AMD Ryzen 9 3900X 12-Core testing with a MSI X570-A PRO (MS-7C37) v3.0 (H.70 BIOS) and NVIDIA GeForce RTX 3060 12GB on Ubuntu 23.10 via the Phoronix Test Suite.
Compare your own system(s) to this result file with the
Phoronix Test Suite by running the command:
phoronix-test-suite benchmark 2405015-VPA1-DESKTOP46 Desktop machine learning - Phoronix Test Suite Desktop machine learning AMD Ryzen 9 3900X 12-Core testing with a MSI X570-A PRO (MS-7C37) v3.0 (H.70 BIOS) and NVIDIA GeForce RTX 3060 12GB on Ubuntu 23.10 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2405015-VPA1-DESKTOP46&grr&sor&rro .
Desktop 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 noble 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 DELL P2314H + U32J59x Realtek RTL8111/8168/8211/8411 Ubuntu 24.04 6.8.0-31-generic (x86_64) GCC 13.2.0 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 - noble: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-backtrace --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-uJ7kn6/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-uJ7kn6/gcc-13-13.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v Processor Details - Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0x8701013 Python Details - mantic: Python 3.11.6 - mantic-no-omit-framepointer: Python 3.11.6 - noble: Python 3.12.3 Security Details - mantic: gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Mitigation of untrained return thunk; SMT enabled with STIBP protection + spec_rstack_overflow: Mitigation of safe RET + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected - mantic-no-omit-framepointer: gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Mitigation of untrained return thunk; SMT enabled with STIBP protection + spec_rstack_overflow: Mitigation of safe RET + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected - noble: gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + reg_file_data_sampling: Not affected + retbleed: Mitigation of untrained return thunk; SMT enabled with STIBP protection + spec_rstack_overflow: Mitigation of Safe RET + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines; IBPB: conditional; STIBP: always-on; RSB filling; PBRSB-eIBRS: Not affected; BHI: Not affected + srbds: Not affected + tsx_async_abort: Not affected Environment Details - mantic-no-omit-framepointer: CXXFLAGS=-fno-omit-frame-pointer QMAKE_CFLAGS=-fno-omit-frame-pointer CFLAGS=-fno-omit-frame-pointer CFLAGS_OVERRIDE=-fno-omit-frame-pointer QMAKE_CXXFLAGS=-fno-omit-frame-pointer FFLAGS=-fno-omit-frame-pointer - noble: CXXFLAGS="-fno-omit-frame-pointer -frecord-gcc-switches -O2" QMAKE_CFLAGS="-fno-omit-frame-pointer -frecord-gcc-switches -O2" CFLAGS="-fno-omit-frame-pointer -frecord-gcc-switches -O2" CFLAGS_OVERRIDE="-fno-omit-frame-pointer -frecord-gcc-switches -O2" QMAKE_CXXFLAGS="-fno-omit-frame-pointer -frecord-gcc-switches -O2" FFLAGS="-fno-omit-frame-pointer -frecord-gcc-switches -O2"
Desktop machine learning scikit-learn: Isotonic / Perturbed Logarithm scikit-learn: SAGA 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 - 16 - Efficientnet_v2_l pytorch: CPU - 32 - Efficientnet_v2_l pytorch: CPU - 256 - Efficientnet_v2_l pytorch: CPU - 512 - Efficientnet_v2_l pytorch: CPU - 64 - Efficientnet_v2_l scikit-learn: TSNE MNIST Dataset scikit-learn: Plot Hierarchical scikit-learn: Plot Neighbors pytorch: NVIDIA CUDA GPU - 64 - Efficientnet_v2_l scikit-learn: Sparsify scikit-learn: Sample Without Replacement pytorch: CPU - 512 - ResNet-152 pytorch: CPU - 256 - ResNet-152 pytorch: CPU - 32 - ResNet-152 pytorch: CPU - 64 - ResNet-152 pytorch: CPU - 16 - ResNet-152 scikit-learn: Plot Polynomial Kernel Approximation pytorch: NVIDIA CUDA GPU - 256 - Efficientnet_v2_l pytorch: NVIDIA CUDA GPU - 32 - Efficientnet_v2_l numpy: scikit-learn: Feature Expansions scikit-learn: SGD Regression scikit-learn: Plot Incremental PCA pytorch: CPU - 1 - Efficientnet_v2_l scikit-learn: LocalOutlierFactor scikit-learn: Hist Gradient Boosting scikit-learn: Hist Gradient Boosting Threading scikit-learn: Hist Gradient Boosting Adult scikit-learn: Tree pyhpc: CPU - Numpy - 4194304 - Isoneutral Mixing pyhpc: GPU - Numpy - 4194304 - Isoneutral Mixing pytorch: NVIDIA CUDA GPU - 512 - Efficientnet_v2_l scikit-learn: Plot OMP vs. LARS scikit-learn: MNIST Dataset scikit-learn: Kernel PCA Solvers / Time vs. N Samples pytorch: CPU - 1 - ResNet-152 scikit-learn: Text Vectorizers pytorch: CPU - 32 - ResNet-50 pytorch: CPU - 512 - ResNet-50 pytorch: CPU - 64 - ResNet-50 pytorch: CPU - 256 - ResNet-50 pytorch: CPU - 16 - 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 pytorch: CPU - 1 - ResNet-50 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: raytrace pyperformance: go pyhpc: GPU - Numpy - 1048576 - Isoneutral Mixing pyhpc: CPU - Numpy - 1048576 - Isoneutral Mixing pyperformance: 2to3 scikit-learn: Hist Gradient Boosting Categorical Only pytorch: NVIDIA CUDA GPU - 1 - ResNet-50 pyperformance: json_loads pyhpc: CPU - Numpy - 262144 - Isoneutral Mixing pyhpc: GPU - Numpy - 262144 - Isoneutral Mixing pyperformance: pathlib pybench: Total For Average Test Times pyperformance: pickle_pure_python pytorch: NVIDIA CUDA GPU - 1 - ResNet-152 pyperformance: nbody pyperformance: django_template pyperformance: float pytorch: NVIDIA CUDA GPU - 32 - ResNet-50 pyperformance: regex_compile pyperformance: crypto_pyaes pyperformance: chaos 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: GPU - Numpy - 16384 - Equation of State pyhpc: CPU - Numpy - 1048576 - Equation of State pyhpc: GPU - Numpy - 1048576 - 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 - 65536 - Equation of State pyhpc: GPU - Numpy - 65536 - Equation of State pyhpc: CPU - Numpy - 16384 - Equation of State pyhpc: CPU - JAX - 16384 - Isoneutral Mixing mantic mantic-no-omit-framepointer noble 1788.259 868.018 1470.806 289.371 613.547 379.739 511.848 376.145 293.598 5.63 5.63 5.61 5.61 5.62 236.865 211.286 147.752 37.88 127.282 158.262 9.87 9.77 9.84 9.88 9.88 150.732 37.36 37.71 426.28 131.277 106.315 31.006 7.31 53.464 109.984 110.215 103.497 48.338 2.670 2.662 37.43 91.499 65.763 72.541 12.72 60.814 24.29 24.13 24.24 24.42 24.28 57.824 7.61 38.95 41.519 37.242 1.422 1.402 32.36 71.74 73.01 71.81 72.31 74.15 39.35 262 129 0.631 0.619 221 18.579 210.88 19.5 0.131 0.131 19.7 774 259 73.91 76.2 28.5 67.4 199.46 116 65.1 62.8 200.30 203.18 202.72 201.41 0.009 0.009 0.003 0.263 0.263 0.062 0.061 0.033 0.032 0.015 0.015 0.003 1828.300 873.822 1471.834 336.372 631.071 382.611 509.537 370.694 295.096 5.64 5.64 5.64 5.65 5.65 236.786 208.391 142.451 37.24 125.442 161.460 9.80 9.91 10.00 9.91 9.93 150.376 36.60 37.16 428.61 133.092 107.527 31.057 7.32 56.754 111.255 110.374 105.647 52.969 2.626 2.620 37.22 92.582 65.877 72.909 12.78 63.875 24.35 24.28 24.40 24.37 24.38 57.545 7.64 36.10 41.728 37.889 1.411 1.405 32.54 72.91 72.24 73.65 73.75 73.36 37.29 274 131 0.622 0.618 224 18.865 211.46 20.8 0.132 0.128 20.2 790 263 72.27 77.1 29.5 66.9 202.68 120 66.6 63.6 200.17 201.14 203.22 205.95 0.008 0.008 0.002 