phoronix-machine-learning.txt AMD Ryzen Threadripper 7960X 24-Cores testing with a Gigabyte TRX50 AERO D (FA BIOS) and Sapphire AMD Radeon RX 7900 XTX 24GB on Ubuntu 24.04 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2411137-NE-PHORONIXM28&grs .
phoronix-machine-learning.txt Processor Motherboard Chipset Memory Disk Graphics Audio Monitor Network OS Kernel Desktop Display Server OpenGL OpenCL Compiler File-System Screen Resolution phoronix-ml.txt AMD Ryzen Threadripper 7960X 24-Cores @ 7.79GHz (24 Cores / 48 Threads) Gigabyte TRX50 AERO D (FA BIOS) AMD Device 14a4 4 x 32GB DDR5-5200MT/s Micron MTC20F1045S1RC56BG1 1000GB GIGABYTE AG512K1TB Sapphire AMD Radeon RX 7900 XTX 24GB AMD Device 14cc HP E273 Aquantia AQC113C NBase-T/IEEE + Realtek RTL8125 2.5GbE + Qualcomm WCN785x Wi-Fi 7 Ubuntu 24.04 6.8.0-48-generic (x86_64) GNOME Shell 46.0 X Server + Wayland 4.6 Mesa 24.2.0-devel (LLVM 18.1.7 DRM 3.58) OpenCL 2.1 AMD-APP (3625.0) GCC 13.2.0 ext4 1920x1080 OpenBenchmarking.org - Transparent Huge Pages: madvise - --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 - Scaling Governor: amd-pstate-epp powersave (EPP: balance_performance) - CPU Microcode: 0xa108105 - BAR1 / Visible vRAM Size: 24560 MB - Python 3.12.3 - 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: Not affected + 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 Enhanced / Automatic IBRS; IBPB: conditional; STIBP: always-on; RSB filling; PBRSB-eIBRS: Not affected; BHI: Not affected + srbds: Not affected + tsx_async_abort: Not affected
phoronix-machine-learning.txt shoc: OpenCL - FFT SP shoc: OpenCL - Reduction shoc: OpenCL - Triad shoc: OpenCL - GEMM SGEMM_N shoc: OpenCL - MD5 Hash shoc: OpenCL - S3D whisper-cpp: ggml-medium.en - 2016 State of the Union whisper-cpp: ggml-small.en - 2016 State of the Union whisper-cpp: ggml-base.en - 2016 State of the Union scikit-learn: Sparse Rand Projections / 100 Iterations scikit-learn: Kernel PCA Solvers / Time vs. N Components scikit-learn: Kernel PCA Solvers / Time vs. N Samples scikit-learn: Hist Gradient Boosting Categorical Only scikit-learn: Plot Polynomial Kernel Approximation scikit-learn: 20 Newsgroups / Logistic Regression scikit-learn: Hist Gradient Boosting Higgs Boson scikit-learn: Hist Gradient Boosting Threading scikit-learn: Isotonic / Perturbed Logarithm scikit-learn: Hist Gradient Boosting Adult scikit-learn: Covertype Dataset Benchmark scikit-learn: Sample Without Replacement scikit-learn: Isotonic / Pathological scikit-learn: Hist Gradient Boosting scikit-learn: Plot Incremental PCA scikit-learn: Isotonic / Logistic scikit-learn: TSNE MNIST Dataset scikit-learn: LocalOutlierFactor scikit-learn: Feature Expansions scikit-learn: Plot OMP vs. LARS scikit-learn: Plot Hierarchical scikit-learn: Text Vectorizers scikit-learn: Isolation Forest scikit-learn: SGDOneClassSVM scikit-learn: SGD Regression scikit-learn: Plot Neighbors scikit-learn: MNIST Dataset scikit-learn: Plot Ward scikit-learn: Sparsify scikit-learn: Lasso scikit-learn: Tree scikit-learn: SAGA scikit-learn: GLM onnx: ResNet101_DUC_HDC-12 - CPU - Standard onnx: ResNet101_DUC_HDC-12 - CPU - Parallel onnx: super-resolution-10 - CPU - Standard onnx: super-resolution-10 - CPU - Parallel onnx: ResNet50 v1-12-int8 - CPU - Standard onnx: ResNet50 v1-12-int8 - CPU - Parallel onnx: fcn-resnet101-11 - CPU - Standard onnx: CaffeNet 12-int8 - CPU - Standard onnx: CaffeNet 12-int8 - CPU - Parallel onnx: T5 Encoder - CPU - Standard onnx: T5 Encoder - CPU - Parallel onnx: ZFNet-512 - CPU - Standard onnx: ZFNet-512 - CPU - Parallel onnx: yolov4 - CPU - Standard onnx: yolov4 - CPU - Parallel openvino: Age Gender Recognition Retail 0013 FP16-INT8 - CPU openvino: Age Gender Recognition Retail 0013 FP16-INT8 - CPU openvino: Handwritten English Recognition FP16-INT8 - CPU openvino: Handwritten English Recognition FP16-INT8 - CPU openvino: Age Gender Recognition Retail 0013 FP16 - CPU openvino: Age Gender Recognition Retail 0013 FP16 - CPU openvino: Person Re-Identification Retail FP16 - CPU openvino: Person Re-Identification Retail FP16 - CPU openvino: Handwritten English Recognition FP16 - CPU openvino: Handwritten English Recognition FP16 - CPU openvino: Noise Suppression Poconet-Like FP16 - CPU openvino: Person Vehicle Bike Detection FP16 - CPU openvino: Person Vehicle Bike Detection FP16 - CPU openvino: Weld Porosity Detection FP16-INT8 - CPU openvino: Weld Porosity Detection FP16-INT8 - CPU openvino: Machine Translation EN To DE FP16 - CPU openvino: Road Segmentation ADAS FP16-INT8 - CPU openvino: Road Segmentation ADAS FP16-INT8 - CPU openvino: Face Detection Retail FP16-INT8 - CPU openvino: Face Detection Retail FP16-INT8 - CPU openvino: Weld Porosity Detection FP16 - CPU openvino: Weld Porosity Detection FP16 - CPU openvino: Vehicle Detection FP16-INT8 - CPU openvino: Vehicle Detection FP16-INT8 - CPU openvino: Face Detection Retail FP16 - CPU openvino: Face Detection Retail FP16 - CPU openvino: Face Detection FP16-INT8 - CPU openvino: Face Detection FP16-INT8 - CPU openvino: Vehicle Detection FP16 - CPU openvino: Vehicle Detection FP16 - CPU openvino: Person Detection FP32 - CPU openvino: Person Detection FP32 - CPU openvino: Face Detection FP16 - CPU openvino: Face Detection FP16 - CPU xnnpack: QS8MobileNetV2 xnnpack: FP16MobileNetV3Small xnnpack: FP16MobileNetV3Large xnnpack: FP16MobileNetV2 xnnpack: FP16MobileNetV1 xnnpack: FP32MobileNetV3Small xnnpack: FP32MobileNetV3Large xnnpack: FP32MobileNetV2 xnnpack: FP32MobileNetV1 ncnn: Vulkan GPU - vision_transformer ncnn: Vulkan GPU - regnety_400m ncnn: Vulkan GPU - yolov4-tiny ncnn: Vulkan GPUv2-yolov3v2-yolov3 - mobilenetv2-yolov3 ncnn: Vulkan GPU - alexnet ncnn: Vulkan GPU - resnet18 ncnn: Vulkan GPU - vgg16 ncnn: Vulkan GPU - googlenet ncnn: Vulkan GPU - blazeface ncnn: Vulkan GPU - mnasnet ncnn: Vulkan GPU - shufflenet-v2 ncnn: Vulkan GPU-v3-v3 - mobilenet-v3 ncnn: Vulkan GPU-v2-v2 - mobilenet-v2 ncnn: Vulkan GPU - mobilenet ncnn: CPU - vision_transformer ncnn: CPU - regnety_400m ncnn: CPU - squeezenet_ssd ncnn: CPU - yolov4-tiny ncnn: CPUv2-yolov3v2-yolov3 - mobilenetv2-yolov3 ncnn: CPU - resnet50 ncnn: CPU - alexnet ncnn: CPU - resnet18 ncnn: CPU - vgg16 ncnn: CPU - googlenet ncnn: CPU - blazeface ncnn: CPU - efficientnet-b0 ncnn: CPU - shufflenet-v2 ncnn: CPU-v3-v3 - mobilenet-v3 ncnn: CPU-v2-v2 - mobilenet-v2 ncnn: CPU - mobilenet mnn: inception-v3 mnn: mobilenet-v1-1.0 mnn: MobileNetV2_224 mnn: SqueezeNetV1.0 mnn: resnet-v2-50 mnn: squeezenetv1.1 mnn: mobilenetV3 mnn: nasnet tensorflow: GPU - 512 - ResNet-50 tensorflow: GPU - 512 - GoogLeNet tensorflow: GPU - 256 - ResNet-50 tensorflow: GPU - 256 - GoogLeNet tensorflow: CPU - 512 - ResNet-50 tensorflow: CPU - 512 - GoogLeNet tensorflow: CPU - 256 - ResNet-50 tensorflow: CPU - 256 - GoogLeNet tensorflow: GPU - 64 - ResNet-50 tensorflow: GPU - 64 - GoogLeNet tensorflow: GPU - 32 - ResNet-50 tensorflow: GPU - 32 - GoogLeNet tensorflow: GPU - 16 - ResNet-50 tensorflow: GPU - 16 - GoogLeNet tensorflow: CPU - 64 - ResNet-50 tensorflow: CPU - 64 - GoogLeNet tensorflow: CPU - 32 - ResNet-50 tensorflow: CPU - 32 - GoogLeNet tensorflow: CPU - 16 - ResNet-50 tensorflow: CPU - 16 - GoogLeNet tensorflow: GPU - 512 - AlexNet tensorflow: GPU - 256 - AlexNet tensorflow: GPU - 1 - ResNet-50 tensorflow: GPU - 1 - GoogLeNet tensorflow: CPU - 512 - AlexNet tensorflow: CPU - 256 - AlexNet tensorflow: CPU - 1 - ResNet-50 tensorflow: CPU - 1 - GoogLeNet tensorflow: GPU - 64 - AlexNet tensorflow: GPU - 512 - VGG-16 tensorflow: GPU - 32 - AlexNet tensorflow: GPU - 256 - VGG-16 tensorflow: GPU - 16 - AlexNet tensorflow: CPU - 64 - AlexNet tensorflow: CPU - 512 - VGG-16 tensorflow: CPU - 32 - AlexNet tensorflow: CPU - 256 - VGG-16 tensorflow: CPU - 16 - AlexNet tensorflow: GPU - 64 - VGG-16 tensorflow: GPU - 32 - VGG-16 tensorflow: GPU - 16 - VGG-16 tensorflow: GPU - 1 - AlexNet tensorflow: CPU - 64 - VGG-16 tensorflow: CPU - 32 - VGG-16 tensorflow: CPU - 16 - VGG-16 tensorflow: CPU - 1 - AlexNet tensorflow: GPU - 1 - VGG-16 tensorflow: CPU - 1 - VGG-16 pytorch: CPU - 512 - Efficientnet_v2_l pytorch: CPU - 256 - Efficientnet_v2_l pytorch: CPU - 64 - Efficientnet_v2_l pytorch: CPU - 32 - Efficientnet_v2_l pytorch: CPU - 16 - Efficientnet_v2_l pytorch: CPU - 1 - Efficientnet_v2_l pytorch: CPU - 512 - ResNet-152 pytorch: CPU - 256 - ResNet-152 pytorch: CPU - 64 - ResNet-152 pytorch: CPU - 512 - ResNet-50 pytorch: CPU - 32 - ResNet-152 pytorch: CPU - 256 - ResNet-50 pytorch: CPU - 16 - ResNet-152 pytorch: CPU - 64 - ResNet-50 pytorch: CPU - 32 - ResNet-50 pytorch: CPU - 16 - ResNet-50 pytorch: CPU - 1 - ResNet-152 pytorch: CPU - 1 - ResNet-50 tensorflow-lite: Inception ResNet V2 tensorflow-lite: Mobilenet Quant tensorflow-lite: Mobilenet Float tensorflow-lite: NASNet Mobile tensorflow-lite: SqueezeNet rnnoise: 26 Minute Long Talking Sample rbenchmark: deepspeech: CPU numpy: onednn: Recurrent Neural Network Inference - CPU onednn: Recurrent Neural Network Training - CPU onednn: Deconvolution Batch shapes_3d - CPU onednn: Deconvolution Batch shapes_1d - CPU onednn: Convolution Batch Shapes Auto - CPU onednn: IP Shapes 3D - CPU onednn: IP Shapes 1D - CPU shoc: OpenCL - Texture Read Bandwidth shoc: OpenCL - Bus Speed Readback shoc: OpenCL - Bus Speed Download shoc: OpenCL - Max SP Flops opencv: DNN - Deep Neural Network scikit-learn: Plot Parallel Pairwise onnx: ResNet101_DUC_HDC-12 - CPU - Standard onnx: ResNet101_DUC_HDC-12 - CPU - Parallel onnx: super-resolution-10 - CPU - Standard onnx: super-resolution-10 - CPU - Parallel onnx: ResNet50 v1-12-int8 - CPU - Standard onnx: ResNet50 v1-12-int8 - CPU - Parallel onnx: fcn-resnet101-11 - CPU - Standard onnx: fcn-resnet101-11 - CPU - Parallel onnx: fcn-resnet101-11 - CPU - Parallel onnx: CaffeNet 12-int8 - CPU - Standard onnx: CaffeNet 12-int8 - CPU - Parallel onnx: T5 Encoder - CPU - Standard onnx: T5 Encoder - CPU - Parallel onnx: ZFNet-512 - CPU - Standard onnx: ZFNet-512 - CPU - Parallel onnx: yolov4 - CPU - Standard onnx: yolov4 - CPU - Parallel openvino: Noise Suppression Poconet-Like FP16 - CPU openvino: Machine Translation EN To DE FP16 - CPU openvino: Road Segmentation ADAS FP16 - CPU openvino: Road Segmentation ADAS FP16 - CPU openvino: Person Detection FP16 - CPU openvino: Person Detection FP16 - CPU ncnn: Vulkan GPU - FastestDet ncnn: Vulkan GPU - squeezenet_ssd ncnn: Vulkan GPU - resnet50 ncnn: Vulkan GPU - efficientnet-b0 ncnn: CPU - FastestDet ncnn: CPU - mnasnet tensorflow-lite: Inception V4 lczero: BLAS phoronix-ml.txt 752.837 42.9449 13.8158 7615.35 46.5084 289.543 579.17077 218.18523 92.75261 504.829 31.037 61.611 30.188 104.700 10.450 65.736 52.729 1528.966 153.338 320.394 90.640 3843.258 166.776 31.209 1406.452 247.556 21.616 100.353 41.476 141.463 45.340 176.287 233.407 64.368 114.839 52.736 42.104 108.455 308.225 46.970 669.118 168.333 2.21086 1.29718 97.3177 123.736 328.293 108.875 4.11676 639.905 203.059 172.037 272.184 109.863 57.8612 9.65442 5.39684 0.3 67537.77 21.58 1108.65 0.43 48433.92 4.72 2523.59 23.12 1035.29 11.53 6.18 1930.08 6.31 3742.77 63.99 17.62 679.66 3.58 6458.38 12.25 1947.67 5.51 2160.57 2.76 4273.09 320.42 37.36 11.12 1077.87 100.46 119.41 607.48 19.69 1398 1464 2128 1495 1144 1503 2465 1873 1233 41.05 18.63 23.64 13.79 5.28 7.86 25.13 16.01 3.14 5.99 8.06 6.49 6.31 13.79 40.59 18.58 14.34 24.14 13.85 13.33 5.56 8.02 25.71 16.42 3.11 8.28 8.15 6.45 6.30 13.85 36.452 3.784 3.268 6.429 18.534 4.327 2.536 15.297 9.34 28.73 9.36 29.03 58.50 185.25 59.70 227.06 9.24 28.53 9.14 27.91 8.94 26.91 70.89 225.16 67.94 218.07 62.29 198.11 49.28 48.92 6.69 21.03 643.44 627.50 18.38 60.92 47.85 2.55 46.14 2.55 42.43 516.18 30.43 409.56 30.16 288.71 2.55 2.54 2.51 15.38 29.05 28.32 27.34 30.66 2.20 9.70 9.94 10.11 9.98 9.85 9.90 14.18 17.99 17.92 17.64 45.75 17.78 46.06 17.97 46.69 46.42 45.59 23.19 60.17 33356.3 2501.21 1381.25 33662.5 1836.28 7.852 0.1252 46.23475 715.50 736.400 1261.40 1.85206 3.77567 2.36317 1.39591 1.13657 1003.321 26.2525 24.9893 93757.3 33080 167.997 452.314 770.906 10.2754 8.08149 3.04551 9.18292 242.908 834.572 1.20339 1.56253 4.92768 5.81489 3.67307 9.10376 17.2875 103.579 185.360 2052.02 187.88 25.83 465.86 93.29 129.45 9.82 16.04 14.65 8.68 9.80 6.11 20372.6 184 OpenBenchmarking.org
SHOC Scalable HeterOgeneous Computing Target: OpenCL - Benchmark: FFT SP OpenBenchmarking.org GFLOPS, More Is Better SHOC Scalable HeterOgeneous Computing 2020-04-17 Target: OpenCL - Benchmark: FFT SP phoronix-ml.txt 600 1200 1800 2400 3000 SE +/- 2.81, N = 3 2703.37 1. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi
SHOC Scalable HeterOgeneous Computing Target: OpenCL - Benchmark: Reduction OpenBenchmarking.org GB/s, More Is Better SHOC Scalable HeterOgeneous Computing 2020-04-17 Target: OpenCL - Benchmark: Reduction phoronix-ml.txt 130 260 390 520 650 SE +/- 0.41, N = 3 595.05 1. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi
SHOC Scalable HeterOgeneous Computing Target: OpenCL - Benchmark: Triad OpenBenchmarking.org GB/s, More Is Better SHOC Scalable HeterOgeneous Computing 2020-04-17 Target: OpenCL - Benchmark: Triad phoronix-ml.txt 6 12 18 24 30 SE +/- 0.23, N = 6 23.05 1. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi
SHOC Scalable HeterOgeneous Computing Target: OpenCL - Benchmark: GEMM SGEMM_N OpenBenchmarking.org GFLOPS, More Is Better SHOC Scalable HeterOgeneous Computing 2020-04-17 Target: OpenCL - Benchmark: GEMM SGEMM_N phoronix-ml.txt 2K 4K 6K 8K 10K SE +/- 23.01, N = 3 8470.02 1. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi
SHOC Scalable HeterOgeneous Computing Target: OpenCL - Benchmark: MD5 Hash OpenBenchmarking.org GHash/s, More Is Better SHOC Scalable HeterOgeneous Computing 2020-04-17 Target: OpenCL - Benchmark: MD5 Hash phoronix-ml.txt 11 22 33 44 55 SE +/- 0.68, N = 3 49.64 1. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi
SHOC Scalable HeterOgeneous Computing Target: OpenCL - Benchmark: S3D OpenBenchmarking.org GFLOPS, More Is Better SHOC Scalable HeterOgeneous Computing 2020-04-17 Target: OpenCL - Benchmark: S3D phoronix-ml.txt 70 140 210 280 350 SE +/- 3.81, N = 15 298.49 1. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi
Whisper.cpp Model: ggml-medium.en - Input: 2016 State of the Union OpenBenchmarking.org Seconds, Fewer Is Better Whisper.cpp 1.6.2 Model: ggml-medium.en - Input: 2016 State of the Union phoronix-ml.txt 130 260 390 520 650 SE +/- 1.41, N = 3 579.17 1. (CXX) g++ options: -O3 -std=c++11 -fPIC -pthread -msse3 -mssse3 -mavx -mf16c -mfma -mavx2 -mavx512f -mavx512cd -mavx512vl -mavx512dq -mavx512bw -mavx512vbmi -mavx512vnni
Whisper.cpp Model: ggml-small.en - Input: 2016 State of the Union OpenBenchmarking.org Seconds, Fewer Is Better Whisper.cpp 1.6.2 Model: ggml-small.en - Input: 2016 State of the Union phoronix-ml.txt 50 100 150 200 250 SE +/- 0.41, N = 3 218.19 1. (CXX) g++ options: -O3 -std=c++11 -fPIC -pthread -msse3 -mssse3 -mavx -mf16c -mfma -mavx2 -mavx512f -mavx512cd -mavx512vl -mavx512dq -mavx512bw -mavx512vbmi -mavx512vnni
Whisper.cpp Model: ggml-base.en - Input: 2016 State of the Union OpenBenchmarking.org Seconds, Fewer Is Better Whisper.cpp 1.6.2 Model: ggml-base.en - Input: 2016 State of the Union phoronix-ml.txt 20 40 60 80 100 SE +/- 0.44, N = 3 92.75 1. (CXX) g++ options: -O3 -std=c++11 -fPIC -pthread -msse3 -mssse3 -mavx -mf16c -mfma -mavx2 -mavx512f -mavx512cd -mavx512vl -mavx512dq -mavx512bw -mavx512vbmi -mavx512vnni
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 phoronix-ml.txt 110 220 330 440 550 SE +/- 2.62, N = 3 504.83 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 phoronix-ml.txt 7 14 21 28 35 SE +/- 0.33, N = 3 31.04 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 phoronix-ml.txt 14 28 42 56 70 SE +/- 0.24, N = 3 61.61 1. (F9X) gfortran options: -O0
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 phoronix-ml.txt 7 14 21 28 35 SE +/- 0.30, N = 15 30.19 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Plot Polynomial Kernel Approximation OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot Polynomial Kernel Approximation phoronix-ml.txt 20 40 60 80 100 SE +/- 0.04, N = 3 104.70 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: 20 Newsgroups / Logistic Regression OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: 20 Newsgroups / Logistic Regression phoronix-ml.txt 3 6 9 12 15 SE +/- 0.06, N = 3 10.45 1. (F9X) gfortran options: -O0
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 phoronix-ml.txt 15 30 45 60 75 SE +/- 0.83, N = 3 65.74 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 phoronix-ml.txt 12 24 36 48 60 SE +/- 0.61, N = 4 52.73 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 phoronix-ml.txt 300 600 900 1200 1500 SE +/- 2.36, N = 3 1528.97 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 phoronix-ml.txt 30 60 90 120 150 SE +/- 1.23, N = 12 153.34 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 phoronix-ml.txt 70 140 210 280 350 SE +/- 0.61, N = 3 320.39 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 phoronix-ml.txt 20 40 60 80 100 SE +/- 0.76, N = 3 90.64 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Isotonic / Pathological OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Isotonic / Pathological phoronix-ml.txt 800 1600 2400 3200 4000 SE +/- 9.06, N = 3 3843.26 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 phoronix-ml.txt 40 80 120 160 200 SE +/- 0.90, N = 3 166.78 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 phoronix-ml.txt 7 14 21 28 35 SE +/- 0.08, N = 3 31.21 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Isotonic / Logistic OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Isotonic / Logistic phoronix-ml.txt 300 600 900 1200 1500 SE +/- 0.82, N = 3 1406.45 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: TSNE MNIST Dataset OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: TSNE MNIST Dataset phoronix-ml.txt 50 100 150 200 250 SE +/- 0.66, N = 3 247.56 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: LocalOutlierFactor OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: LocalOutlierFactor phoronix-ml.txt 5 10 15 20 25 SE +/- 0.13, N = 3 21.62 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Feature Expansions OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Feature Expansions phoronix-ml.txt 20 40 60 80 100 SE +/- 0.56, N = 3 100.35 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 phoronix-ml.txt 9 18 27 36 45 SE +/- 0.09, N = 3 41.48 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Plot Hierarchical OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot Hierarchical phoronix-ml.txt 30 60 90 120 150 SE +/- 0.41, N = 3 141.46 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Text Vectorizers OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Text Vectorizers phoronix-ml.txt 10 20 30 40 50 SE +/- 0.18, N = 3 45.34 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Isolation Forest OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Isolation Forest phoronix-ml.txt 40 80 120 160 200 SE +/- 0.54, N = 3 176.29 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: SGDOneClassSVM OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: SGDOneClassSVM phoronix-ml.txt 50 100 150 200 250 SE +/- 0.33, N = 3 233.41 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: SGD Regression OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: SGD Regression phoronix-ml.txt 14 28 42 56 70 SE +/- 0.08, N = 3 64.37 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Plot Neighbors OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot Neighbors phoronix-ml.txt 30 60 90 120 150 SE +/- 0.47, N = 3 114.84 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: MNIST Dataset OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: MNIST Dataset phoronix-ml.txt 12 24 36 48 60 SE +/- 0.42, N = 3 52.74 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Plot Ward OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot Ward phoronix-ml.txt 10 20 30 40 50 SE +/- 0.35, N = 8 42.10 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Sparsify OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Sparsify phoronix-ml.txt 20 40 60 80 100 SE +/- 0.30, N = 3 108.46 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Lasso OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Lasso phoronix-ml.txt 70 140 210 280 350 SE +/- 0.07, N = 3 308.23 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Tree OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Tree phoronix-ml.txt 11 22 33 44 55 SE +/- 0.48, N = 5 46.97 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: SAGA OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: SAGA phoronix-ml.txt 140 280 420 560 700 SE +/- 3.66, N = 3 669.12 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: GLM OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: GLM phoronix-ml.txt 40 80 120 160 200 SE +/- 0.81, N = 3 168.33 1. (F9X) gfortran options: -O0
