machine learning AMD Ryzen 9 3900X 12-Core testing with a MSI X570-A PRO (MS-7C37) v3.0 (H.70 BIOS) and NVIDIA GeForce RTX 3060 12GB on Ubuntu 23.10 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2404285-VPA1-DESKTOP14&rdt&grs .
machine learning Processor Motherboard Chipset Memory Disk Graphics Audio Monitor Network OS Kernel Display Server Display Driver OpenCL Compiler File-System Screen Resolution mantic mantic-no-omit-framepointer AMD Ryzen 9 3900X 12-Core @ 3.80GHz (12 Cores / 24 Threads) MSI X570-A PRO (MS-7C37) v3.0 (H.70 BIOS) AMD Starship/Matisse 2 x 16GB DDR4-3200MT/s F4-3200C16-16GVK 2000GB Seagate ST2000DM006-2DM1 + 2000GB Western Digital WD20EZAZ-00G + 500GB Samsung SSD 860 + 8002GB Seagate ST8000DM004-2CX1 + 1000GB CT1000BX500SSD1 + 512GB TS512GESD310C NVIDIA GeForce RTX 3060 12GB NVIDIA GA104 HD Audio DELL P2314H Realtek RTL8111/8168/8411 Ubuntu 23.10 6.5.0-9-generic (x86_64) X Server 1.21.1.7 NVIDIA OpenCL 3.0 CUDA 12.2.146 GCC 13.2.0 + CUDA 12.2 ext4 1920x1080 NVIDIA GeForce RTX 3060 OpenBenchmarking.org Kernel Details - Transparent Huge Pages: madvise Compiler 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 Processor Details - Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0x8701013 Python Details - Python 3.11.6 Security Details - 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 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
machine learning pyhpc: GPU - Numpy - 16384 - Isoneutral Mixing pyhpc: CPU - Numpy - 16384 - Isoneutral Mixing scikit-learn: Tree pytorch: NVIDIA CUDA GPU - 16 - Efficientnet_v2_l pyhpc: GPU - Numpy - 262144 - Equation of State pyperformance: json_loads scikit-learn: LocalOutlierFactor pytorch: NVIDIA CUDA GPU - 1 - Efficientnet_v2_l pyhpc: CPU - Numpy - 262144 - Equation of State scikit-learn: Text Vectorizers pyperformance: raytrace scikit-learn: Plot Neighbors pyperformance: django_template pyperformance: regex_compile scikit-learn: Sparse Rand Projections / 100 Iterations pytorch: NVIDIA CUDA GPU - 64 - ResNet-152 pyperformance: pathlib pyhpc: GPU - Numpy - 262144 - Isoneutral Mixing pyperformance: crypto_pyaes pytorch: NVIDIA CUDA GPU - 1 - ResNet-152 pytorch: NVIDIA CUDA GPU - 64 - ResNet-50 scikit-learn: Isotonic / Perturbed Logarithm scikit-learn: Hist Gradient Boosting Adult pytorch: NVIDIA CUDA GPU - 256 - Efficientnet_v2_l pybench: Total For Average Test Times scikit-learn: Sample Without Replacement pytorch: NVIDIA CUDA GPU - 512 - ResNet-152 scikit-learn: Kernel PCA Solvers / Time vs. N Components pytorch: NVIDIA CUDA GPU - 64 - Efficientnet_v2_l pyhpc: CPU - Numpy - 4194304 - Isoneutral Mixing pytorch: NVIDIA CUDA GPU - 256 - ResNet-152 pytorch: CPU - 32 - ResNet-152 pytorch: NVIDIA CUDA GPU - 32 - ResNet-50 pyhpc: GPU - Numpy - 4194304 - Isoneutral Mixing pyperformance: go pyperformance: pickle_pure_python scikit-learn: Hist Gradient Boosting Categorical Only pytorch: NVIDIA CUDA GPU - 32 - Efficientnet_v2_l scikit-learn: Covertype Dataset Benchmark scikit-learn: Sparsify pyhpc: GPU - Numpy - 1048576 - Isoneutral Mixing pytorch: CPU - 256 - ResNet-152 scikit-learn: Plot Hierarchical scikit-learn: Feature Expansions pyperformance: 2to3 pyperformance: chaos scikit-learn: Plot OMP vs. LARS pyperformance: nbody scikit-learn: Hist Gradient Boosting pyhpc: GPU - Numpy - 1048576 - Equation of State scikit-learn: SGD Regression pytorch: NVIDIA CUDA GPU - 32 - ResNet-152 pytorch: NVIDIA CUDA GPU - 16 - ResNet-152 pytorch: NVIDIA CUDA GPU - 512 - ResNet-50 pyhpc: GPU - Numpy - 4194304 - Equation of State pyhpc: CPU - Numpy - 262144 - Isoneutral Mixing scikit-learn: SGDOneClassSVM pyperformance: float pytorch: CPU - 512 - ResNet-152 pytorch: CPU - 512 - Efficientnet_v2_l scikit-learn: SAGA pytorch: CPU - 64 - ResNet-50 pytorch: CPU - 512 - ResNet-50 pytorch: NVIDIA CUDA GPU - 512 - Efficientnet_v2_l pytorch: CPU - 1 - ResNet-50 numpy: pytorch: CPU - 256 - Efficientnet_v2_l pytorch: CPU - 64 - Efficientnet_v2_l scikit-learn: GLM scikit-learn: Kernel PCA Solvers / Time vs. N Samples pytorch: CPU - 16 - ResNet-152 scikit-learn: 20 Newsgroups / Logistic Regression scikit-learn: Plot Ward pytorch: CPU - 1 - ResNet-152 scikit-learn: Lasso