CX1 + 1000GB CT1000BX500SSD1 + 512GB TS512GESD310CNVIDIA GeForce RTX 3060 12GBNVIDIA GeForce RTX 3060NVIDIA GA104 HD AudioDELL P2314HRealtek RTL8111/8168/8411Ubuntu 23.106.5.0-9-generic (x86_64)X Server 1.21.1.7NVIDIAOpenCL 3.0 CUDA 12.2.146GCC 13.2.0 + CUDA 12.2ext41920x1080ProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDisplay ServerDisplay DriverOpenCLCompilerFile-SystemScreen ResolutionMachine Learning BenchmarksSystem Logs- Transparent Huge Pages: madvise- mantic: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-XYspKM/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-XYspKM/gcc-13-13.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-build-config=bootstrap-lto-lean --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v - mantic-no-omit-framepointer: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-b9QCDx/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-b9QCDx/gcc-13-13.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-build-config=bootstrap-lto-lean --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v - Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0x8701013- Python 3.11.6- gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Mitigation of untrained return thunk; SMT enabled with STIBP protection + spec_rstack_overflow: Mitigation of safe RET + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected - mantic-no-omit-framepointer: CXXFLAGS=-fno-omit-frame-pointer QMAKE_CFLAGS=-fno-omit-frame-pointer CFLAGS=-fno-omit-frame-pointer CFLAGS_OVERRIDE=-fno-omit-frame-pointer QMAKE_CXXFLAGS=-fno-omit-frame-pointer FFLAGS=-fno-omit-frame-pointer