ONNX Runtime Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Standard phoronix-ml.txt 0.4974 0.9948 1.4922 1.9896 2.487 SE +/- 0.00518, N = 3 2.21086 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Parallel phoronix-ml.txt 0.2919 0.5838 0.8757 1.1676 1.4595 SE +/- 0.00341, N = 3 1.29718 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: super-resolution-10 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: super-resolution-10 - Device: CPU - Executor: Standard phoronix-ml.txt 20 40 60 80 100 SE +/- 0.21, N = 3 97.32 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: super-resolution-10 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: super-resolution-10 - Device: CPU - Executor: Parallel phoronix-ml.txt 30 60 90 120 150 SE +/- 0.93, N = 3 123.74 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard phoronix-ml.txt 70 140 210 280 350 SE +/- 0.68, N = 3 328.29 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Parallel phoronix-ml.txt 20 40 60 80 100 SE +/- 0.36, N = 3 108.88 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: fcn-resnet101-11 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: fcn-resnet101-11 - Device: CPU - Executor: Standard phoronix-ml.txt 0.9263 1.8526 2.7789 3.7052 4.6315 SE +/- 0.00482, N = 3 4.11676 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard phoronix-ml.txt 140 280 420 560 700 SE +/- 7.24, N = 3 639.91 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: CaffeNet 12-int8 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: CaffeNet 12-int8 - Device: CPU - Executor: Parallel phoronix-ml.txt 40 80 120 160 200 SE +/- 1.63, N = 15 203.06 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: T5 Encoder - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: T5 Encoder - Device: CPU - Executor: Standard phoronix-ml.txt 40 80 120 160 200 SE +/- 2.15, N = 4 172.04 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: T5 Encoder - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: T5 Encoder - Device: CPU - Executor: Parallel phoronix-ml.txt 60 120 180 240 300 SE +/- 1.41, N = 3 272.18 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ZFNet-512 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: ZFNet-512 - Device: CPU - Executor: Standard phoronix-ml.txt 20 40 60 80 100 SE +/- 1.32, N = 4 109.86 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ZFNet-512 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: ZFNet-512 - Device: CPU - Executor: Parallel phoronix-ml.txt 13 26 39 52 65 SE +/- 0.67, N = 4 57.86 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: yolov4 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: yolov4 - Device: CPU - Executor: Standard phoronix-ml.txt 3 6 9 12 15 SE +/- 0.03544, N = 3 9.65442 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: yolov4 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: yolov4 - Device: CPU - Executor: Parallel phoronix-ml.txt 1.2143 2.4286 3.6429 4.8572 6.0715 SE +/- 0.07557, N = 3 5.39684 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
OpenVINO Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU phoronix-ml.txt 0.0675 0.135 0.2025 0.27 0.3375 SE +/- 0.00, N = 3 0.3 MIN: 0.17 / MAX: 9.44 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU phoronix-ml.txt 14K 28K 42K 56K 70K SE +/- 38.55, N = 3 67537.77 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Handwritten English Recognition FP16-INT8 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Handwritten English Recognition FP16-INT8 - Device: CPU phoronix-ml.txt 5 10 15 20 25 SE +/- 0.08, N = 3 21.58 MIN: 16.4 / MAX: 43.47 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Handwritten English Recognition FP16-INT8 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Handwritten English Recognition FP16-INT8 - Device: CPU phoronix-ml.txt 200 400 600 800 1000 SE +/- 4.28, N = 3 1108.65 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU phoronix-ml.txt 0.0968 0.1936 0.2904 0.3872 0.484 SE +/- 0.00, N = 3 0.43 MIN: 0.23 / MAX: 11.96 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU phoronix-ml.txt 10K 20K 30K 40K 50K SE +/- 17.34, N = 3 48433.92 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Person Re-Identification Retail FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Person Re-Identification Retail FP16 - Device: CPU phoronix-ml.txt 1.062 2.124 3.186 4.248 5.31 SE +/- 0.01, N = 3 4.72 MIN: 2.72 / MAX: 17.21 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Person Re-Identification Retail FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Person Re-Identification Retail FP16 - Device: CPU phoronix-ml.txt 500 1000 1500 2000 2500 SE +/- 3.45, N = 3 2523.59 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Handwritten English Recognition FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Handwritten English Recognition FP16 - Device: CPU phoronix-ml.txt 6 12 18 24 30 SE +/- 0.09, N = 3 23.12 MIN: 14.9 / MAX: 38.92 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Handwritten English Recognition FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Handwritten English Recognition FP16 - Device: CPU phoronix-ml.txt 200 400 600 800 1000 SE +/- 4.12, N = 3 1035.29 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Noise Suppression Poconet-Like FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Noise Suppression Poconet-Like FP16 - Device: CPU phoronix-ml.txt 3 6 9 12 15 SE +/- 0.18, N = 15 11.53 MIN: 5.76 / MAX: 42.76 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Person Vehicle Bike Detection FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Person Vehicle Bike Detection FP16 - Device: CPU phoronix-ml.txt 2 4 6 8 10 SE +/- 0.01, N = 3 6.18 MIN: 3.57 / MAX: 20.46 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Person Vehicle Bike Detection FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Person Vehicle Bike Detection FP16 - Device: CPU phoronix-ml.txt 400 800 1200 1600 2000 SE +/- 3.67, N = 3 1930.08 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Weld Porosity Detection FP16-INT8 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Weld Porosity Detection FP16-INT8 - Device: CPU phoronix-ml.txt 2 4 6 8 10 SE +/- 0.01, N = 3 6.31 MIN: 3.31 / MAX: 21.37 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Weld Porosity Detection FP16-INT8 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Weld Porosity Detection FP16-INT8 - Device: CPU phoronix-ml.txt 800 1600 2400 3200 4000 SE +/- 3.08, N = 3 3742.77 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Machine Translation EN To DE FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Machine Translation EN To DE FP16 - Device: CPU phoronix-ml.txt 14 28 42 56 70 SE +/- 1.08, N = 12 63.99 MIN: 29.61 / MAX: 110.09 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Road Segmentation ADAS FP16-INT8 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Road Segmentation ADAS FP16-INT8 - Device: CPU phoronix-ml.txt 4 8 12 16 20 SE +/- 0.06, N = 3 17.62 MIN: 9.01 / MAX: 33.69 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Road Segmentation ADAS FP16-INT8 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Road Segmentation ADAS FP16-INT8 - Device: CPU phoronix-ml.txt 150 300 450 600 750 SE +/- 2.47, N = 3 679.66 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Face Detection Retail FP16-INT8 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Face Detection Retail FP16-INT8 - Device: CPU phoronix-ml.txt 0.8055 1.611 2.4165 3.222 4.0275 SE +/- 0.00, N = 3 3.58 MIN: 1.95 / MAX: 16.91 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Face Detection Retail FP16-INT8 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Face Detection Retail FP16-INT8 - Device: CPU phoronix-ml.txt 1400 2800 4200 5600 7000 SE +/- 8.36, N = 3 6458.38 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Weld Porosity Detection FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Weld Porosity Detection FP16 - Device: CPU phoronix-ml.txt 3 6 9 12 15 SE +/- 0.01, N = 3 12.25 MIN: 6.32 / MAX: 26.6 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Weld Porosity Detection FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Weld Porosity Detection FP16 - Device: CPU phoronix-ml.txt 400 800 1200 1600 2000 SE +/- 1.95, N = 3 1947.67 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Vehicle Detection FP16-INT8 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Vehicle Detection FP16-INT8 - Device: CPU phoronix-ml.txt 1.2398 2.4796 3.7194 4.9592 6.199 SE +/- 0.01, N = 3 5.51 MIN: 2.97 / MAX: 19.15 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Vehicle Detection FP16-INT8 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Vehicle Detection FP16-INT8 - Device: CPU phoronix-ml.txt 500 1000 1500 2000 2500 SE +/- 3.34, N = 3 2160.57 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Face Detection Retail FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Face Detection Retail FP16 - Device: CPU phoronix-ml.txt 0.621 1.242 1.863 2.484 3.105 SE +/- 0.01, N = 3 2.76 MIN: 1.41 / MAX: 15.61 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Face Detection Retail FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Face Detection Retail FP16 - Device: CPU phoronix-ml.txt 900 1800 2700 3600 4500 SE +/- 13.60, N = 3 4273.09 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Face Detection FP16-INT8 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Face Detection FP16-INT8 - Device: CPU phoronix-ml.txt 70 140 210 280 350 SE +/- 0.15, N = 3 320.42 MIN: 299.06 / MAX: 380.18 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Face Detection FP16-INT8 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Face Detection FP16-INT8 - Device: CPU phoronix-ml.txt 9 18 27 36 45 SE +/- 0.02, N = 3 37.36 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Vehicle Detection FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Vehicle Detection FP16 - Device: CPU phoronix-ml.txt 3 6 9 12 15 SE +/- 0.14, N = 15 11.12 MIN: 4.52 / MAX: 34.07 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Vehicle Detection FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Vehicle Detection FP16 - Device: CPU phoronix-ml.txt 200 400 600 800 1000 SE +/- 15.55, N = 15 1077.87 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Person Detection FP32 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Person Detection FP32 - Device: CPU phoronix-ml.txt 20 40 60 80 100 SE +/- 0.82, N = 15 100.46 MIN: 32.5 / MAX: 161.81 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Person Detection FP32 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Person Detection FP32 - Device: CPU phoronix-ml.txt 30 60 90 120 150 SE +/- 1.07, N = 15 119.41 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Face Detection FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Face Detection FP16 - Device: CPU phoronix-ml.txt 130 260 390 520 650 SE +/- 2.89, N = 3 607.48 MIN: 575.29 / MAX: 657.06 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Face Detection FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Face Detection FP16 - Device: CPU phoronix-ml.txt 5 10 15 20 25 SE +/- 0.09, N = 3 19.69 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
XNNPACK Model: QS8MobileNetV2 OpenBenchmarking.org us, Fewer Is Better XNNPACK b7b048 Model: QS8MobileNetV2 phoronix-ml.txt 300 600 900 1200 1500 SE +/- 7.84, N = 3 1398 1. (CXX) g++ options: -O3 -lrt -lm
XNNPACK Model: FP16MobileNetV3Small OpenBenchmarking.org us, Fewer Is Better XNNPACK b7b048 Model: FP16MobileNetV3Small phoronix-ml.txt 300 600 900 1200 1500 SE +/- 5.55, N = 3 1464 1. (CXX) g++ options: -O3 -lrt -lm
XNNPACK Model: FP16MobileNetV3Large OpenBenchmarking.org us, Fewer Is Better XNNPACK b7b048 Model: FP16MobileNetV3Large phoronix-ml.txt 500 1000 1500 2000 2500 SE +/- 6.66, N = 3 2128 1. (CXX) g++ options: -O3 -lrt -lm
XNNPACK Model: FP16MobileNetV2 OpenBenchmarking.org us, Fewer Is Better XNNPACK b7b048 Model: FP16MobileNetV2 phoronix-ml.txt 300 600 900 1200 1500 SE +/- 15.14, N = 3 1495 1. (CXX) g++ options: -O3 -lrt -lm
XNNPACK Model: FP16MobileNetV1 OpenBenchmarking.org us, Fewer Is Better XNNPACK b7b048 Model: FP16MobileNetV1 phoronix-ml.txt 200 400 600 800 1000 SE +/- 6.56, N = 3 1144 1. (CXX) g++ options: -O3 -lrt -lm
XNNPACK Model: FP32MobileNetV3Small OpenBenchmarking.org us, Fewer Is Better XNNPACK b7b048 Model: FP32MobileNetV3Small phoronix-ml.txt 300 600 900 1200 1500 SE +/- 3.61, N = 3 1503 1. (CXX) g++ options: -O3 -lrt -lm
XNNPACK Model: FP32MobileNetV3Large OpenBenchmarking.org us, Fewer Is Better XNNPACK b7b048 Model: FP32MobileNetV3Large phoronix-ml.txt 500 1000 1500 2000 2500 SE +/- 12.67, N = 3 2465 1. (CXX) g++ options: -O3 -lrt -lm
XNNPACK Model: FP32MobileNetV2 OpenBenchmarking.org us, Fewer Is Better XNNPACK b7b048 Model: FP32MobileNetV2 phoronix-ml.txt 400 800 1200 1600 2000 SE +/- 14.40, N = 3 1873 1. (CXX) g++ options: -O3 -lrt -lm
XNNPACK Model: FP32MobileNetV1 OpenBenchmarking.org us, Fewer Is Better XNNPACK b7b048 Model: FP32MobileNetV1 phoronix-ml.txt 300 600 900 1200 1500 SE +/- 2.52, N = 3 1233 1. (CXX) g++ options: -O3 -lrt -lm