0.262 0.260 0.058 0.058 0.033 0.032 0.015 0.015 0.003 1963.772 869.369 1684.546 314.034 663.953 385.383 345.400 381.447 269.806 5.59 5.59 5.61 5.60 5.60 285.823 207.104 142.159 125.069 179.638 9.87 9.86 9.81 9.87 9.88 145.363 430.83 133.144 78.880 30.617 7.31 54.288 117.407 111.554 112.713 47.033 2.720 2.668 68.172 65.416 70.022 12.89 66.393 24.12 24.30 24.19 24.33 24.43 56.132 8.76 41.914 37.107 1.446 1.436 32.34 121 0.630 0.631 19.932 22.8 0.133 0.136 839 0.009 0.008 0.003 0.261 0.262 0.061 0.060 0.034 0.033 0.016 0.015 0.003 OpenBenchmarking.org
Scikit-Learn Benchmark: Isotonic / Perturbed Logarithm OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Isotonic / Perturbed Logarithm noble mantic-no-omit-framepointer mantic 400 800 1200 1600 2000 SE +/- 1.48, N = 3 SE +/- 16.46, N = 3 SE +/- 24.41, N = 3 1963.77 1828.30 1788.26 -O2 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: SAGA OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: SAGA mantic-no-omit-framepointer noble mantic 200 400 600 800 1000 SE +/- 5.60, N = 3 SE +/- 10.35, N = 3 SE +/- 8.69, N = 6 873.82 869.37 868.02 -O2 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Isotonic / Logistic OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Isotonic / Logistic noble mantic-no-omit-framepointer mantic 400 800 1200 1600 2000 SE +/- 9.43, N = 3 SE +/- 14.46, N = 3 SE +/- 12.29, N = 3 1684.55 1471.83 1470.81 -O2 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-no-omit-framepointer noble mantic 70 140 210 280 350 SE +/- 51.04, N = 9 SE +/- 2.83, N = 3 SE +/- 1.30, N = 3 336.37 314.03 289.37 -O2 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 noble mantic-no-omit-framepointer mantic 140 280 420 560 700 SE +/- 4.34, N = 3 SE +/- 7.06, N = 4 SE +/- 3.80, N = 3 663.95 631.07 613.55 -O2 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: SGDOneClassSVM OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: SGDOneClassSVM noble mantic-no-omit-framepointer mantic 80 160 240 320 400 SE +/- 3.55, N = 3 SE +/- 3.48, N = 7 SE +/- 4.18, N = 3 385.38 382.61 379.74 -O2 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 noble 110 220 330 440 550 SE +/- 3.22, N = 3 SE +/- 3.50, N = 3 SE +/- 1.37, N = 3 511.85 509.54 345.40 -O2 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 noble mantic mantic-no-omit-framepointer 80 160 240 320 400 SE +/- 2.58, N = 3 SE +/- 4.88, N = 3 SE +/- 3.40, N = 3 381.45 376.15 370.69 -O2 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: GLM OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: GLM mantic-no-omit-framepointer mantic noble 60 120 180 240 300 SE +/- 1.07, N = 3 SE +/- 1.06, N = 3 SE +/- 0.93, N = 3 295.10 293.60 269.81 -O2 1. (F9X) gfortran options: -O0
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 noble mantic mantic-no-omit-framepointer 1.269 2.538 3.807 5.076 6.345 SE +/- 0.02, N = 3 SE +/- 0.02, N = 3 SE +/- 0.01, N = 3 5.59 5.63 5.64 MIN: 5.31 / MAX: 5.65 MIN: 5.39 / MAX: 5.71 MIN: 5.45 / MAX: 5.68
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 noble mantic mantic-no-omit-framepointer 1.269 2.538 3.807 5.076 6.345 SE +/- 0.00, N = 3 SE +/- 0.01, N = 3 SE +/- 0.01, N = 3 5.59 5.63 5.64 MIN: 5.46 / MAX: 5.64 MIN: 5.31 / MAX: 5.68 MIN: 5.52 / MAX: 5.69
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 noble mantic mantic-no-omit-framepointer 1.269 2.538 3.807 5.076 6.345 SE +/- 0.02, N = 3 SE +/- 0.02, N = 3 SE +/- 0.01, N = 3 5.61 5.61 5.64 MIN: 5.46 / MAX: 5.67 MIN: 5.44 / MAX: 5.65 MIN: 5.29 / 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 noble 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 SE +/- 0.02, N = 3 5.60 5.61 5.65 MIN: 5.37 / MAX: 5.66 MIN: 5.45 / MAX: 5.66 MIN: 5.36 / MAX: 5.93