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

machine learningscikit-learn: SAGAscikit-learn: Isotonic / Perturbed Logarithmscikit-learn: Isotonic / Logisticscikit-learn: Isolation Forestscikit-learn: Sparse Rand Projections / 100 Iterationsscikit-learn: SGDOneClassSVMscikit-learn: Lassoscikit-learn: Covertype Dataset Benchmarkscikit-learn: GLMpytorch: CPU - 256 - Efficientnet_v2_lpytorch: CPU - 16 - Efficientnet_v2_lpytorch: CPU - 512 - Efficientnet_v2_lpytorch: CPU - 32 - Efficientnet_v2_lpytorch: CPU - 64 - Efficientnet_v2_lscikit-learn: TSNE MNIST Datasetscikit-learn: Plot Neighborsscikit-learn: Plot Hierarchicalscikit-learn: Sparsifypytorch: NVIDIA CUDA GPU - 64 - Efficientnet_v2_lscikit-learn: Plot Incremental PCAscikit-learn: Sample Without Replacementpytorch: CPU - 512 - ResNet-152pytorch: CPU - 256 - ResNet-152pytorch: CPU - 16 - ResNet-152pytorch: CPU - 32 - ResNet-152pytorch: CPU - 64 - ResNet-152scikit-learn: Plot Polynomial Kernel Approximationscikit-learn: SGD Regressionscikit-learn: LocalOutlierFactorpytorch: NVIDIA CUDA GPU - 256 - Efficientnet_v2_lpytorch: NVIDIA CUDA GPU - 32 - Efficientnet_v2_lscikit-learn: Treenumpy: scikit-learn: Feature Expansionspytorch: CPU - 1 - Efficientnet_v2_lscikit-learn: Hist Gradient Boostingscikit-learn: Hist Gradient Boosting Threadingscikit-learn: Hist Gradient Boosting Adultscikit-learn: Plot OMP vs. LARSpyhpc: CPU - Numpy - 4194304 - Isoneutral Mixingpyhpc: GPU - Numpy - 4194304 - Isoneutral Mixingpytorch: NVIDIA CUDA GPU - 512 - Efficientnet_v2_lscikit-learn: MNIST Datasetscikit-learn: Kernel PCA Solvers / Time vs. N Samplespytorch: CPU - 1 - ResNet-152scikit-learn: Text Vectorizerspytorch: CPU - 512 - ResNet-50pytorch: CPU - 64 - ResNet-50pytorch: CPU - 16 - ResNet-50pytorch: CPU - 32 - ResNet-50pytorch: CPU - 256 - ResNet-50scikit-learn: Plot Wardpyperformance: python_startuppytorch: NVIDIA CUDA GPU - 16 - Efficientnet_v2_lscikit-learn: 20 Newsgroups / Logistic Regressionscikit-learn: Kernel PCA Solvers / Time vs. N Componentspyhpc: GPU - Numpy - 4194304 - Equation of Statepyhpc: CPU - Numpy - 4194304 - Equation of Statepyperformance: raytracepyperformance: 2to3pytorch: CPU - 1 - ResNet-50pyperformance: pathlibpytorch: NVIDIA CUDA GPU - 256 - ResNet-152pytorch: NVIDIA CUDA GPU - 16 - ResNet-152pytorch: NVIDIA CUDA GPU - 64 - ResNet-152pytorch: NVIDIA CUDA GPU - 512 - ResNet-152pytorch: NVIDIA CUDA GPU - 32 - ResNet-152pytorch: NVIDIA CUDA GPU - 1 - Efficientnet_v2_lpyperformance: pickle_pure_pythonpyhpc: GPU - Numpy - 1048576 - Isoneutral Mixingpyperformance: gopyhpc: CPU - Numpy - 1048576 - Isoneutral Mixingpyperformance: nbodypyperformance: django_templatepytorch: NVIDIA CUDA GPU - 1 - ResNet-50pyperformance: json_loadsscikit-learn: Hist Gradient Boosting Categorical Onlypyperformance: floatpyperformance: regex_compilepyperformance: crypto_pyaespyperformance: chaospyhpc: CPU - Numpy - 262144 - Isoneutral Mixingpyhpc: GPU - Numpy - 262144 - Isoneutral Mixingpytorch: NVIDIA CUDA GPU - 1 - ResNet-152pybench: Total For Average Test Timespytorch: NVIDIA CUDA GPU - 32 - ResNet-50pytorch: NVIDIA CUDA GPU - 16 - ResNet-50pytorch: NVIDIA CUDA GPU - 512 - ResNet-50pytorch: NVIDIA CUDA GPU - 256 - ResNet-50pytorch: NVIDIA CUDA GPU - 64 - ResNet-50pyhpc: CPU - Numpy - 16384 - Isoneutral Mixingpyhpc: GPU - Numpy - 16384 - Isoneutral Mixingpyhpc: CPU - Numpy - 1048576 - Equation of Statepyhpc: GPU - Numpy - 1048576 - Equation of Statepyhpc: GPU - Numpy - 16384 - Equation of Statepyhpc: GPU - Numpy - 262144 - Equation of Statepyhpc: CPU - Numpy - 262144 - Equation of Statepyhpc: GPU - Numpy - 65536 - Isoneutral Mixingpyhpc: CPU - Numpy - 65536 - Isoneutral Mixingpyhpc: CPU - Numpy - 16384 - Equation of Statepyhpc: GPU - Numpy - 65536 - Equation of Statepyhpc: CPU - Numpy - 65536 - Equation of Statepyhpc: CPU - JAX - 16384 - Isoneutral Mixingmanticmantic-no-omit-framepointer868.0181788.2591470.806289.371613.547379.739511.848376.145293.5985.615.635.615.635.62236.865147.752211.286127.28237.8831.006158.2629.879.779.889.849.88150.732106.31553.46437.3637.7148.338426.28131.2777.31109.984110.215103.49791.4992.6702.66237.4365.76372.54112.7260.81424.1324.2424.2824.2924.4257.8247.6138.9541.51937.2421.4221.40226222132.3619.771.7473.0171.8172.3174.1539.352590.6311290.61976.228.5210.8819.518.57967.411665.162.80.1310.13173.91774199.46200.30203.18202.72201.410.0090.0090.2630.2630.0030.0620.0610.0330.0320.0030.0150.015873.8221828.3001471.834336.372631.071382.611509.537370.694295.0965.645.645.655.645.65236.786142.451208.391125.44237.2431.057161.4609.809.919.9310.009.91150.376107.52756.75436.6037.1652.969428.61133.0927.32111.255110.374105.64792.5822.6262.62037.2265.87772.90912.7863.87524.2824.4024.3824.3524.3757.5457.6436.1041.72837.8891.4111.40527422432.5420.272.9172.2473.6573.7573.3637.292630.6221310.61877.129.5211.4620.818.86566.912066.663.60.1320.12872.27790202.68200.17201.14203.22205.950.0080.0080.2620.2600.0020.0580.0580.0330.0320.0030.0150.015OpenBenchmarking.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: SAGAmanticmantic-no-omit-framepointer2004006008001000SE +/- 8.69, N = 6SE +/- 5.60, N = 3868.02873.821. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Isotonic / Perturbed Logarithmmanticmantic-no-omit-framepointer400800120016002000SE +/- 24.41, N = 3SE +/- 16.46, N = 31788.261828.301. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Isotonic / Logisticmanticmantic-no-omit-framepointer30060090012001500SE +/- 12.29, N = 3SE +/- 14.46, N = 31470.811471.831. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Isolation Forestmanticmantic-no-omit-framepointer70140210280350SE +/- 1.30, N = 3SE +/- 51.04, N = 9289.37336.371. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sparse Random Projections / 100 Iterationsmanticmantic-no-omit-framepointer140280420560700SE +/- 3.80, N = 3SE +/- 7.06, N = 4613.55631.071. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SGDOneClassSVMmanticmantic-no-omit-framepointer80160240320400SE +/- 4.18, N = 3SE +/- 3.48, N = 7379.74382.611. (F9X) gfortran options: -O0

