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&grr .
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 scikit-learn: SAGA scikit-learn: Isotonic / Perturbed Logarithm scikit-learn: Isotonic / Logistic scikit-learn: Isolation Forest scikit-learn: Sparse Rand Projections / 100 Iterations scikit-learn: SGDOneClassSVM scikit-learn: Lasso scikit-learn: Covertype Dataset Benchmark scikit-learn: GLM pytorch: CPU - 256 - Efficientnet_v2_l pytorch: CPU - 16 - Efficientnet_v2_l pytorch: CPU - 512 - Efficientnet_v2_l pytorch: CPU - 32 - Efficientnet_v2_l pytorch: CPU - 64 - Efficientnet_v2_l scikit-learn: TSNE MNIST Dataset scikit-learn: Plot Neighbors scikit-learn: Plot Hierarchical scikit-learn: Sparsify pytorch: NVIDIA CUDA GPU - 64 - Efficientnet_v2_l scikit-learn: Plot Incremental PCA scikit-learn: Sample Without Replacement pytorch: CPU - 512 - ResNet-152 pytorch: CPU - 256 - ResNet-152 pytorch: CPU - 16 - ResNet-152 pytorch: CPU - 32 - ResNet-152 pytorch: CPU - 64 - ResNet-152 scikit-learn: Plot Polynomial Kernel Approximation scikit-learn: SGD Regression scikit-learn: LocalOutlierFactor pytorch: NVIDIA CUDA GPU - 256 - Efficientnet_v2_l pytorch: NVIDIA CUDA GPU - 32 - Efficientnet_v2_l scikit-learn: Tree numpy: scikit-learn: Feature Expansions pytorch: CPU - 1 - Efficientnet_v2_l scikit-learn: Hist Gradient Boosting scikit-learn: Hist Gradient Boosting Threading scikit-learn: Hist Gradient Boosting Adult scikit-learn: Plot OMP vs. LARS pyhpc: CPU - Numpy - 4194304 - Isoneutral Mixing pyhpc: GPU - Numpy - 4194304 - Isoneutral Mixing pytorch: NVIDIA CUDA GPU - 512 - Efficientnet_v2_l scikit-learn: MNIST Dataset scikit-learn: Kernel PCA Solvers / Time vs. N Samples pytorch: CPU - 1 - ResNet-152 scikit-learn: Text Vectorizers pytorch: CPU - 512 - ResNet-50 pytorch: CPU - 64 - ResNet-50 pytorch: CPU - 16 - ResNet-50 pytorch: CPU - 32 - ResNet-50 pytorch: CPU - 256 - ResNet-50 scikit-learn: Plot Ward pyperformance: python_startup pytorch: NVIDIA CUDA GPU - 16 - Efficientnet_v2_l scikit-learn: 20 Newsgroups / Logistic Regression scikit-learn: Kernel PCA Solvers / Time vs. N Components pyhpc: GPU - Numpy - 4194304 - Equation of State pyhpc: CPU - Numpy - 4194304 - Equation of State pyperformance: raytrace pyperformance: 2to3 pytorch: CPU - 1 - ResNet-50 pyperformance: pathlib pytorch: NVIDIA CUDA GPU - 256 - ResNet-152 pytorch: NVIDIA CUDA GPU - 16 - ResNet-152 pytorch: NVIDIA CUDA GPU - 64 - ResNet-152 pytorch: NVIDIA CUDA GPU - 512 - ResNet-152 pytorch: NVIDIA CUDA GPU - 32 - ResNet-152 pytorch: NVIDIA CUDA GPU - 1 - Efficientnet_v2_l pyperformance: pickle_pure_python pyhpc: GPU - Numpy - 1048576 - Isoneutral Mixing pyperformance: go pyhpc: CPU - Numpy - 1048576 - Isoneutral Mixing pyperformance: nbody pyperformance: django_template pytorch: NVIDIA CUDA GPU - 1 - ResNet-50 pyperformance: json_loads scikit-learn: Hist Gradient Boosting Categorical Only pyperformance: float pyperformance: regex_compile pyperformance: crypto_pyaes pyperformance: chaos pyhpc: CPU - Numpy - 262144 - Isoneutral Mixing pyhpc: GPU - Numpy - 262144 - Isoneutral Mixing pytorch: NVIDIA CUDA GPU - 1 - ResNet-152 pybench: Total For Average Test Times pytorch: NVIDIA CUDA GPU - 32 - ResNet-50 pytorch: NVIDIA CUDA GPU - 16 - ResNet-50 pytorch: NVIDIA CUDA GPU - 512 - ResNet-50 pytorch: NVIDIA CUDA GPU - 256 - ResNet-50 pytorch: NVIDIA CUDA GPU - 64 - ResNet-50 pyhpc: CPU - Numpy - 16384 - Isoneutral Mixing pyhpc: GPU - Numpy - 16384 - Isoneutral Mixing pyhpc: CPU - Numpy - 1048576 - Equation of State pyhpc: GPU - Numpy - 1048576 - Equation of State pyhpc: GPU - Numpy - 16384 - Equation of State pyhpc: GPU - Numpy - 262144 - Equation of State pyhpc: CPU - Numpy - 262144 - Equation of State pyhpc: GPU - Numpy - 65536 - Isoneutral Mixing pyhpc: CPU - Numpy - 65536 - Isoneutral Mixing pyhpc: CPU - Numpy - 16384 - Equation of State pyhpc: GPU - Numpy - 65536 - Equation of State pyhpc: CPU - Numpy - 65536 - Equation of State pyhpc: CPU - JAX - 16384 - Isoneutral Mixing mantic mantic-no-omit-framepointer 868.018 1788.259 1470.806 289.371 613.547 379.739 511.848 376.145 293.598 5.61 5.63 5.61 5.63 5.62 236.865 147.752 211.286 127.282 37.88 31.006 158.262 9.87 9.77 9.88 9.84 9.88 150.732 106.315 53.464 37.36 37.71 48.338 426.28 131.277 7.31 109.984 110.215 103.497 91.499 2.670 2.662 37.43 65.763 72.541 12.72 60.814 24.13 24.24 24.28 24.29 24.42 57.824 7.61 38.95 41.519 37.242 1.422 1.402 262 221 32.36 19.7 71.74 73.01 71.81 72.31 74.15 39.35 259 0.631 129 0.619 76.2 28.5 210.88 19.5 18.579 67.4 116 65.1 62.8 0.131 0.131 73.91 774 199.46 200.30 203.18 202.72 201.41 0.009 0.009 0.263 0.263 0.003 0.062 0.061 0.033 0.032 0.003 0.015 0.015 873.822 1828.300 1471.834 336.372 631.071 382.611 509.537 370.694 295.096 5.64 5.64 5.65 5.64 5.65 236.786 142.451 208.391 125.442 37.24 31.057 161.460 9.80 9.91 9.93 10.00 9.91 150.376 107.527 56.754 36.60 37.16 52.969 428.61 133.092 7.32 111.255 110.374 105.647 92.582 2.626 2.620 37.22 65.877 72.909 12.78 63.875 24.28 24.40 24.38 24.35 24.37 57.545 7.64 36.10 41.728 37.889 1.411 1.405 274 224 32.54 20.2 72.91 72.24 73.65 73.75 73.36 37.29 263 0.622 131 0.618 77.1 29.5 211.46 20.8 18.865 66.9 120 66.6 63.6 0.132 0.128 72.27 790 202.68 200.17 201.14 203.22 205.95 0.008 0.008 0.262 0.260 0.002 0.058 0.058 0.033 0.032 0.003 0.015 0.015 OpenBenchmarking.org
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
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: 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
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
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
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
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
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: 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
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: 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
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
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: 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: 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
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
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: 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
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
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
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: 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: 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
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
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: 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
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
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
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: 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
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: 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
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
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
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: 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
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
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
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
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
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
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
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: 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: 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: 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
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: 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: 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
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: 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
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
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
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
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: 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
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: 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
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
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
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
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
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: 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: 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
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
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: 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
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
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
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
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
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
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
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
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: 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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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: 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
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
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
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
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
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: 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: 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
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: 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
Phoronix Test Suite v10.8.4