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 noble 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 SE +/- 0.01, N = 3 5.60 5.62 5.65 MIN: 5.32 / MAX: 5.64 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 noble mantic mantic-no-omit-framepointer 60 120 180 240 300 SE +/- 0.91, N = 3 SE +/- 0.44, N = 3 SE +/- 0.54, N = 3 285.82 236.87 236.79 -O2 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 noble 50 100 150 200 250 SE +/- 0.75, N = 3 SE +/- 0.42, N = 3 SE +/- 2.35, N = 3 211.29 208.39 207.10 -O2 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 noble 30 60 90 120 150 SE +/- 1.34, N = 7 SE +/- 0.59, N = 3 SE +/- 1.09, N = 3 147.75 142.45 142.16 -O2 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-no-omit-framepointer mantic 9 18 27 36 45 SE +/- 0.31, N = 15 SE +/- 0.30, N = 9 37.24 37.88 MIN: 33.97 / MAX: 39.43 MIN: 35.67 / MAX: 39.63
Scikit-Learn Benchmark: Sparsify OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Sparsify mantic mantic-no-omit-framepointer noble 30 60 90 120 150 SE +/- 1.36, N = 5 SE +/- 1.28, N = 5 SE +/- 0.65, N = 3 127.28 125.44 125.07 -O2 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 noble mantic-no-omit-framepointer mantic 40 80 120 160 200 SE +/- 2.21, N = 3 SE +/- 0.62, N = 3 SE +/- 0.60, N = 3 179.64 161.46 158.26 -O2 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-no-omit-framepointer noble mantic 3 6 9 12 15 SE +/- 0.07, N = 3 SE +/- 0.03, N = 3 SE +/- 0.02, N = 3 9.80 9.87 9.87 MIN: 9.12 / MAX: 9.98 MIN: 9.21 / MAX: 10 MIN: 9.09 / MAX: 9.96
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 noble mantic-no-omit-framepointer 3 6 9 12 15 SE +/- 0.07, N = 3 SE +/- 0.03, N = 3 SE +/- 0.04, N = 3 9.77 9.86 9.91 MIN: 9.17 / MAX: 10 MIN: 8.69 / MAX: 9.99 MIN: 9.19 / MAX: 10.05
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 noble mantic mantic-no-omit-framepointer 3 6 9 12 15 SE +/- 0.04, N = 3 SE +/- 0.05, N = 3 SE +/- 0.09, N = 3 9.81 9.84 10.00 MIN: 9.42 / MAX: 9.93 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 noble mantic mantic-no-omit-framepointer 3 6 9 12 15 SE +/- 0.01, N = 3 SE +/- 0.03, N = 3 SE +/- 0.02, N = 3 9.87 9.88 9.91 MIN: 8.61 / MAX: 9.96 MIN: 8.8 / MAX: 9.98 MIN: 8.69 / MAX: 10.08
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 noble mantic mantic-no-omit-framepointer 3 6 9 12 15 SE +/- 0.02, N = 3 SE +/- 0.04, N = 3 SE +/- 0.01, N = 3 9.88 9.88 9.93 MIN: 9.15 / MAX: 9.98 MIN: 9.31 / MAX: 10.01 MIN: 9.39 / MAX: 10.01
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 noble 30 60 90 120 150 SE +/- 1.22, N = 3 SE +/- 1.20, N = 3 SE +/- 1.46, N = 3 150.73 150.38 145.36 -O2 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-no-omit-framepointer mantic 9 18 27 36 45 SE +/- 0.30, N = 15 SE +/- 0.15, N = 3 36.60 37.36 MIN: 33.07 / MAX: 39.53 MIN: 35.47 / MAX: 37.85
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-no-omit-framepointer mantic 9 18 27 36 45 SE +/- 0.30, N = 15 SE +/- 0.24, N = 3 37.16 37.71 MIN: 34.12 / MAX: 39.48 MIN: 35.52 / MAX: 38.25
Numpy Benchmark OpenBenchmarking.org Score, More Is Better Numpy Benchmark mantic mantic-no-omit-framepointer noble 90 180 270 360 450 SE +/- 1.20, N = 3 SE +/- 0.90, N = 3 SE +/- 1.01, N = 3 426.28 428.61 430.83