pytorch: CPU - 16 - ResNet-50 pyperformance: python_startup pyhpc: CPU - Numpy - 1048576 - Equation of State pytorch: CPU - 64 - ResNet-152 pytorch: NVIDIA CUDA GPU - 1 - ResNet-50 pytorch: CPU - 32 - ResNet-50 pytorch: NVIDIA CUDA GPU - 256 - ResNet-50 scikit-learn: Plot Polynomial Kernel Approximation pyhpc: CPU - Numpy - 4194304 - Equation of State pytorch: CPU - 256 - ResNet-50 pytorch: CPU - 32 - Efficientnet_v2_l pytorch: CPU - 16 - Efficientnet_v2_l scikit-learn: MNIST Dataset scikit-learn: Plot Incremental PCA pyhpc: CPU - Numpy - 1048576 - Isoneutral Mixing scikit-learn: Hist Gradient Boosting Threading pytorch: CPU - 1 - Efficientnet_v2_l scikit-learn: Isotonic / Logistic pytorch: NVIDIA CUDA GPU - 16 - ResNet-50 scikit-learn: TSNE MNIST Dataset pyhpc: GPU - Numpy - 65536 - Isoneutral Mixing pyhpc: GPU - Numpy - 65536 - Equation of State pyhpc: CPU - Numpy - 65536 - Isoneutral Mixing pyhpc: CPU - Numpy - 65536 - Equation of State pyhpc: CPU - Numpy - 16384 - Equation of State scikit-learn: Isolation Forest pyhpc: GPU - Numpy - 16384 - Equation of State mantic mantic-no-omit-framepointer 0.009 0.009 48.338 38.95 0.062 19.5 53.464 39.35 0.061 60.814 262 147.752 28.5 116 613.547 71.81 19.7 0.131 65.1 73.91 201.41 1788.259 103.497 37.36 774 158.262 72.31 37.242 37.88 2.670 71.74 9.84 199.46 2.662 129 259 18.579 37.71 376.145 127.282 0.631 9.77 211.286 131.277 221 62.8 91.499 76.2 109.984 0.263 106.315 74.15 73.01 203.18 1.422 0.131 379.739 67.4 9.87 5.61 868.018 24.24 24.13 37.43 32.36 426.28 5.61 5.62 293.598 72.541 9.88 41.519 57.824 12.72 511.848 24.28 7.61 0.263 9.88 210.88 24.29 202.72 150.732 1.402 24.42 5.63 5.63 65.763 31.006 0.619 110.215 7.31 1470.806 200.30 236.865 0.033 0.015 0.032 0.015 0.003 289.371 0.003 0.008 0.008 52.969 36.10 0.058 20.8 56.754 37.29 0.058 63.875 274 142.451 29.5 120 631.071 73.65 20.2 0.128 66.6 72.27 205.95 1828.300 105.647 36.60 790 161.460 73.75 37.889 37.24 2.626 72.91 10.00 202.68 2.620 131 263 18.865 37.16 370.694 125.442 0.622 9.91 208.391 133.092 224 63.6 92.582 77.1 111.255 0.260 107.527 73.36 72.24 201.14 1.411 0.132 382.611 66.9 9.80 5.65 873.822 24.40 24.28 37.22 32.54 428.61 5.64 5.65 295.096 72.909 9.93 41.728 57.545 12.78 509.537 24.38 7.64 0.262 9.91 211.46 24.35 203.22 150.376 1.405 24.37 5.64 5.64 65.877 31.057 0.618 110.374 7.32 1471.834 200.17 236.786 0.033 0.015 0.032 0.015 0.003 336.372 0.002 OpenBenchmarking.org
PyHPC Benchmarks Device: GPU - Backend: Numpy - Project Size: 16384 - Benchmark: Isoneutral Mixing OpenBenchmarking.org Seconds, Fewer Is Better PyHPC Benchmarks 3.0 Device: GPU - Backend: Numpy - Project Size: 16384 - Benchmark: Isoneutral Mixing mantic mantic-no-omit-framepointer 0.002 0.004 0.006 0.008 0.01 SE +/- 0.000, N = 3 SE +/- 0.000, N = 3 0.009 0.008
PyHPC Benchmarks Device: CPU - Backend: Numpy - Project Size: 16384 - Benchmark: Isoneutral Mixing OpenBenchmarking.org Seconds, Fewer Is Better PyHPC Benchmarks 3.0 Device: CPU - Backend: Numpy - Project Size: 16384 - Benchmark: Isoneutral Mixing mantic mantic-no-omit-framepointer 0.002 0.004 0.006 0.008 0.01 SE +/- 0.000, N = 3 SE +/- 0.000, N = 3 0.009 0.008
Scikit-Learn Benchmark: Tree OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Tree mantic mantic-no-omit-framepointer 12 24 36 48 60 SE +/- 0.59, N = 4 SE +/- 0.48, N = 15 48.34 52.97 1. (F9X) gfortran options: -O0
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: Efficientnet_v2_l mantic mantic-no-omit-framepointer 9 18 27 36 45 SE +/- 0.08, N = 3 SE +/- 0.02, N = 3 38.95 36.10 MIN: 37.12 / MAX: 39.27 MIN: 34.25 / MAX: 38.01
PyHPC Benchmarks Device: GPU - Backend: Numpy - Project Size: 262144 - Benchmark: Equation of State OpenBenchmarking.org Seconds, Fewer Is Better PyHPC Benchmarks 3.0 Device: GPU - Backend: Numpy - Project Size: 262144 - Benchmark: Equation of State mantic mantic-no-omit-framepointer 0.014 0.028 0.042 0.056 0.07 SE +/- 0.001, N = 3 SE +/- 0.000, N = 3 0.062 0.058
PyPerformance Benchmark: json_loads OpenBenchmarking.org Milliseconds, Fewer Is Better PyPerformance 1.0.0 Benchmark: json_loads mantic mantic-no-omit-framepointer 5 10 15 20 25 SE +/- 0.06, N = 3 SE +/- 0.03, N = 3 19.5 20.8