NCNN Target: Vulkan GPU - Model: vision_transformer OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: vision_transformer phoronix-ml.txt 9 18 27 36 45 SE +/- 0.27, N = 3 41.05 MIN: 38.99 / MAX: 101.63 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: regnety_400m OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: regnety_400m phoronix-ml.txt 5 10 15 20 25 SE +/- 0.09, N = 3 18.63 MIN: 18.07 / MAX: 108.4 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: yolov4-tiny OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: yolov4-tiny phoronix-ml.txt 6 12 18 24 30 SE +/- 0.41, N = 3 23.64 MIN: 22.17 / MAX: 39.94 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPUv2-yolov3v2-yolov3 - Model: mobilenetv2-yolov3 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPUv2-yolov3v2-yolov3 - Model: mobilenetv2-yolov3 phoronix-ml.txt 4 8 12 16 20 SE +/- 0.10, N = 3 13.79 MIN: 13.15 / MAX: 23.26 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: alexnet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: alexnet phoronix-ml.txt 1.188 2.376 3.564 4.752 5.94 SE +/- 0.02, N = 3 5.28 MIN: 5.1 / MAX: 15.43 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: resnet18 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: resnet18 phoronix-ml.txt 2 4 6 8 10 SE +/- 0.08, N = 3 7.86 MIN: 7.54 / MAX: 15.18 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: vgg16 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: vgg16 phoronix-ml.txt 6 12 18 24 30 SE +/- 0.31, N = 3 25.13 MIN: 23.1 / MAX: 139.05 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: googlenet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: googlenet phoronix-ml.txt 4 8 12 16 20 SE +/- 0.04, N = 3 16.01 MIN: 15.51 / MAX: 26.58 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: blazeface OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: blazeface phoronix-ml.txt 0.7065 1.413 2.1195 2.826 3.5325 SE +/- 0.00, N = 3 3.14 MIN: 3.01 / MAX: 8.5 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: mnasnet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: mnasnet phoronix-ml.txt 1.3478 2.6956 4.0434 5.3912 6.739 SE +/- 0.01, N = 3 5.99 MIN: 5.68 / MAX: 14.25 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: shufflenet-v2 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: shufflenet-v2 phoronix-ml.txt 2 4 6 8 10 SE +/- 0.05, N = 3 8.06 MIN: 7.83 / MAX: 15.08 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3 phoronix-ml.txt 2 4 6 8 10 SE +/- 0.03, N = 3 6.49 MIN: 6.21 / MAX: 15.75 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2 phoronix-ml.txt 2 4 6 8 10 SE +/- 0.02, N = 3 6.31 MIN: 5.98 / MAX: 14.56 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: mobilenet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: mobilenet phoronix-ml.txt 4 8 12 16 20 SE +/- 0.10, N = 3 13.79 MIN: 13.15 / MAX: 23.26 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: vision_transformer OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: vision_transformer phoronix-ml.txt 9 18 27 36 45 SE +/- 0.18, N = 15 40.59 MIN: 37.83 / MAX: 299.43 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: regnety_400m OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: regnety_400m phoronix-ml.txt 5 10 15 20 25 SE +/- 0.12, N = 15 18.58 MIN: 17.32 / MAX: 295.21 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: squeezenet_ssd OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: squeezenet_ssd phoronix-ml.txt 4 8 12 16 20 SE +/- 0.12, N = 15 14.34 MIN: 13.13 / MAX: 263.27 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: yolov4-tiny OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: yolov4-tiny phoronix-ml.txt 6 12 18 24 30 SE +/- 0.11, N = 15 24.14 MIN: 21.58 / MAX: 105.12 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPUv2-yolov3v2-yolov3 - Model: mobilenetv2-yolov3 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPUv2-yolov3v2-yolov3 - Model: mobilenetv2-yolov3 phoronix-ml.txt 4 8 12 16 20 SE +/- 0.12, N = 15 13.85 MIN: 12.83 / MAX: 247.13 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: resnet50 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: resnet50 phoronix-ml.txt 3 6 9 12 15 SE +/- 0.13, N = 15 13.33 MIN: 11.94 / MAX: 281.3 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: alexnet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: alexnet phoronix-ml.txt 1.251 2.502 3.753 5.004 6.255 SE +/- 0.07, N = 15 5.56 MIN: 5.07 / MAX: 35.35 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: resnet18 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: resnet18 phoronix-ml.txt 2 4 6 8 10 SE +/- 0.07, N = 15 8.02 MIN: 7.54 / MAX: 17.96 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: vgg16 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: vgg16 phoronix-ml.txt 6 12 18 24 30 SE +/- 0.34, N = 15 25.71 MIN: 22.56 / MAX: 344.2 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: googlenet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: googlenet phoronix-ml.txt 4 8 12 16 20 SE +/- 0.15, N = 15 16.42 MIN: 15.38 / MAX: 271.34 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: blazeface OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: blazeface phoronix-ml.txt 0.6998 1.3996 2.0994 2.7992 3.499 SE +/- 0.02, N = 15 3.11 MIN: 2.85 / MAX: 11.53 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: efficientnet-b0 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: efficientnet-b0 phoronix-ml.txt 2 4 6 8 10 SE +/- 0.09, N = 15 8.28 MIN: 7.57 / MAX: 296.15 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: shufflenet-v2 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: shufflenet-v2 phoronix-ml.txt 2 4 6 8 10 SE +/- 0.09, N = 15 8.15 MIN: 7.52 / MAX: 291.18 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU-v3-v3 - Model: mobilenet-v3 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU-v3-v3 - Model: mobilenet-v3 phoronix-ml.txt 2 4 6 8 10 SE +/- 0.04, N = 15 6.45 MIN: 5.96 / MAX: 63.77 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU-v2-v2 - Model: mobilenet-v2 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU-v2-v2 - Model: mobilenet-v2 phoronix-ml.txt 2 4 6 8 10 SE +/- 0.05, N = 15 6.30 MIN: 5.63 / MAX: 33.15 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: mobilenet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: mobilenet phoronix-ml.txt 4 8 12 16 20 SE +/- 0.12, N = 15 13.85 MIN: 12.83 / MAX: 247.13 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
Mobile Neural Network Model: inception-v3 OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 2.9.b11b7037d Model: inception-v3 phoronix-ml.txt 8 16 24 32 40 SE +/- 0.04, N = 3 36.45 MIN: 36.16 / MAX: 50.97 1. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -pthread -ldl
Mobile Neural Network Model: mobilenet-v1-1.0 OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 2.9.b11b7037d Model: mobilenet-v1-1.0 phoronix-ml.txt 0.8514 1.7028 2.5542 3.4056 4.257 SE +/- 0.007, N = 3 3.784 MIN: 3.71 / MAX: 6.58 1. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -pthread -ldl