Scikit-Learn Benchmark: Feature Expansions OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Feature Expansions noble mantic-no-omit-framepointer mantic 30 60 90 120 150 SE +/- 1.21, N = 3 SE +/- 1.22, N = 3 SE +/- 0.86, N = 3 133.14 133.09 131.28 -O2 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-no-omit-framepointer mantic noble 20 40 60 80 100 SE +/- 0.49, N = 3 SE +/- 1.06, N = 6 SE +/- 0.05, N = 3 107.53 106.32 78.88 -O2 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-no-omit-framepointer mantic noble 7 14 21 28 35 SE +/- 0.07, N = 3 SE +/- 0.03, N = 3 SE +/- 0.06, N = 3 31.06 31.01 30.62 -O2 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 noble mantic mantic-no-omit-framepointer 2 4 6 8 10 SE +/- 0.00, N = 3 SE +/- 0.00, N = 3 SE +/- 0.02, N = 3 7.31 7.31 7.32 MIN: 7.07 / MAX: 7.36 MIN: 7.16 / MAX: 7.34 MIN: 7.23 / MAX: 7.38
Scikit-Learn Benchmark: LocalOutlierFactor OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: LocalOutlierFactor mantic-no-omit-framepointer noble mantic 13 26 39 52 65 SE +/- 0.74, N = 15 SE +/- 0.02, N = 3 SE +/- 0.18, N = 3 56.75 54.29 53.46 -O2 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Hist Gradient Boosting OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Hist Gradient Boosting noble mantic-no-omit-framepointer mantic 30 60 90 120 150 SE +/- 0.17, N = 3 SE +/- 0.25, N = 3 SE +/- 0.22, N = 3 117.41 111.26 109.98 -O2 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 noble mantic-no-omit-framepointer mantic 20 40 60 80 100 SE +/- 0.13, N = 3 SE +/- 0.15, N = 3 SE +/- 0.13, N = 3 111.55 110.37 110.22 -O2 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 noble mantic-no-omit-framepointer mantic 30 60 90 120 150 SE +/- 0.52, N = 3 SE +/- 0.59, N = 3 SE +/- 0.70, N = 3 112.71 105.65 103.50 -O2 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Tree OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Tree mantic-no-omit-framepointer mantic noble 12 24 36 48 60 SE +/- 0.48, N = 15 SE +/- 0.59, N = 4 SE +/- 0.52, N = 3 52.97 48.34 47.03 -O2 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 noble mantic mantic-no-omit-framepointer 0.612 1.224 1.836 2.448 3.06 SE +/- 0.010, N = 3 SE +/- 0.010, N = 3 SE +/- 0.002, N = 3 2.720 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 noble mantic mantic-no-omit-framepointer 0.6003 1.2006 1.8009 2.4012 3.0015 SE +/- 0.006, N = 3 SE +/- 0.006, N = 3 SE +/- 0.006, N = 3 2.668 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-no-omit-framepointer mantic 9 18 27 36 45 SE +/- 0.33, N = 8 SE +/- 0.03, N = 3 37.22 37.43 MIN: 34.99 / MAX: 39.08 MIN: 35.81 / MAX: 38.02
Scikit-Learn Benchmark: Plot OMP vs. LARS OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot OMP vs. LARS mantic-no-omit-framepointer mantic noble 20 40 60 80 100 SE +/- 0.44, N = 3 SE +/- 0.08, N = 3 SE +/- 0.03, N = 3 92.58 91.50 68.17 -O2 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: MNIST Dataset OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: MNIST Dataset mantic-no-omit-framepointer mantic noble 15 30 45 60 75 SE +/- 0.47, N = 3 SE +/- 0.82, N = 4 SE +/- 0.67, N = 3 65.88 65.76 65.42 -O2 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-no-omit-framepointer mantic noble 16 32 48 64 80 SE +/- 0.16, N = 3 SE +/- 0.05, N = 3 SE +/- 0.44, N = 3 72.91 72.54 70.02 -O2 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 noble 3 6 9 12 15 SE +/- 0.03, N = 3 SE +/- 0.04, N = 3 SE +/- 0.05, N = 3 12.72 12.78 12.89 MIN: 11.99 / MAX: 12.8 MIN: 11.9 / MAX: 12.9 MIN: 12.36 / MAX: 13.05