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

Benchmark: Isotonic / Pathological

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

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

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: GLMmanticmantic-no-omit-framepointer60120180240300SE +/- 1.06, N = 3SE +/- 1.07, N = 3293.60295.101. (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: Efficientnet_v2_lmantic-no-omit-framepointermantic1.2692.5383.8075.0766.345SE +/- 0.01, N = 3SE +/- 0.02, N = 35.645.61MIN: 5.29 / MAX: 5.68MIN: 5.44 / MAX: 5.65

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

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

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

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

Scikit-Learn

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

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

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

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

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

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-framepointer714212835SE +/- 0.03, N = 3SE +/- 0.07, N = 331.0131.061. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sample Without Replacementmanticmantic-no-omit-framepointer4080120160200SE +/- 0.60, N = 3SE +/- 0.62, N = 3158.26161.461. (F9X) gfortran options: -O0

PyTorch

This is a benchmark of PyTorch making use of pytorch-benchmark [https://github.com/LukasHedegaard/pytorch-benchmark]. Currently this test profile is catered to CPU-based testing. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: ResNet-152manticmantic-no-omit-framepointer3691215SE +/- 0.02, N = 3SE +/- 0.07, N = 39.879.80MIN: 9.09 / MAX: 9.96MIN: 9.12 / MAX: 9.98

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: ResNet-152mantic-no-omit-framepointermantic3691215SE +/- 0.04, N = 3SE +/- 0.07, N = 39.919.77MIN: 9.19 / MAX: 10.05MIN: 9.17 / MAX: 10

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-152mantic-no-omit-framepointermantic3691215SE +/- 0.01, N = 3SE +/- 0.04, N = 39.939.88MIN: 9.39 / MAX: 10.01MIN: 9.31 / MAX: 10.01

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: ResNet-152mantic-no-omit-framepointermantic3691215SE +/- 0.02, N = 3SE +/- 0.03, N = 39.919.88MIN: 8.69 / MAX: 10.08MIN: 8.8 / MAX: 9.98

Scikit-Learn

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

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

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SGD Regressionmanticmantic-no-omit-framepointer20406080100SE +/- 1.06, N = 6SE +/- 0.49, N = 3106.32107.531. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: LocalOutlierFactormanticmantic-no-omit-framepointer1326395265SE +/- 0.18, N = 3SE +/- 0.74, N = 1553.4656.751. (F9X) gfortran options: -O0

PyTorch

This is a benchmark of PyTorch making use of pytorch-benchmark [https://github.com/LukasHedegaard/pytorch-benchmark]. Currently this test profile is catered to CPU-based testing. Learn more via the OpenBenchmarking.org test page.

