AMD Ryzen 9 5900HX testing with a ASUS G513QY v1.0 (G513QY.318 BIOS) and ASUS AMD Cezanne 512MB on Ubuntu 22.10 via the Phoronix Test Suite.
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phoronix-test-suite benchmark 2305113-NE-5900HXSCI86 5900hx scikit learn - Phoronix Test Suite 5900hx scikit learn AMD Ryzen 9 5900HX testing with a ASUS G513QY v1.0 (G513QY.318 BIOS) and ASUS AMD Cezanne 512MB on Ubuntu 22.10 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2305113-NE-5900HXSCI86&rdt&gru .
5900hx scikit learn Processor Motherboard Chipset Memory Disk Graphics Audio Monitor Network OS Kernel Desktop Display Server OpenGL Vulkan Compiler File-System Screen Resolution a b c AMD Ryzen 9 5900HX @ 3.30GHz (8 Cores / 16 Threads) ASUS G513QY v1.0 (G513QY.318 BIOS) AMD Renoir/Cezanne 16GB 512GB SAMSUNG MZVLQ512HBLU-00B00 ASUS AMD Cezanne 512MB (2500/1000MHz) AMD Navi 21/23 LQ156M1JW25 Realtek RTL8111/8168/8411 + MEDIATEK MT7921 802.11ax PCI Ubuntu 22.10 5.19.0-41-generic (x86_64) GNOME Shell 43.0 X Server 1.21.1.4 + Wayland 4.6 Mesa 22.2.5 (LLVM 15.0.2 DRM 3.47) 1.3.224 GCC 12.2.0 ext4 1920x1080 OpenBenchmarking.org Kernel Details - Transparent Huge Pages: madvise Compiler Details - --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-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-12-U8K4Qv/gcc-12-12.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-12-U8K4Qv/gcc-12-12.2.0/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) - Platform Profile: balanced - CPU Microcode: 0xa50000c - ACPI Profile: balanced Python Details - Python 3.10.7 Security Details - itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Not affected + 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 IBRS_FW STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected
5900hx scikit learn scikit-learn: GLM scikit-learn: SAGA scikit-learn: Tree scikit-learn: Lasso scikit-learn: Sparsify scikit-learn: Plot Ward scikit-learn: MNIST Dataset scikit-learn: Plot Neighbors scikit-learn: SGD Regression scikit-learn: Plot Lasso Path scikit-learn: Text Vectorizers scikit-learn: Plot Hierarchical scikit-learn: Plot OMP vs. LARS scikit-learn: Feature Expansions scikit-learn: LocalOutlierFactor scikit-learn: TSNE MNIST Dataset scikit-learn: Plot Incremental PCA scikit-learn: Hist Gradient Boosting scikit-learn: Sample Without Replacement scikit-learn: Covertype Dataset Benchmark scikit-learn: Hist Gradient Boosting Adult scikit-learn: Hist Gradient Boosting Threading scikit-learn: Plot Singular Value Decomposition scikit-learn: Hist Gradient Boosting Higgs Boson scikit-learn: 20 Newsgroups / Logistic Regression scikit-learn: Plot Polynomial Kernel Approximation scikit-learn: Hist Gradient Boosting Categorical Only scikit-learn: Kernel PCA Solvers / Time vs. N Samples scikit-learn: Kernel PCA Solvers / Time vs. N Components scikit-learn: Sparse Rand Projections / 100 Iterations a b c 406.904 732.860 40.724 430.672 114.745 54.355 59.282 138.946 101.875 196.189 58.809 180.443 104.786 134.561 59.868 236.977 34.627 92.236 119.672 378.753 71.041 183.486 135.597 67.847 42.017 149.925 15.724 193.448 169.270 760.377 407.075 733.048 41.491 430.417 114.767 54.557 59.969 138.218 101.663 194.416 58.185 179.059 104.643 133.854 59.985 235.418 34.864 93.010 122.696 376.287 71.041 183.553 136.273 67.709 42.225 150.684 15.716 193.399 170.439 767.671 406.724 735.468 41.617 431.810 114.830 54.368 59.800 140.708 102.002 194.465 58.557 180.307 104.965 134.335 59.969 237.618 34.772 93.366 120.901 375.258 70.758 183.756 136.193 67.468 42.095 150.152 15.935 195.460 172.701 762.752 OpenBenchmarking.org
Scikit-Learn Benchmark: GLM OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: GLM a b c 90 180 270 360 450 SE +/- 2.13, N = 3 SE +/- 0.79, N = 3 SE +/- 0.54, N = 3 406.90 407.08 406.72 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: SAGA OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: SAGA a b c 160 320 480 640 800 SE +/- 6.30, N = 3 SE +/- 1.92, N = 3 SE +/- 0.31, N = 3 732.86 733.05 735.47 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: Tree OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Tree a b c 9 18 27 36 45 SE +/- 0.44, N = 5 SE +/- 0.36, N = 15 SE +/- 0.41, N = 5 40.72 41.49 41.62 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: Lasso OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Lasso a b c 90 180 270 360 450 SE +/- 0.52, N = 3 SE +/- 0.98, N = 3 SE +/- 0.43, N = 3 430.67 430.42 431.81 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: Sparsify OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Sparsify a b c 30 60 90 120 150 SE +/- 0.50, N = 3 SE +/- 0.20, N = 3 SE +/- 0.14, N = 3 114.75 114.77 114.83 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: Plot Ward OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot Ward a b c 12 24 36 48 60 SE +/- 0.29, N = 3 SE +/- 0.18, N = 3 SE +/- 0.04, N = 3 54.36 54.56 54.37 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: MNIST Dataset OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: MNIST Dataset a b c 13 26 39 52 65 SE +/- 0.07, N = 3 SE +/- 0.13, N = 3 SE +/- 0.08, N = 3 59.28 59.97 59.80 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: Plot Neighbors OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot Neighbors a b c 30 60 90 120 150 SE +/- 1.26, N = 3 SE +/- 1.49, N = 3 SE +/- 0.77, N = 3 138.95 138.22 140.71 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: SGD Regression OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: SGD Regression a b c 20 40 60 80 100 SE +/- 0.26, N = 3 SE +/- 0.16, N = 3 SE +/- 0.25, N = 3 101.88 101.66 102.00 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: Plot Lasso Path OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot Lasso Path a b c 40 80 120 160 200 SE +/- 0.31, N = 3 SE +/- 0.18, N = 3 SE +/- 0.54, N = 3 196.19 194.42 194.47 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: Text Vectorizers OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Text Vectorizers a b c 13 26 39 52 65 SE +/- 0.02, N = 3 SE +/- 0.07, N = 3 SE +/- 0.14, N = 3 58.81 58.19 58.56 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: Plot Hierarchical OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot Hierarchical a b c 40 80 120 160 200 SE +/- 0.21, N = 3 SE +/- 0.37, N = 3 SE +/- 0.84, N = 3 180.44 179.06 180.31 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: Plot OMP vs. LARS OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot OMP vs. LARS a b c 20 40 60 80 100 SE +/- 0.21, N = 3 SE +/- 0.08, N = 3 SE +/- 0.24, N = 3 104.79 104.64 104.97 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: Feature Expansions OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Feature Expansions a b c 30 60 90 120 150 SE +/- 0.35, N = 3 SE +/- 0.21, N = 3 SE +/- 0.04, N = 3 134.56 133.85 134.34 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: LocalOutlierFactor OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: LocalOutlierFactor a b c 13 26 39 52 65 SE +/- 0.64, N = 3 SE +/- 0.58, N = 3 SE +/- 0.24, N = 3 59.87 59.99 59.97 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: TSNE MNIST Dataset OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: TSNE MNIST Dataset a b c 50 100 150 200 250 SE +/- 0.39, N = 3 SE +/- 0.31, N = 3 SE +/- 0.31, N = 3 236.98 235.42 237.62 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: Plot Incremental PCA OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot Incremental PCA a b c 8 16 24 32 40 SE +/- 0.14, N = 3 SE +/- 0.50, N = 3 SE +/- 0.34, N = 6 34.63 34.86 34.77 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: Hist Gradient Boosting OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Hist Gradient Boosting a b c 20 40 60 80 100 SE +/- 0.17, N = 3 SE +/- 0.95, N = 3 SE +/- 0.74, N = 3 92.24 93.01 93.37 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: Sample Without Replacement OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Sample Without Replacement a b c 30 60 90 120 150 SE +/- 0.18, N = 3 SE +/- 0.79, N = 3 SE +/- 1.46, N = 3 119.67 122.70 120.90 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: Covertype Dataset Benchmark OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Covertype Dataset Benchmark a b c 80 160 240 320 400 SE +/- 1.06, N = 3 SE +/- 0.57, N = 3 SE +/- 0.82, N = 3 378.75 376.29 375.26 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: Hist Gradient Boosting Adult OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Hist Gradient Boosting Adult a b c 16 32 48 64 80 SE +/- 0.07, N = 3 SE +/- 0.30, N = 3 SE +/- 0.08, N = 3 71.04 71.04 70.76 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: Hist Gradient Boosting Threading OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Hist Gradient Boosting Threading a b c 40 80 120 160 200 SE +/- 0.15, N = 3 SE +/- 0.23, N = 3 SE +/- 0.21, N = 3 183.49 183.55 183.76 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: Plot Singular Value Decomposition OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot Singular Value Decomposition a b c 30 60 90 120 150 SE +/- 0.16, N = 3 SE +/- 0.11, N = 3 SE +/- 0.42, N = 3 135.60 136.27 136.19 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: Hist Gradient Boosting Higgs Boson OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Hist Gradient Boosting Higgs Boson a b c 15 30 45 60 75 SE +/- 0.07, N = 3 SE +/- 0.18, N = 3 SE +/- 0.02, N = 3 67.85 67.71 67.47 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: 20 Newsgroups / Logistic Regression OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: 20 Newsgroups / Logistic Regression a b c 10 20 30 40 50 SE +/- 0.14, N = 3 SE +/- 0.17, N = 3 SE +/- 0.06, N = 3 42.02 42.23 42.10 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Scikit-Learn Benchmark: Plot Polynomial Kernel Approximation OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot Polynomial Kernel Approximation a b c 30 60 90 120 150 SE +/- 0.18, N = 3 SE +/- 0.38, N = 3 SE +/- 0.07, N = 3 149.93 150.68 150.15 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
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 a b c 4 8 12 16 20 SE +/- 0.08, N = 3 SE +/- 0.08, N = 3 SE +/- 0.18, N = 3 15.72 15.72 15.94 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
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 a b c 40 80 120 160 200 SE +/- 1.38, N = 3 SE +/- 0.46, N = 3 SE +/- 0.78, N = 3 193.45 193.40 195.46 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
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 a b c 40 80 120 160 200 SE +/- 2.86, N = 12 SE +/- 2.38, N = 12 SE +/- 2.77, N = 12 169.27 170.44 172.70 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
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 a b c 170 340 510 680 850 SE +/- 2.60, N = 3 SE +/- 1.20, N = 3 SE +/- 3.45, N = 3 760.38 767.67 762.75 1. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Phoronix Test Suite v10.8.4