Scikit-Learn Benchmark: Text Vectorizers OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Text Vectorizers noble mantic-no-omit-framepointer mantic 15 30 45 60 75 SE +/- 0.32, N = 3 SE +/- 0.08, N = 3 SE +/- 0.19, N = 3 66.39 63.88 60.81 -O2 1. (F9X) gfortran options: -O0
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 noble mantic mantic-no-omit-framepointer 6 12 18 24 30 SE +/- 0.06, N = 3 SE +/- 0.10, N = 3 SE +/- 0.16, N = 3 24.12 24.29 24.35 MIN: 22.33 / MAX: 24.46 MIN: 22.24 / MAX: 24.66 MIN: 23.67 / MAX: 24.87
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 noble 6 12 18 24 30 SE +/- 0.02, N = 3 SE +/- 0.08, N = 3 SE +/- 0.14, N = 3 24.13 24.28 24.30 MIN: 23.58 / MAX: 24.41 MIN: 22.31 / MAX: 24.53 MIN: 22.45 / MAX: 24.75
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 noble mantic mantic-no-omit-framepointer 6 12 18 24 30 SE +/- 0.11, N = 3 SE +/- 0.04, N = 3 SE +/- 0.15, N = 3 24.19 24.24 24.40 MIN: 22.75 / MAX: 24.73 MIN: 23.59 / MAX: 24.49 MIN: 21.6 / MAX: 24.8
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 noble mantic-no-omit-framepointer mantic 6 12 18 24 30 SE +/- 0.06, N = 3 SE +/- 0.11, N = 3 SE +/- 0.03, N = 3 24.33 24.37 24.42 MIN: 22.79 / MAX: 24.66 MIN: 23.76 / MAX: 24.81 MIN: 20.15 / MAX: 24.74
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 noble 6 12 18 24 30 SE +/- 0.05, N = 3 SE +/- 0.16, N = 3 SE +/- 0.01, N = 3 24.28 24.38 24.43 MIN: 20.22 / MAX: 24.56 MIN: 22.2 / MAX: 24.87 MIN: 22.57 / MAX: 24.72
Scikit-Learn Benchmark: Plot Ward OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot Ward mantic mantic-no-omit-framepointer noble 13 26 39 52 65 SE +/- 0.21, N = 3 SE +/- 0.22, N = 3 SE +/- 0.20, N = 3 57.82 57.55 56.13 -O2 1. (F9X) gfortran options: -O0
PyPerformance Benchmark: python_startup OpenBenchmarking.org Milliseconds, Fewer Is Better PyPerformance 1.0.0 Benchmark: python_startup noble mantic-no-omit-framepointer mantic 2 4 6 8 10 SE +/- 0.01, N = 3 SE +/- 0.01, N = 3 SE +/- 0.01, N = 3 8.76 7.64 7.61
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-no-omit-framepointer mantic 9 18 27 36 45 SE +/- 0.02, N = 3 SE +/- 0.08, N = 3 36.10 38.95 MIN: 34.25 / MAX: 38.01 MIN: 37.12 / MAX: 39.27
Scikit-Learn Benchmark: 20 Newsgroups / Logistic Regression OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: 20 Newsgroups / Logistic Regression noble mantic-no-omit-framepointer mantic 10 20 30 40 50 SE +/- 0.12, N = 3 SE +/- 0.24, N = 3 SE +/- 0.19, N = 3 41.91 41.73 41.52 -O2 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-no-omit-framepointer mantic noble 9 18 27 36 45 SE +/- 0.36, N = 3 SE +/- 0.21, N = 3 SE +/- 0.43, N = 3 37.89 37.24 37.11 -O2 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 noble mantic mantic-no-omit-framepointer 0.3254 0.6508 0.9762 1.3016 1.627 SE +/- 0.006, N = 3 SE +/- 0.004, N = 3 SE +/- 0.001, N = 3 1.446 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 noble mantic-no-omit-framepointer mantic 0.3231 0.6462 0.9693 1.2924 1.6155 SE +/- 0.003, N = 3 SE +/- 0.004, N = 3 SE +/- 0.003, N = 3 1.436 1.405 1.402
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 noble mantic mantic-no-omit-framepointer 8 16 24 32 40 SE +/- 0.17, N = 3 SE +/- 0.11, N = 3 SE +/- 0.16, N = 3 32.34 32.36 32.54 MIN: 28.9 / MAX: 32.83 MIN: 31.89 / MAX: 32.7 MIN: 31.64 / MAX: 32.94
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-no-omit-framepointer mantic 16 32 48 64 80 SE +/- 0.20, N = 3 SE +/- 0.96, N = 3 72.24 73.01 MIN: 68.36 / MAX: 73.14 MIN: 68.06 / MAX: 75.3
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-no-omit-framepointer mantic 16 32 48 64 80 SE +/- 0.74, N = 3 SE +/- 0.96, N = 3 73.36 74.15 MIN: 68.19 / MAX: 74.63 MIN: 68.27 / MAX: 75.61