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

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

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: Treemanticmantic-no-omit-framepointer1224364860SE +/- 0.59, N = 4SE +/- 0.48, N = 1548.3452.971. (F9X) gfortran options: -O0

Numpy Benchmark

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

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

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-framepointer306090120150SE +/- 0.86, N = 3SE +/- 1.22, N = 3131.28133.091. (F9X) gfortran options: -O0

PyTorch

This is a benchmark of PyTorch making use of pytorch-benchmark [https://github.com/LukasHedegaard/pytorch-benchmark]. Currently this test profile is catered to CPU-based testing. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_lmantic-no-omit-framepointermantic246810SE +/- 0.02, N = 3SE +/- 0.00, N = 37.327.31MIN: 7.23 / MAX: 7.38MIN: 7.16 / MAX: 7.34

Scikit-Learn

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

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boostingmanticmantic-no-omit-framepointer20406080100SE +/- 0.22, N = 3SE +/- 0.25, N = 3109.98111.261. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Threadingmanticmantic-no-omit-framepointer20406080100SE +/- 0.13, N = 3SE +/- 0.15, N = 3110.22110.371. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Adultmanticmantic-no-omit-framepointer20406080100SE +/- 0.70, N = 3SE +/- 0.59, N = 3103.50105.651. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot OMP vs. LARSmanticmantic-no-omit-framepointer20406080100SE +/- 0.08, N = 3SE +/- 0.44, N = 391.5092.581. (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 Mixingmantic-no-omit-framepointermantic0.60081.20161.80242.40323.004SE +/- 0.002, N = 3SE +/- 0.010, N = 32.6262.670

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

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: 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

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-framepointer1530456075SE +/- 0.82, N = 4SE +/- 0.47, N = 365.7665.881. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Kernel PCA Solvers / Time vs. N Samplesmanticmantic-no-omit-framepointer1632486480SE +/- 0.05, N = 3SE +/- 0.16, N = 372.5472.911. (F9X) gfortran options: -O0

PyTorch

This is a benchmark of PyTorch making use of pytorch-benchmark [https://github.com/LukasHedegaard/pytorch-benchmark]. Currently this test profile is catered to CPU-based testing. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-152mantic-no-omit-framepointermantic3691215SE +/- 0.04, N = 3SE +/- 0.03, N = 312.7812.72MIN: 11.9 / MAX: 12.9MIN: 11.99 / MAX: 12.8

Scikit-Learn

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

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Text Vectorizersmanticmantic-no-omit-framepointer1428425670SE +/- 0.19, N = 3SE +/- 0.08, N = 360.8163.881. (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-50mantic-no-omit-framepointermantic612182430SE +/- 0.08, N = 3SE +/- 0.02, N = 324.2824.13MIN: 22.31 / MAX: 24.53MIN: 23.58 / MAX: 24.41

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: ResNet-50mantic-no-omit-framepointermantic612182430SE +/- 0.15, N = 3SE +/- 0.04, N = 324.4024.24MIN: 21.6 / MAX: 24.8MIN: 23.59 / MAX: 24.49

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-50mantic-no-omit-framepointermantic612182430SE +/- 0.16, N = 3SE +/- 0.05, N = 324.3824.28MIN: 22.2 / MAX: 24.87MIN: 20.22 / MAX: 24.56

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: ResNet-50manticmantic-no-omit-framepointer612182430SE +/- 0.03, N = 3SE +/- 0.11, N = 324.4224.37MIN: 20.15 / MAX: 24.74MIN: 23.76 / MAX: 24.81

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 Wardmantic-no-omit-framepointermantic1326395265SE +/- 0.22, N = 3SE +/- 0.21, N = 357.5557.821. (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: python_startupmanticmantic-no-omit-framepointer246810SE +/- 0.01, N = 3SE +/- 0.01, N = 37.617.64

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: 20 Newsgroups / Logistic Regressionmanticmantic-no-omit-framepointer1020304050SE +/- 0.19, N = 3SE +/- 0.24, N = 341.5241.731. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Kernel PCA Solvers / Time vs. N Componentsmanticmantic-no-omit-framepointer918273645SE +/- 0.21, N = 3SE +/- 0.36, N = 337.2437.891. (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: 4194304 - Benchmark: Equation of Statemantic-no-omit-framepointermantic0.320.640.961.281.6SE +/- 0.001, N = 3SE +/- 0.004, N = 31.4111.422

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Equation of Statemanticmantic-no-omit-framepointer0.31610.63220.94831.26441.5805SE +/- 0.003, N = 3SE +/- 0.004, N = 31.4021.405

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

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

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-50mantic-no-omit-framepointermantic816243240SE +/- 0.16, N = 3SE +/- 0.11, N = 332.5432.36MIN: 31.64 / MAX: 32.94MIN: 31.89 / MAX: 32.7