Scikit-Learn Benchmark: LocalOutlierFactor OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: LocalOutlierFactor mantic mantic-no-omit-framepointer 13 26 39 52 65 SE +/- 0.18, N = 3 SE +/- 0.74, N = 15 53.46 56.75 1. (F9X) gfortran options: -O0
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: Efficientnet_v2_l mantic mantic-no-omit-framepointer 9 18 27 36 45 SE +/- 0.47, N = 3 SE +/- 0.26, N = 3 39.35 37.29 MIN: 36.65 / MAX: 40.42 MIN: 35.83 / MAX: 39.17
PyHPC Benchmarks Device: CPU - Backend: Numpy - Project Size: 262144 - Benchmark: Equation of State OpenBenchmarking.org Seconds, Fewer Is Better PyHPC Benchmarks 3.0 Device: CPU - Backend: Numpy - Project Size: 262144 - Benchmark: Equation of State mantic mantic-no-omit-framepointer 0.0137 0.0274 0.0411 0.0548 0.0685 SE +/- 0.001, N = 3 SE +/- 0.000, N = 3 0.061 0.058
Scikit-Learn Benchmark: Text Vectorizers OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Text Vectorizers mantic mantic-no-omit-framepointer 14 28 42 56 70 SE +/- 0.19, N = 3 SE +/- 0.08, N = 3 60.81 63.88 1. (F9X) gfortran options: -O0
PyPerformance Benchmark: raytrace OpenBenchmarking.org Milliseconds, Fewer Is Better PyPerformance 1.0.0 Benchmark: raytrace mantic mantic-no-omit-framepointer 60 120 180 240 300 SE +/- 0.33, N = 3 SE +/- 0.33, N = 3 262 274
Scikit-Learn Benchmark: Plot Neighbors OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot Neighbors mantic mantic-no-omit-framepointer 30 60 90 120 150 SE +/- 1.34, N = 7 SE +/- 0.59, N = 3 147.75 142.45 1. (F9X) gfortran options: -O0
PyPerformance Benchmark: django_template OpenBenchmarking.org Milliseconds, Fewer Is Better PyPerformance 1.0.0 Benchmark: django_template mantic mantic-no-omit-framepointer 7 14 21 28 35 SE +/- 0.03, N = 3 SE +/- 0.06, N = 3 28.5 29.5
PyPerformance Benchmark: regex_compile OpenBenchmarking.org Milliseconds, Fewer Is Better PyPerformance 1.0.0 Benchmark: regex_compile mantic mantic-no-omit-framepointer 30 60 90 120 150 SE +/- 0.00, N = 3 SE +/- 0.33, N = 3 116 120
Scikit-Learn Benchmark: Sparse Random Projections / 100 Iterations OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Sparse Random Projections / 100 Iterations mantic mantic-no-omit-framepointer 140 280 420 560 700 SE +/- 3.80, N = 3 SE +/- 7.06, N = 4 613.55 631.07 1. (F9X) gfortran options: -O0
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-152 mantic mantic-no-omit-framepointer 16 32 48 64 80 SE +/- 0.44, N = 3 SE +/- 0.66, N = 3 71.81 73.65 MIN: 67.31 / MAX: 72.89 MIN: 68.88 / MAX: 75.03
PyPerformance Benchmark: pathlib OpenBenchmarking.org Milliseconds, Fewer Is Better PyPerformance 1.0.0 Benchmark: pathlib mantic mantic-no-omit-framepointer 5 10 15 20 25 SE +/- 0.00, N = 3 SE +/- 0.00, N = 3 19.7 20.2
PyHPC Benchmarks Device: GPU - Backend: Numpy - Project Size: 262144 - Benchmark: Isoneutral Mixing OpenBenchmarking.org Seconds, Fewer Is Better PyHPC Benchmarks 3.0 Device: GPU - Backend: Numpy - Project Size: 262144 - Benchmark: Isoneutral Mixing mantic mantic-no-omit-framepointer 0.0295 0.059 0.0885 0.118 0.1475 SE +/- 0.000, N = 3 SE +/- 0.001, N = 3 0.131 0.128
PyPerformance Benchmark: crypto_pyaes OpenBenchmarking.org Milliseconds, Fewer Is Better PyPerformance 1.0.0 Benchmark: crypto_pyaes mantic mantic-no-omit-framepointer 15 30 45 60 75 SE +/- 0.06, N = 3 SE +/- 0.00, N = 3 65.1 66.6
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-152 mantic mantic-no-omit-framepointer 16 32 48 64 80 SE +/- 0.56, N = 3 SE +/- 0.96, N = 3 73.91 72.27 MIN: 68.9 / MAX: 75.9 MIN: 68.86 / MAX: 76.62
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50 mantic mantic-no-omit-framepointer 50 100 150 200 250 SE +/- 0.58, N = 3 SE +/- 1.98, N = 3 201.41 205.95 MIN: 184.02 / MAX: 203.68 MIN: 186.96 / MAX: 210.21
Scikit-Learn Benchmark: Isotonic / Perturbed Logarithm OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Isotonic / Perturbed Logarithm mantic mantic-no-omit-framepointer 400 800 1200 1600 2000 SE +/- 24.41, N = 3 SE +/- 16.46, N = 3 1788.26 1828.30 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Hist Gradient Boosting Adult OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Hist Gradient Boosting Adult mantic mantic-no-omit-framepointer 20 40 60 80 100 SE +/- 0.70, N = 3 SE +/- 0.59, N = 3 103.50 105.65 1. (F9X) gfortran options: -O0