Mobile Neural Network Model: MobileNetV2_224 OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 2.9.b11b7037d Model: MobileNetV2_224 phoronix-ml.txt 0.7353 1.4706 2.2059 2.9412 3.6765 SE +/- 0.042, N = 3 3.268 MIN: 3.15 / MAX: 5.05 1. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -pthread -ldl
Mobile Neural Network Model: SqueezeNetV1.0 OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 2.9.b11b7037d Model: SqueezeNetV1.0 phoronix-ml.txt 2 4 6 8 10 SE +/- 0.200, N = 3 6.429 MIN: 5.97 / MAX: 7.14 1. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -pthread -ldl
Mobile Neural Network Model: resnet-v2-50 OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 2.9.b11b7037d Model: resnet-v2-50 phoronix-ml.txt 5 10 15 20 25 SE +/- 0.11, N = 3 18.53 MIN: 18.26 / MAX: 28.9 1. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -pthread -ldl
Mobile Neural Network Model: squeezenetv1.1 OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 2.9.b11b7037d Model: squeezenetv1.1 phoronix-ml.txt 0.9736 1.9472 2.9208 3.8944 4.868 SE +/- 0.117, N = 3 4.327 MIN: 3.96 / MAX: 6.65 1. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -pthread -ldl
Mobile Neural Network Model: mobilenetV3 OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 2.9.b11b7037d Model: mobilenetV3 phoronix-ml.txt 0.5706 1.1412 1.7118 2.2824 2.853 SE +/- 0.008, N = 3 2.536 MIN: 2.4 / MAX: 3.47 1. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -pthread -ldl
Mobile Neural Network Model: nasnet OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 2.9.b11b7037d Model: nasnet phoronix-ml.txt 4 8 12 16 20 SE +/- 0.02, N = 3 15.30 MIN: 14.65 / MAX: 21.59 1. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -pthread -ldl
TensorFlow Device: GPU - Batch Size: 512 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 512 - Model: ResNet-50 phoronix-ml.txt 3 6 9 12 15 SE +/- 0.03, N = 3 9.34
TensorFlow Device: GPU - Batch Size: 512 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 512 - Model: GoogLeNet phoronix-ml.txt 7 14 21 28 35 SE +/- 0.10, N = 3 28.73
TensorFlow Device: GPU - Batch Size: 256 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 256 - Model: ResNet-50 phoronix-ml.txt 3 6 9 12 15 SE +/- 0.01, N = 3 9.36
TensorFlow Device: GPU - Batch Size: 256 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 256 - Model: GoogLeNet phoronix-ml.txt 7 14 21 28 35 SE +/- 0.01, N = 3 29.03
TensorFlow Device: CPU - Batch Size: 512 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 512 - Model: ResNet-50 phoronix-ml.txt 13 26 39 52 65 SE +/- 0.33, N = 3 58.50
TensorFlow Device: CPU - Batch Size: 512 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 512 - Model: GoogLeNet phoronix-ml.txt 40 80 120 160 200 SE +/- 1.69, N = 7 185.25
TensorFlow Device: CPU - Batch Size: 256 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 256 - Model: ResNet-50 phoronix-ml.txt 13 26 39 52 65 SE +/- 0.91, N = 9 59.70
TensorFlow Device: CPU - Batch Size: 256 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 256 - Model: GoogLeNet phoronix-ml.txt 50 100 150 200 250 SE +/- 0.25, N = 3 227.06
TensorFlow Device: GPU - Batch Size: 64 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 64 - Model: ResNet-50 phoronix-ml.txt 3 6 9 12 15 SE +/- 0.00, N = 3 9.24
TensorFlow Device: GPU - Batch Size: 64 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 64 - Model: GoogLeNet phoronix-ml.txt 7 14 21 28 35 SE +/- 0.02, N = 3 28.53
TensorFlow Device: GPU - Batch Size: 32 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 32 - Model: ResNet-50 phoronix-ml.txt 3 6 9 12 15 SE +/- 0.01, N = 3 9.14
TensorFlow Device: GPU - Batch Size: 32 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 32 - Model: GoogLeNet phoronix-ml.txt 7 14 21 28 35 SE +/- 0.04, N = 3 27.91
TensorFlow Device: GPU - Batch Size: 16 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 16 - Model: ResNet-50 phoronix-ml.txt 2 4 6 8 10 SE +/- 0.01, N = 3 8.94
TensorFlow Device: GPU - Batch Size: 16 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 16 - Model: GoogLeNet phoronix-ml.txt 6 12 18 24 30 SE +/- 0.01, N = 3 26.91
TensorFlow Device: CPU - Batch Size: 64 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 64 - Model: ResNet-50 phoronix-ml.txt 16 32 48 64 80 SE +/- 0.33, N = 3 70.89
TensorFlow Device: CPU - Batch Size: 64 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 64 - Model: GoogLeNet phoronix-ml.txt 50 100 150 200 250 SE +/- 0.29, N = 3 225.16
TensorFlow Device: CPU - Batch Size: 32 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 32 - Model: ResNet-50 phoronix-ml.txt 15 30 45 60 75 SE +/- 0.07, N = 3 67.94
TensorFlow Device: CPU - Batch Size: 32 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 32 - Model: GoogLeNet phoronix-ml.txt 50 100 150 200 250 SE +/- 0.23, N = 3 218.07
TensorFlow Device: CPU - Batch Size: 16 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 16 - Model: ResNet-50 phoronix-ml.txt 14 28 42 56 70 SE +/- 0.06, N = 3 62.29
TensorFlow Device: CPU - Batch Size: 16 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 16 - Model: GoogLeNet phoronix-ml.txt 40 80 120 160 200 SE +/- 0.22, N = 3 198.11
TensorFlow Device: GPU - Batch Size: 512 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 512 - Model: AlexNet phoronix-ml.txt 11 22 33 44 55 SE +/- 0.09, N = 3 49.28
TensorFlow Device: GPU - Batch Size: 256 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 256 - Model: AlexNet phoronix-ml.txt 11 22 33 44 55 SE +/- 0.03, N = 3 48.92
TensorFlow Device: GPU - Batch Size: 1 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 1 - Model: ResNet-50 phoronix-ml.txt 2 4 6 8 10 SE +/- 0.03, N = 3 6.69
TensorFlow Device: GPU - Batch Size: 1 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 1 - Model: GoogLeNet phoronix-ml.txt 5 10 15 20 25 SE +/- 0.15, N = 3 21.03
TensorFlow Device: CPU - Batch Size: 512 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 512 - Model: AlexNet phoronix-ml.txt 140 280 420 560 700 SE +/- 1.19, N = 3 643.44
TensorFlow Device: CPU - Batch Size: 256 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 256 - Model: AlexNet phoronix-ml.txt 140 280 420 560 700 SE +/- 0.20, N = 3 627.50
TensorFlow Device: CPU - Batch Size: 1 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 1 - Model: ResNet-50 phoronix-ml.txt 5 10 15 20 25 SE +/- 0.11, N = 3 18.38
TensorFlow Device: CPU - Batch Size: 1 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 1 - Model: GoogLeNet phoronix-ml.txt 14 28 42 56 70 SE +/- 0.30, N = 3 60.92
TensorFlow Device: GPU - Batch Size: 64 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 64 - Model: AlexNet phoronix-ml.txt 11 22 33 44 55 SE +/- 0.04, N = 3 47.85