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

PyTorch

This is a benchmark of PyTorch making use of pytorch-benchmark [https://github.com/LukasHedegaard/pytorch-benchmark]. Currently this test profile is catered to CPU-based testing. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-152mantic-no-omit-framepointermantic1632486480SE +/- 0.83, N = 3SE +/- 0.24, N = 372.9171.74MIN: 68 / MAX: 75.45MIN: 67.87 / MAX: 72.6

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: 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: 64 - Model: ResNet-152mantic-no-omit-framepointermantic1632486480SE +/- 0.66, N = 3SE +/- 0.44, N = 373.6571.81MIN: 68.88 / MAX: 75.03MIN: 67.31 / MAX: 72.89

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-152mantic-no-omit-framepointermantic1632486480SE +/- 0.50, N = 3SE +/- 0.94, N = 373.7572.31MIN: 68.91 / MAX: 75.15MIN: 67.38 / MAX: 74.62

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

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

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

PyHPC Benchmarks

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

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

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-framepointer306090120150SE +/- 0.00, N = 3SE +/- 0.33, N = 3129131

PyHPC Benchmarks

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

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

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

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

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-50mantic-no-omit-framepointermantic50100150200250SE +/- 1.46, N = 15SE +/- 2.67, N = 3211.46210.88MIN: 192.13 / MAX: 223.01MIN: 195.21 / MAX: 218.16

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-framepointer510152025SE +/- 0.06, N = 3SE +/- 0.03, N = 319.520.8

Scikit-Learn

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

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Categorical Onlymanticmantic-no-omit-framepointer510152025SE +/- 0.06, N = 3SE +/- 0.12, N = 318.5818.871. (F9X) gfortran options: -O0

PyPerformance

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

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

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

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

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

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-framepointer0.02970.05940.08910.11880.1485SE +/- 0.001, N = 3SE +/- 0.000, N = 30.1310.132

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

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

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-framepointer2004006008001000SE +/- 1.00, N = 3SE +/- 1.20, N = 3774790

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-50mantic-no-omit-framepointermantic4080120160200SE +/- 2.52, N = 4SE +/- 1.06, N = 3202.68199.46MIN: 182.69 / MAX: 211.53MIN: 182.77 / MAX: 206.03

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

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: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50mantic-no-omit-framepointermantic4080120160200SE +/- 1.21, N = 3SE +/- 1.76, N = 3203.22202.72MIN: 185.88 / MAX: 206.71MIN: 183.1 / MAX: 207.93

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

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: 16384 - Benchmark: Isoneutral Mixingmantic-no-omit-framepointermantic0.0020.0040.0060.0080.01SE +/- 0.000, N = 3SE +/- 0.000, N = 30.0080.009

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

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: RCV1 Logreg Convergencet

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

PyHPC Benchmarks

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

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

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

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

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

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

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: GPU - Backend: Numpy - Project Size: 65536 - Benchmark: Isoneutral Mixingmanticmantic-no-omit-framepointer0.00740.01480.02220.02960.037SE +/- 0.000, N = 3SE +/- 0.000, N = 30.0330.033

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 65536 - Benchmark: Isoneutral Mixingmanticmantic-no-omit-framepointer0.00720.01440.02160.02880.036SE +/- 0.000, N = 3SE +/- 0.000, N = 30.0320.032

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 16384 - Benchmark: Equation of Statemanticmantic-no-omit-framepointer0.00070.00140.00210.00280.0035SE +/- 0.000, N = 3SE +/- 0.000, N = 30.0030.003

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: GPU - Backend: Numpy - Project Size: 65536 - Benchmark: Equation of Statemanticmantic-no-omit-framepointer0.00340.00680.01020.01360.017SE +/- 0.000, N = 3SE +/- 0.000, N = 30.0150.015

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 65536 - Benchmark: Equation of Statemanticmantic-no-omit-framepointer0.00340.00680.01020.01360.017SE +/- 0.000, N = 3SE +/- 0.000, N = 30.0150.015