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: Efficientnet_v2_l mantic mantic-no-omit-framepointer 9 18 27 36 45 SE +/- 0.15, N = 3 SE +/- 0.30, N = 15 37.36 36.60 MIN: 35.47 / MAX: 37.85 MIN: 33.07 / MAX: 39.53
PyBench Total For Average Test Times OpenBenchmarking.org Milliseconds, Fewer Is Better PyBench 2018-02-16 Total For Average Test Times mantic mantic-no-omit-framepointer 200 400 600 800 1000 SE +/- 1.00, N = 3 SE +/- 1.20, N = 3 774 790
Scikit-Learn Benchmark: Sample Without Replacement OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Sample Without Replacement mantic mantic-no-omit-framepointer 40 80 120 160 200 SE +/- 0.60, N = 3 SE +/- 0.62, N = 3 158.26 161.46 1. (F9X) gfortran options: -O0
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-152 mantic mantic-no-omit-framepointer 16 32 48 64 80 SE +/- 0.94, N = 3 SE +/- 0.50, N = 3 72.31 73.75 MIN: 67.38 / MAX: 74.62 MIN: 68.91 / MAX: 75.15
Scikit-Learn Benchmark: Kernel PCA Solvers / Time vs. N Components OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Kernel PCA Solvers / Time vs. N Components mantic mantic-no-omit-framepointer 9 18 27 36 45 SE +/- 0.21, N = 3 SE +/- 0.36, N = 3 37.24 37.89 1. (F9X) gfortran options: -O0
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: Efficientnet_v2_l mantic mantic-no-omit-framepointer 9 18 27 36 45 SE +/- 0.30, N = 9 SE +/- 0.31, N = 15 37.88 37.24 MIN: 35.67 / MAX: 39.63 MIN: 33.97 / MAX: 39.43
PyHPC Benchmarks Device: CPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Isoneutral Mixing OpenBenchmarking.org Seconds, Fewer Is Better PyHPC Benchmarks 3.0 Device: CPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Isoneutral Mixing mantic mantic-no-omit-framepointer 0.6008 1.2016 1.8024 2.4032 3.004 SE +/- 0.010, N = 3 SE +/- 0.002, N = 3 2.670 2.626
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-152 mantic mantic-no-omit-framepointer 16 32 48 64 80 SE +/- 0.24, N = 3 SE +/- 0.83, N = 3 71.74 72.91 MIN: 67.87 / MAX: 72.6 MIN: 68 / MAX: 75.45
PyTorch Device: CPU - Batch Size: 32 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 32 - Model: ResNet-152 mantic mantic-no-omit-framepointer 3 6 9 12 15 SE +/- 0.05, N = 3 SE +/- 0.09, N = 3 9.84 10.00 MIN: 9.6 / MAX: 9.98 MIN: 8.09 / MAX: 10.27
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50 mantic mantic-no-omit-framepointer 40 80 120 160 200 SE +/- 1.06, N = 3 SE +/- 2.52, N = 4 199.46 202.68 MIN: 182.77 / MAX: 206.03 MIN: 182.69 / MAX: 211.53
PyHPC Benchmarks Device: GPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Isoneutral Mixing OpenBenchmarking.org Seconds, Fewer Is Better PyHPC Benchmarks 3.0 Device: GPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Isoneutral Mixing mantic mantic-no-omit-framepointer 0.599 1.198 1.797 2.396 2.995 SE +/- 0.006, N = 3 SE +/- 0.006, N = 3 2.662 2.620
PyPerformance Benchmark: go OpenBenchmarking.org Milliseconds, Fewer Is Better PyPerformance 1.0.0 Benchmark: go mantic mantic-no-omit-framepointer 30 60 90 120 150 SE +/- 0.00, N = 3 SE +/- 0.33, N = 3 129 131
PyPerformance Benchmark: pickle_pure_python OpenBenchmarking.org Milliseconds, Fewer Is Better PyPerformance 1.0.0 Benchmark: pickle_pure_python mantic mantic-no-omit-framepointer 60 120 180 240 300 SE +/- 0.33, N = 3 SE +/- 0.58, N = 3 259 263
Scikit-Learn Benchmark: Hist Gradient Boosting Categorical Only OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Hist Gradient Boosting Categorical Only mantic mantic-no-omit-framepointer 5 10 15 20 25 SE +/- 0.06, N = 3 SE +/- 0.12, N = 3 18.58 18.87 1. (F9X) gfortran options: -O0
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: Efficientnet_v2_l mantic mantic-no-omit-framepointer 9 18 27 36 45 SE +/- 0.24, N = 3 SE +/- 0.30, N = 15 37.71 37.16 MIN: 35.52 / MAX: 38.25 MIN: 34.12 / MAX: 39.48