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-no-omit-framepointer mantic 9 18 27 36 45 SE +/- 0.26, N = 3 SE +/- 0.47, N = 3 37.29 39.35 MIN: 35.83 / MAX: 39.17 MIN: 36.65 / MAX: 40.42
PyPerformance Benchmark: raytrace OpenBenchmarking.org Milliseconds, Fewer Is Better PyPerformance 1.0.0 Benchmark: raytrace mantic-no-omit-framepointer mantic 60 120 180 240 300 SE +/- 0.33, N = 3 SE +/- 0.33, N = 3 274 262
PyPerformance Benchmark: go OpenBenchmarking.org Milliseconds, Fewer Is Better PyPerformance 1.0.0 Benchmark: go mantic-no-omit-framepointer mantic noble 30 60 90 120 150 SE +/- 0.33, N = 3 SE +/- 0.00, N = 3 SE +/- 0.00, N = 3 131 129 121
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 noble mantic-no-omit-framepointer 0.142 0.284 0.426 0.568 0.71 SE +/- 0.002, N = 3 SE +/- 0.004, N = 3 SE +/- 0.007, N = 3 0.631 0.630 0.622
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 noble mantic mantic-no-omit-framepointer 0.142 0.284 0.426 0.568 0.71 SE +/- 0.006, N = 3 SE +/- 0.001, N = 3 SE +/- 0.000, N = 3 0.631 0.619 0.618
PyPerformance Benchmark: 2to3 OpenBenchmarking.org Milliseconds, Fewer Is Better PyPerformance 1.0.0 Benchmark: 2to3 mantic-no-omit-framepointer mantic 50 100 150 200 250 SE +/- 0.33, N = 3 SE +/- 0.00, N = 3 224 221
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 noble mantic-no-omit-framepointer mantic 5 10 15 20 25 SE +/- 0.10, N = 3 SE +/- 0.12, N = 3 SE +/- 0.06, N = 3 19.93 18.87 18.58 -O2 1. (F9X) gfortran options: -O0
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 noble mantic-no-omit-framepointer mantic 5 10 15 20 25 SE +/- 0.03, N = 3 SE +/- 0.03, N = 3 SE +/- 0.06, N = 3 22.8 20.8 19.5
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 noble mantic-no-omit-framepointer mantic 0.0299 0.0598 0.0897 0.1196 0.1495 SE +/- 0.002, N = 3 SE +/- 0.000, N = 3 SE +/- 0.001, N = 3 0.133 0.132 0.131
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 noble mantic mantic-no-omit-framepointer 0.0306 0.0612 0.0918 0.1224 0.153 SE +/- 0.001, N = 3 SE +/- 0.000, N = 3 SE +/- 0.001, N = 3 0.136 0.131 0.128
PyPerformance Benchmark: pathlib OpenBenchmarking.org Milliseconds, Fewer Is Better PyPerformance 1.0.0 Benchmark: pathlib mantic-no-omit-framepointer mantic 5 10 15 20 25 SE +/- 0.00, N = 3 SE +/- 0.00, N = 3 20.2 19.7
PyBench Total For Average Test Times OpenBenchmarking.org Milliseconds, Fewer Is Better PyBench 2018-02-16 Total For Average Test Times noble mantic-no-omit-framepointer mantic 200 400 600 800 1000 SE +/- 8.70, N = 4 SE +/- 1.20, N = 3 SE +/- 1.00, N = 3 839 790 774
PyPerformance Benchmark: pickle_pure_python OpenBenchmarking.org Milliseconds, Fewer Is Better PyPerformance 1.0.0 Benchmark: pickle_pure_python mantic-no-omit-framepointer mantic 60 120 180 240 300 SE +/- 0.58, N = 3 SE +/- 0.33, N = 3 263 259
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-no-omit-framepointer mantic 16 32 48 64 80 SE +/- 0.96, N = 3 SE +/- 0.56, N = 3 72.27 73.91 MIN: 68.86 / MAX: 76.62 MIN: 68.9 / MAX: 75.9
PyPerformance Benchmark: nbody OpenBenchmarking.org Milliseconds, Fewer Is Better PyPerformance 1.0.0 Benchmark: nbody mantic-no-omit-framepointer mantic 20 40 60 80 100 SE +/- 0.07, N = 3 SE +/- 0.06, N = 3 77.1 76.2
PyPerformance Benchmark: django_template OpenBenchmarking.org Milliseconds, Fewer Is Better PyPerformance 1.0.0 Benchmark: django_template mantic-no-omit-framepointer mantic 7 14 21 28 35 SE +/- 0.06, N = 3 SE +/- 0.03, N = 3 29.5 28.5