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 Parallel Pairwise

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

Benchmark: Plot Fast KMeans

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

Benchmark: Plot Lasso Path

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

Benchmark: Plot Singular Value Decomposition

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

Benchmark: Glmnet

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

PyHPC Benchmarks

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

102 Results Shown

Scikit-Learn:
  SAGA
  Isotonic / Perturbed Logarithm
  Isotonic / Logistic
  Isolation Forest
  Sparse Rand Projections / 100 Iterations
  SGDOneClassSVM
  Lasso
  Covertype Dataset Benchmark
  GLM
PyTorch:
  CPU - 256 - Efficientnet_v2_l
  CPU - 16 - Efficientnet_v2_l
  CPU - 512 - Efficientnet_v2_l
  CPU - 32 - Efficientnet_v2_l
  CPU - 64 - Efficientnet_v2_l
Scikit-Learn:
  TSNE MNIST Dataset
  Plot Neighbors
  Plot Hierarchical
  Sparsify
PyTorch
Scikit-Learn:
  Plot Incremental PCA
  Sample Without Replacement
PyTorch:
  CPU - 512 - ResNet-152
  CPU - 256 - ResNet-152
  CPU - 16 - ResNet-152
  CPU - 32 - ResNet-152
  CPU - 64 - ResNet-152
Scikit-Learn:
  Plot Polynomial Kernel Approximation
  SGD Regression
  LocalOutlierFactor
PyTorch:
  NVIDIA CUDA GPU - 256 - Efficientnet_v2_l
  NVIDIA CUDA GPU - 32 - Efficientnet_v2_l
Scikit-Learn
Numpy Benchmark
Scikit-Learn
PyTorch
Scikit-Learn:
  Hist Gradient Boosting
  Hist Gradient Boosting Threading
  Hist Gradient Boosting Adult
  Plot OMP vs. LARS
PyHPC Benchmarks:
  CPU - Numpy - 4194304 - Isoneutral Mixing
  GPU - Numpy - 4194304 - Isoneutral Mixing
PyTorch
Scikit-Learn:
  MNIST Dataset
  Kernel PCA Solvers / Time vs. N Samples
PyTorch
Scikit-Learn
PyTorch:
  CPU - 512 - ResNet-50
  CPU - 64 - ResNet-50
  CPU - 16 - ResNet-50
  CPU - 32 - ResNet-50
  CPU - 256 - ResNet-50
Scikit-Learn
PyPerformance
PyTorch
Scikit-Learn:
  20 Newsgroups / Logistic Regression
  Kernel PCA Solvers / Time vs. N Components
PyHPC Benchmarks:
  GPU - Numpy - 4194304 - Equation of State
  CPU - Numpy - 4194304 - Equation of State
PyPerformance:
  raytrace
  2to3
PyTorch
PyPerformance
PyTorch:
  NVIDIA CUDA GPU - 256 - ResNet-152
  NVIDIA CUDA GPU - 16 - ResNet-152
  NVIDIA CUDA GPU - 64 - ResNet-152
  NVIDIA CUDA GPU - 512 - ResNet-152
  NVIDIA CUDA GPU - 32 - ResNet-152
  NVIDIA CUDA GPU - 1 - Efficientnet_v2_l
PyPerformance
PyHPC Benchmarks
PyPerformance
PyHPC Benchmarks
PyPerformance:
  nbody
  django_template
PyTorch
PyPerformance
Scikit-Learn
PyPerformance:
  float
  regex_compile
  crypto_pyaes
  chaos
PyHPC Benchmarks:
  CPU - Numpy - 262144 - Isoneutral Mixing
  GPU - Numpy - 262144 - Isoneutral Mixing
PyTorch
PyBench
PyTorch:
  NVIDIA CUDA GPU - 32 - ResNet-50
  NVIDIA CUDA GPU - 16 - ResNet-50
  NVIDIA CUDA GPU - 512 - ResNet-50
  NVIDIA CUDA GPU - 256 - ResNet-50
  NVIDIA CUDA GPU - 64 - ResNet-50
PyHPC Benchmarks:
  CPU - Numpy - 16384 - Isoneutral Mixing
  GPU - Numpy - 16384 - Isoneutral Mixing
  CPU - Numpy - 1048576 - Equation of State
  GPU - Numpy - 1048576 - Equation of State
  GPU - Numpy - 16384 - Equation of State
  GPU - Numpy - 262144 - Equation of State
  CPU - Numpy - 262144 - Equation of State
  GPU - Numpy - 65536 - Isoneutral Mixing
  CPU - Numpy - 65536 - Isoneutral Mixing
  CPU - Numpy - 16384 - Equation of State
  GPU - Numpy - 65536 - Equation of State
  CPU - Numpy - 65536 - Equation of State

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.