Scikit-Learn Benchmark: Covertype Dataset Benchmark OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Covertype Dataset Benchmark mantic mantic-no-omit-framepointer 80 160 240 320 400 SE +/- 4.88, N = 3 SE +/- 3.40, N = 3 376.15 370.69 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Sparsify OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Sparsify mantic mantic-no-omit-framepointer 30 60 90 120 150 SE +/- 1.36, N = 5 SE +/- 1.28, N = 5 127.28 125.44 1. (F9X) gfortran options: -O0
PyHPC Benchmarks Device: GPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Isoneutral Mixing OpenBenchmarking.org Seconds, Fewer Is Better PyHPC Benchmarks 3.0 Device: GPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Isoneutral Mixing mantic mantic-no-omit-framepointer 0.142 0.284 0.426 0.568 0.71 SE +/- 0.002, N = 3 SE +/- 0.007, N = 3 0.631 0.622
PyTorch Device: CPU - Batch Size: 256 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 256 - Model: ResNet-152 mantic mantic-no-omit-framepointer 3 6 9 12 15 SE +/- 0.07, N = 3 SE +/- 0.04, N = 3 9.77 9.91 MIN: 9.17 / MAX: 10 MIN: 9.19 / MAX: 10.05
Scikit-Learn Benchmark: Plot Hierarchical OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot Hierarchical mantic mantic-no-omit-framepointer 50 100 150 200 250 SE +/- 0.75, N = 3 SE +/- 0.42, N = 3 211.29 208.39 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Feature Expansions OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Feature Expansions mantic mantic-no-omit-framepointer 30 60 90 120 150 SE +/- 0.86, N = 3 SE +/- 1.22, N = 3 131.28 133.09 1. (F9X) gfortran options: -O0
PyPerformance Benchmark: 2to3 OpenBenchmarking.org Milliseconds, Fewer Is Better PyPerformance 1.0.0 Benchmark: 2to3 mantic mantic-no-omit-framepointer 50 100 150 200 250 SE +/- 0.00, N = 3 SE +/- 0.33, N = 3 221 224
PyPerformance Benchmark: chaos OpenBenchmarking.org Milliseconds, Fewer Is Better PyPerformance 1.0.0 Benchmark: chaos mantic mantic-no-omit-framepointer 14 28 42 56 70 SE +/- 0.03, N = 3 SE +/- 0.20, N = 3 62.8 63.6
Scikit-Learn Benchmark: Plot OMP vs. LARS OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot OMP vs. LARS mantic mantic-no-omit-framepointer 20 40 60 80 100 SE +/- 0.08, N = 3 SE +/- 0.44, N = 3 91.50 92.58 1. (F9X) gfortran options: -O0
PyPerformance Benchmark: nbody OpenBenchmarking.org Milliseconds, Fewer Is Better PyPerformance 1.0.0 Benchmark: nbody mantic mantic-no-omit-framepointer 20 40 60 80 100 SE +/- 0.06, N = 3 SE +/- 0.07, N = 3 76.2 77.1
Scikit-Learn Benchmark: Hist Gradient Boosting OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Hist Gradient Boosting mantic mantic-no-omit-framepointer 20 40 60 80 100 SE +/- 0.22, N = 3 SE +/- 0.25, N = 3 109.98 111.26 1. (F9X) gfortran options: -O0
PyHPC Benchmarks Device: GPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Equation of State OpenBenchmarking.org Seconds, Fewer Is Better PyHPC Benchmarks 3.0 Device: GPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Equation of State mantic mantic-no-omit-framepointer 0.0592 0.1184 0.1776 0.2368 0.296 SE +/- 0.002, N = 3 SE +/- 0.001, N = 3 0.263 0.260
Scikit-Learn Benchmark: SGD Regression OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: SGD Regression mantic mantic-no-omit-framepointer 20 40 60 80 100 SE +/- 1.06, N = 6 SE +/- 0.49, N = 3 106.32 107.53 1. (F9X) gfortran options: -O0
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-152 mantic mantic-no-omit-framepointer 16 32 48 64 80 SE +/- 0.96, N = 3 SE +/- 0.74, N = 3 74.15 73.36 MIN: 68.27 / MAX: 75.61 MIN: 68.19 / MAX: 74.63
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-152 mantic mantic-no-omit-framepointer 16 32 48 64 80 SE +/- 0.96, N = 3 SE +/- 0.20, N = 3 73.01 72.24 MIN: 68.06 / MAX: 75.3 MIN: 68.36 / MAX: 73.14
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-50 mantic mantic-no-omit-framepointer 40 80 120 160 200 SE +/- 1.69, N = 3 SE +/- 0.33, N = 3 203.18 201.14 MIN: 183.76 / MAX: 207.98 MIN: 183.61 / MAX: 202.73
PyHPC Benchmarks Device: GPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Equation of State OpenBenchmarking.org Seconds, Fewer Is Better PyHPC Benchmarks 3.0 Device: GPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Equation of State mantic mantic-no-omit-framepointer 0.32 0.64 0.96 1.28 1.6 SE +/- 0.004, N = 3 SE +/- 0.001, N = 3 1.422 1.411