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: 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
PyPerformance Benchmark: regex_compile OpenBenchmarking.org Milliseconds, Fewer Is Better PyPerformance 1.0.0 Benchmark: regex_compile mantic-no-omit-framepointer mantic 30 60 90 120 150 SE +/- 0.33, N = 3 SE +/- 0.00, N = 3 120 116
PyPerformance Benchmark: crypto_pyaes OpenBenchmarking.org Milliseconds, Fewer Is Better PyPerformance 1.0.0 Benchmark: crypto_pyaes mantic-no-omit-framepointer mantic 15 30 45 60 75 SE +/- 0.00, N = 3 SE +/- 0.06, N = 3 66.6 65.1
PyPerformance Benchmark: chaos OpenBenchmarking.org Milliseconds, Fewer Is Better PyPerformance 1.0.0 Benchmark: chaos mantic-no-omit-framepointer mantic 14 28 42 56 70 SE +/- 0.20, N = 3 SE +/- 0.03, N = 3 63.6 62.8
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-no-omit-framepointer mantic 40 80 120 160 200 SE +/- 0.96, N = 3 SE +/- 0.25, N = 3 200.17 200.30 MIN: 183.43 / MAX: 203.55 MIN: 182.88 / MAX: 202.36
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-no-omit-framepointer mantic 40 80 120 160 200 SE +/- 0.33, N = 3 SE +/- 1.69, N = 3 201.14 203.18 MIN: 183.61 / MAX: 202.73 MIN: 183.76 / MAX: 207.98
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 noble 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 SE +/- 0.000, N = 3 0.009 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 noble mantic-no-omit-framepointer 0.002 0.004 0.006 0.008 0.01 SE +/- 0.000, N = 3 SE +/- 0.000, N = 3 SE +/- 0.000, N = 3 0.009 0.008 0.008
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 noble mantic mantic-no-omit-framepointer 0.0007 0.0014 0.0021 0.0028 0.0035 SE +/- 0.000, N = 12 SE +/- 0.000, N = 3 SE +/- 0.000, N = 15 0.003 0.003 0.002
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 noble 0.0592 0.1184 0.1776 0.2368 0.296 SE +/- 0.002, N = 3 SE +/- 0.000, N = 3 SE +/- 0.002, N = 3 0.263 0.262 0.261
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 noble mantic-no-omit-framepointer 0.0592 0.1184 0.1776 0.2368 0.296 SE +/- 0.002, N = 3 SE +/- 0.002, N = 3 SE +/- 0.001, N = 3 0.263 0.262 0.260
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 noble mantic-no-omit-framepointer 0.014 0.028 0.042 0.056 0.07 SE +/- 0.001, N = 3 SE +/- 0.000, N = 3 SE +/- 0.000, N = 3 0.062 0.061 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 noble mantic-no-omit-framepointer 0.0137 0.0274 0.0411 0.0548 0.0685 SE +/- 0.001, N = 3 SE +/- 0.000, N = 3 SE +/- 0.000, N = 3 0.061 0.060 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 noble mantic-no-omit-framepointer mantic 0.0077 0.0154 0.0231 0.0308 0.0385 SE +/- 0.000, N = 3 SE +/- 0.000, N = 3 SE +/- 0.000, N = 3 0.034 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 noble mantic-no-omit-framepointer mantic 0.0074 0.0148 0.0222 0.0296 0.037 SE +/- 0.000, N = 3 SE +/- 0.000, N = 3 SE +/- 0.000, N = 3 0.033 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 noble mantic-no-omit-framepointer mantic 0.0036 0.0072 0.0108 0.0144 0.018 SE +/- 0.000, N = 15 SE +/- 0.000, N = 3 SE +/- 0.000, N = 3 0.016 0.015 0.015
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 noble mantic-no-omit-framepointer mantic 0.0034 0.0068 0.0102 0.0136 0.017 SE +/- 0.000, N = 7 SE +/- 0.000, N = 3 SE +/- 0.000, N = 3 0.015 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 noble mantic-no-omit-framepointer mantic 0.0007 0.0014 0.0021 0.0028 0.0035 SE +/- 0.000, N = 3 SE +/- 0.000, N = 3 SE +/- 0.000, N = 3 0.003 0.003 0.003
Phoronix Test Suite v10.8.4