PyHPC Benchmarks Device: CPU - Backend: Numpy - Project Size: 262144 - Benchmark: Isoneutral Mixing OpenBenchmarking.org Seconds, Fewer Is Better PyHPC Benchmarks 3.0 Device: CPU - Backend: Numpy - Project Size: 262144 - Benchmark: Isoneutral Mixing mantic mantic-no-omit-framepointer 0.0297 0.0594 0.0891 0.1188 0.1485 SE +/- 0.001, N = 3 SE +/- 0.000, N = 3 0.131 0.132
Scikit-Learn Benchmark: SGDOneClassSVM OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: SGDOneClassSVM mantic mantic-no-omit-framepointer 80 160 240 320 400 SE +/- 4.18, N = 3 SE +/- 3.48, N = 7 379.74 382.61 1. (F9X) gfortran options: -O0
PyPerformance Benchmark: float OpenBenchmarking.org Milliseconds, Fewer Is Better PyPerformance 1.0.0 Benchmark: float mantic mantic-no-omit-framepointer 15 30 45 60 75 SE +/- 0.03, N = 3 SE +/- 0.10, N = 3 67.4 66.9
PyTorch Device: CPU - Batch Size: 512 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 512 - Model: ResNet-152 mantic mantic-no-omit-framepointer 3 6 9 12 15 SE +/- 0.02, N = 3 SE +/- 0.07, N = 3 9.87 9.80 MIN: 9.09 / MAX: 9.96 MIN: 9.12 / MAX: 9.98
PyTorch Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l mantic mantic-no-omit-framepointer 1.2713 2.5426 3.8139 5.0852 6.3565 SE +/- 0.01, N = 3 SE +/- 0.02, N = 3 5.61 5.65 MIN: 5.45 / MAX: 5.66 MIN: 5.36 / MAX: 5.93
Scikit-Learn Benchmark: SAGA OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: SAGA mantic mantic-no-omit-framepointer 200 400 600 800 1000 SE +/- 8.69, N = 6 SE +/- 5.60, N = 3 868.02 873.82 1. (F9X) gfortran options: -O0
PyTorch Device: CPU - Batch Size: 64 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 64 - Model: ResNet-50 mantic mantic-no-omit-framepointer 6 12 18 24 30 SE +/- 0.04, N = 3 SE +/- 0.15, N = 3 24.24 24.40 MIN: 23.59 / MAX: 24.49 MIN: 21.6 / MAX: 24.8
PyTorch Device: CPU - Batch Size: 512 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 512 - Model: ResNet-50 mantic mantic-no-omit-framepointer 6 12 18 24 30 SE +/- 0.02, N = 3 SE +/- 0.08, N = 3 24.13 24.28 MIN: 23.58 / MAX: 24.41 MIN: 22.31 / MAX: 24.53
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: Efficientnet_v2_l mantic mantic-no-omit-framepointer 9 18 27 36 45 SE +/- 0.03, N = 3 SE +/- 0.33, N = 8 37.43 37.22 MIN: 35.81 / MAX: 38.02 MIN: 34.99 / MAX: 39.08
PyTorch Device: CPU - Batch Size: 1 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 1 - Model: ResNet-50 mantic mantic-no-omit-framepointer 8 16 24 32 40 SE +/- 0.11, N = 3 SE +/- 0.16, N = 3 32.36 32.54 MIN: 31.89 / MAX: 32.7 MIN: 31.64 / MAX: 32.94
Numpy Benchmark OpenBenchmarking.org Score, More Is Better Numpy Benchmark mantic mantic-no-omit-framepointer 90 180 270 360 450 SE +/- 1.20, N = 3 SE +/- 0.90, N = 3 426.28 428.61
PyTorch Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l mantic mantic-no-omit-framepointer 1.269 2.538 3.807 5.076 6.345 SE +/- 0.02, N = 3 SE +/- 0.01, N = 3 5.61 5.64 MIN: 5.44 / MAX: 5.65 MIN: 5.29 / MAX: 5.68
PyTorch Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l mantic mantic-no-omit-framepointer 1.2713 2.5426 3.8139 5.0852 6.3565 SE +/- 0.01, N = 3 SE +/- 0.01, N = 3 5.62 5.65 MIN: 5.35 / MAX: 5.66 MIN: 5.45 / MAX: 5.7
Scikit-Learn Benchmark: GLM OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: GLM mantic mantic-no-omit-framepointer 60 120 180 240 300 SE +/- 1.06, N = 3 SE +/- 1.07, N = 3 293.60 295.10 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Kernel PCA Solvers / Time vs. N Samples OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Kernel PCA Solvers / Time vs. N Samples mantic mantic-no-omit-framepointer 16 32 48 64 80 SE +/- 0.05, N = 3 SE +/- 0.16, N = 3 72.54 72.91 1. (F9X) gfortran options: -O0
PyTorch Device: CPU - Batch Size: 16 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 16 - Model: ResNet-152 mantic mantic-no-omit-framepointer 3 6 9 12 15 SE +/- 0.04, N = 3 SE +/- 0.01, N = 3 9.88 9.93 MIN: 9.31 / MAX: 10.01 MIN: 9.39 / MAX: 10.01
Scikit-Learn Benchmark: 20 Newsgroups / Logistic Regression OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: 20 Newsgroups / Logistic Regression mantic mantic-no-omit-framepointer 10 20 30 40 50 SE +/- 0.19, N = 3 SE +/- 0.24, N = 3 41.52 41.73 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Plot Ward OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot Ward mantic mantic-no-omit-framepointer 13 26 39 52 65 SE +/- 0.21, N = 3 SE +/- 0.22, N = 3 57.82 57.55 1. (F9X) gfortran options: -O0
PyTorch Device: CPU - Batch Size: 1 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 1 - Model: ResNet-152 mantic mantic-no-omit-framepointer 3 6 9 12 15 SE +/- 0.03, N = 3 SE +/- 0.04, N = 3 12.72 12.78 MIN: 11.99 / MAX: 12.8 MIN: 11.9 / MAX: 12.9
Scikit-Learn Benchmark: Lasso OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Lasso mantic mantic-no-omit-framepointer 110 220 330 440 550 SE +/- 3.22, N = 3 SE +/- 3.50, N = 3 511.85 509.54 1. (F9X) gfortran options: -O0
PyTorch Device: CPU - Batch Size: 16 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 16 - Model: ResNet-50 mantic mantic-no-omit-framepointer 6 12 18 24 30 SE +/- 0.05, N = 3 SE +/- 0.16, N = 3 24.28 24.38 MIN: 20.22 / MAX: 24.56 MIN: 22.2 / MAX: 24.87
PyPerformance Benchmark: python_startup OpenBenchmarking.org Milliseconds, Fewer Is Better PyPerformance 1.0.0 Benchmark: python_startup mantic mantic-no-omit-framepointer 2 4 6 8 10 SE +/- 0.01, N = 3 SE +/- 0.01, N = 3 7.61 7.64
PyHPC Benchmarks Device: CPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Equation of State OpenBenchmarking.org Seconds, Fewer Is Better PyHPC Benchmarks 3.0 Device: CPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Equation of State mantic mantic-no-omit-framepointer 0.0592 0.1184 0.1776 0.2368 0.296 SE +/- 0.002, N = 3 SE +/- 0.000, N = 3 0.263 0.262
PyTorch Device: CPU - Batch Size: 64 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 64 - Model: ResNet-152 mantic mantic-no-omit-framepointer 3 6 9 12 15 SE +/- 0.03, N = 3 SE +/- 0.02, N = 3 9.88 9.91 MIN: 8.8 / MAX: 9.98 MIN: 8.69 / MAX: 10.08
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-50 mantic mantic-no-omit-framepointer 50 100 150 200 250 SE +/- 2.67, N = 3 SE +/- 1.46, N = 15 210.88 211.46 MIN: 195.21 / MAX: 218.16 MIN: 192.13 / MAX: 223.01
PyTorch Device: CPU - Batch Size: 32 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 32 - Model: ResNet-50 mantic mantic-no-omit-framepointer 6 12 18 24 30 SE +/- 0.10, N = 3 SE +/- 0.16, N = 3 24.29 24.35 MIN: 22.24 / MAX: 24.66 MIN: 23.67 / MAX: 24.87
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50 mantic mantic-no-omit-framepointer 40 80 120 160 200 SE +/- 1.76, N = 3 SE +/- 1.21, N = 3 202.72 203.22 MIN: 183.1 / MAX: 207.93 MIN: 185.88 / MAX: 206.71
Scikit-Learn Benchmark: Plot Polynomial Kernel Approximation OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot Polynomial Kernel Approximation mantic mantic-no-omit-framepointer 30 60 90 120 150 SE +/- 1.22, N = 3 SE +/- 1.20, N = 3 150.73 150.38 1. (F9X) gfortran options: -O0
PyHPC Benchmarks Device: CPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Equation of State OpenBenchmarking.org Seconds, Fewer Is Better PyHPC Benchmarks 3.0 Device: CPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Equation of State mantic mantic-no-omit-framepointer 0.3161 0.6322 0.9483 1.2644 1.5805 SE +/- 0.003, N = 3 SE +/- 0.004, N = 3 1.402 1.405
PyTorch Device: CPU - Batch Size: 256 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 256 - Model: ResNet-50 mantic mantic-no-omit-framepointer 6 12 18 24 30 SE +/- 0.03, N = 3 SE +/- 0.11, N = 3 24.42 24.37 MIN: 20.15 / MAX: 24.74 MIN: 23.76 / MAX: 24.81
PyTorch Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l mantic mantic-no-omit-framepointer 1.269 2.538 3.807 5.076 6.345 SE +/- 0.01, N = 3 SE +/- 0.01, N = 3 5.63 5.64 MIN: 5.31 / MAX: 5.68 MIN: 5.52 / MAX: 5.69
PyTorch Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l mantic mantic-no-omit-framepointer 1.269 2.538 3.807 5.076 6.345 SE +/- 0.02, N = 3 SE +/- 0.01, N = 3 5.63 5.64 MIN: 5.39 / MAX: 5.71 MIN: 5.45 / MAX: 5.68
Scikit-Learn Benchmark: MNIST Dataset OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: MNIST Dataset mantic mantic-no-omit-framepointer 15 30 45 60 75 SE +/- 0.82, N = 4 SE +/- 0.47, N = 3 65.76 65.88 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Plot Incremental PCA OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot Incremental PCA mantic mantic-no-omit-framepointer 7 14 21 28 35 SE +/- 0.03, N = 3 SE +/- 0.07, N = 3 31.01 31.06 1. (F9X) gfortran options: -O0
PyHPC Benchmarks Device: CPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Isoneutral Mixing OpenBenchmarking.org Seconds, Fewer Is Better PyHPC Benchmarks 3.0 Device: CPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Isoneutral Mixing mantic mantic-no-omit-framepointer 0.1393 0.2786 0.4179 0.5572 0.6965 SE +/- 0.001, N = 3 SE +/- 0.000, N = 3 0.619 0.618
Scikit-Learn Benchmark: Hist Gradient Boosting Threading OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Hist Gradient Boosting Threading mantic mantic-no-omit-framepointer 20 40 60 80 100 SE +/- 0.13, N = 3 SE +/- 0.15, N = 3 110.22 110.37 1. (F9X) gfortran options: -O0
PyTorch Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l mantic mantic-no-omit-framepointer 2 4 6 8 10 SE +/- 0.00, N = 3 SE +/- 0.02, N = 3 7.31 7.32 MIN: 7.16 / MAX: 7.34 MIN: 7.23 / MAX: 7.38
Scikit-Learn Benchmark: Isotonic / Logistic OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Isotonic / Logistic mantic mantic-no-omit-framepointer 300 600 900 1200 1500 SE +/- 12.29, N = 3 SE +/- 14.46, N = 3 1470.81 1471.83 1. (F9X) gfortran options: -O0
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50 mantic mantic-no-omit-framepointer 40 80 120 160 200 SE +/- 0.25, N = 3 SE +/- 0.96, N = 3 200.30 200.17 MIN: 182.88 / MAX: 202.36 MIN: 183.43 / MAX: 203.55
Scikit-Learn Benchmark: TSNE MNIST Dataset OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: TSNE MNIST Dataset mantic mantic-no-omit-framepointer 50 100 150 200 250 SE +/- 0.44, N = 3 SE +/- 0.54, N = 3 236.87 236.79 1. (F9X) gfortran options: -O0
PyHPC Benchmarks Device: GPU - Backend: Numpy - Project Size: 65536 - Benchmark: Isoneutral Mixing OpenBenchmarking.org Seconds, Fewer Is Better PyHPC Benchmarks 3.0 Device: GPU - Backend: Numpy - Project Size: 65536 - Benchmark: Isoneutral Mixing mantic mantic-no-omit-framepointer 0.0074 0.0148 0.0222 0.0296 0.037 SE +/- 0.000, N = 3 SE +/- 0.000, N = 3 0.033 0.033
PyHPC Benchmarks Device: GPU - Backend: Numpy - Project Size: 65536 - Benchmark: Equation of State OpenBenchmarking.org Seconds, Fewer Is Better PyHPC Benchmarks 3.0 Device: GPU - Backend: Numpy - Project Size: 65536 - Benchmark: Equation of State mantic mantic-no-omit-framepointer 0.0034 0.0068 0.0102 0.0136 0.017 SE +/- 0.000, N = 3 SE +/- 0.000, N = 3 0.015 0.015
PyHPC Benchmarks Device: CPU - Backend: Numpy - Project Size: 65536 - Benchmark: Isoneutral Mixing OpenBenchmarking.org Seconds, Fewer Is Better PyHPC Benchmarks 3.0 Device: CPU - Backend: Numpy - Project Size: 65536 - Benchmark: Isoneutral Mixing mantic mantic-no-omit-framepointer 0.0072 0.0144 0.0216 0.0288 0.036 SE +/- 0.000, N = 3 SE +/- 0.000, N = 3 0.032 0.032
PyHPC Benchmarks Device: CPU - Backend: Numpy - Project Size: 65536 - Benchmark: Equation of State OpenBenchmarking.org Seconds, Fewer Is Better PyHPC Benchmarks 3.0 Device: CPU - Backend: Numpy - Project Size: 65536 - Benchmark: Equation of State mantic mantic-no-omit-framepointer 0.0034 0.0068 0.0102 0.0136 0.017 SE +/- 0.000, N = 3 SE +/- 0.000, N = 3 0.015 0.015
PyHPC Benchmarks Device: CPU - Backend: Numpy - Project Size: 16384 - Benchmark: Equation of State OpenBenchmarking.org Seconds, Fewer Is Better PyHPC Benchmarks 3.0 Device: CPU - Backend: Numpy - Project Size: 16384 - Benchmark: Equation of State mantic mantic-no-omit-framepointer 0.0007 0.0014 0.0021 0.0028 0.0035 SE +/- 0.000, N = 3 SE +/- 0.000, N = 3 0.003 0.003
Scikit-Learn Benchmark: Isolation Forest OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Isolation Forest mantic mantic-no-omit-framepointer 70 140 210 280 350 SE +/- 1.30, N = 3 SE +/- 51.04, N = 9 289.37 336.37 1. (F9X) gfortran options: -O0
PyHPC Benchmarks Device: GPU - Backend: Numpy - Project Size: 16384 - Benchmark: Equation of State OpenBenchmarking.org Seconds, Fewer Is Better PyHPC Benchmarks 3.0 Device: GPU - Backend: Numpy - Project Size: 16384 - Benchmark: Equation of State mantic mantic-no-omit-framepointer 0.0007 0.0014 0.0021 0.0028 0.0035 SE +/- 0.000, N = 3 SE +/- 0.000, N = 15 0.003 0.002
Phoronix Test Suite v10.8.5