TensorFlow Device: GPU - Batch Size: 512 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 512 - Model: VGG-16 phoronix-ml.txt 0.5738 1.1476 1.7214 2.2952 2.869 SE +/- 0.00, N = 3 2.55
TensorFlow Device: GPU - Batch Size: 32 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 32 - Model: AlexNet phoronix-ml.txt 10 20 30 40 50 SE +/- 0.04, N = 3 46.14
TensorFlow Device: GPU - Batch Size: 256 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 256 - Model: VGG-16 phoronix-ml.txt 0.5738 1.1476 1.7214 2.2952 2.869 SE +/- 0.01, N = 3 2.55
TensorFlow Device: GPU - Batch Size: 16 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 16 - Model: AlexNet phoronix-ml.txt 10 20 30 40 50 SE +/- 0.02, N = 3 42.43
TensorFlow Device: CPU - Batch Size: 64 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 64 - Model: AlexNet phoronix-ml.txt 110 220 330 440 550 SE +/- 0.16, N = 3 516.18
TensorFlow Device: CPU - Batch Size: 512 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 512 - Model: VGG-16 phoronix-ml.txt 7 14 21 28 35 SE +/- 0.01, N = 3 30.43
TensorFlow Device: CPU - Batch Size: 32 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 32 - Model: AlexNet phoronix-ml.txt 90 180 270 360 450 SE +/- 0.28, N = 3 409.56
TensorFlow Device: CPU - Batch Size: 256 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 256 - Model: VGG-16 phoronix-ml.txt 7 14 21 28 35 SE +/- 0.08, N = 3 30.16
TensorFlow Device: CPU - Batch Size: 16 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 16 - Model: AlexNet phoronix-ml.txt 60 120 180 240 300 SE +/- 0.39, N = 3 288.71
TensorFlow Device: GPU - Batch Size: 64 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 64 - Model: VGG-16 phoronix-ml.txt 0.5738 1.1476 1.7214 2.2952 2.869 SE +/- 0.00, N = 3 2.55
TensorFlow Device: GPU - Batch Size: 32 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 32 - Model: VGG-16 phoronix-ml.txt 0.5715 1.143 1.7145 2.286 2.8575 SE +/- 0.00, N = 3 2.54
TensorFlow Device: GPU - Batch Size: 16 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 16 - Model: VGG-16 phoronix-ml.txt 0.5648 1.1296 1.6944 2.2592 2.824 SE +/- 0.00, N = 3 2.51
TensorFlow Device: GPU - Batch Size: 1 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 1 - Model: AlexNet phoronix-ml.txt 4 8 12 16 20 SE +/- 0.14, N = 15 15.38
TensorFlow Device: CPU - Batch Size: 64 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 64 - Model: VGG-16 phoronix-ml.txt 7 14 21 28 35 SE +/- 0.02, N = 3 29.05
TensorFlow Device: CPU - Batch Size: 32 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 32 - Model: VGG-16 phoronix-ml.txt 7 14 21 28 35 SE +/- 0.05, N = 3 28.32
TensorFlow Device: CPU - Batch Size: 16 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 16 - Model: VGG-16 phoronix-ml.txt 6 12 18 24 30 SE +/- 0.05, N = 3 27.34
TensorFlow Device: CPU - Batch Size: 1 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 1 - Model: AlexNet phoronix-ml.txt 7 14 21 28 35 SE +/- 0.00, N = 3 30.66
TensorFlow Device: GPU - Batch Size: 1 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 1 - Model: VGG-16 phoronix-ml.txt 0.495 0.99 1.485 1.98 2.475 SE +/- 0.00, N = 3 2.20
TensorFlow Device: CPU - Batch Size: 1 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 1 - Model: VGG-16 phoronix-ml.txt 3 6 9 12 15 SE +/- 0.00, N = 3 9.70
PyTorch Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l phoronix-ml.txt 3 6 9 12 15 SE +/- 0.09, N = 12 9.94 MIN: 7.81 / MAX: 10.45
PyTorch Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l phoronix-ml.txt 3 6 9 12 15 SE +/- 0.05, N = 3 10.11 MIN: 8.31 / MAX: 10.38
PyTorch Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l phoronix-ml.txt 3 6 9 12 15 SE +/- 0.05, N = 3 9.98 MIN: 7.98 / MAX: 10.29
PyTorch Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l phoronix-ml.txt 3 6 9 12 15 SE +/- 0.07, N = 3 9.85 MIN: 8.27 / MAX: 10.3
PyTorch Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l phoronix-ml.txt 3 6 9 12 15 SE +/- 0.09, N = 3 9.90 MIN: 8.07 / MAX: 10.35
PyTorch Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l phoronix-ml.txt 4 8 12 16 20 SE +/- 0.19, N = 3 14.18 MIN: 12.11 / MAX: 14.81
PyTorch Device: CPU - Batch Size: 512 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 512 - Model: ResNet-152 phoronix-ml.txt 4 8 12 16 20 SE +/- 0.07, N = 3 17.99 MIN: 14.67 / MAX: 18.38
PyTorch Device: CPU - Batch Size: 256 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 256 - Model: ResNet-152 phoronix-ml.txt 4 8 12 16 20 SE +/- 0.06, N = 3 17.92 MIN: 14.68 / MAX: 18.36
PyTorch Device: CPU - Batch Size: 64 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 64 - Model: ResNet-152 phoronix-ml.txt 4 8 12 16 20 SE +/- 0.08, N = 3 17.64 MIN: 14.41 / MAX: 18.1
PyTorch Device: CPU - Batch Size: 512 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 512 - Model: ResNet-50 phoronix-ml.txt 10 20 30 40 50 SE +/- 0.29, N = 3 45.75 MIN: 41.27 / MAX: 46.82
PyTorch Device: CPU - Batch Size: 32 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 32 - Model: ResNet-152 phoronix-ml.txt 4 8 12 16 20 SE +/- 0.03, N = 3 17.78 MIN: 15.02 / MAX: 18.13
PyTorch Device: CPU - Batch Size: 256 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 256 - Model: ResNet-50 phoronix-ml.txt 10 20 30 40 50 SE +/- 0.23, N = 3 46.06 MIN: 38.74 / MAX: 46.92
PyTorch Device: CPU - Batch Size: 16 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 16 - Model: ResNet-152 phoronix-ml.txt 4 8 12 16 20 SE +/- 0.07, N = 3 17.97 MIN: 14.56 / MAX: 18.28
PyTorch Device: CPU - Batch Size: 64 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 64 - Model: ResNet-50 phoronix-ml.txt 11 22 33 44 55 SE +/- 0.10, N = 3 46.69 MIN: 42.52 / MAX: 47.36
PyTorch Device: CPU - Batch Size: 32 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 32 - Model: ResNet-50 phoronix-ml.txt 11 22 33 44 55 SE +/- 0.47, N = 3 46.42 MIN: 41.66 / MAX: 47.49
PyTorch Device: CPU - Batch Size: 16 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 16 - Model: ResNet-50 phoronix-ml.txt 10 20 30 40 50 SE +/- 0.20, N = 3 45.59 MIN: 38.82 / MAX: 46.87
PyTorch Device: CPU - Batch Size: 1 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 1 - Model: ResNet-152 phoronix-ml.txt 6 12 18 24 30 SE +/- 0.14, N = 3 23.19 MIN: 19.02 / MAX: 24.35
PyTorch Device: CPU - Batch Size: 1 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 1 - Model: ResNet-50 phoronix-ml.txt 13 26 39 52 65 SE +/- 0.03, N = 3 60.17 MIN: 49.64 / MAX: 62.8
TensorFlow Lite Model: Inception ResNet V2 OpenBenchmarking.org Microseconds, Fewer Is Better TensorFlow Lite 2022-05-18 Model: Inception ResNet V2 phoronix-ml.txt 7K 14K 21K 28K 35K SE +/- 434.39, N = 3 33356.3
TensorFlow Lite Model: Mobilenet Quant OpenBenchmarking.org Microseconds, Fewer Is Better TensorFlow Lite 2022-05-18 Model: Mobilenet Quant phoronix-ml.txt 500 1000 1500 2000 2500 SE +/- 12.96, N = 3 2501.21
TensorFlow Lite Model: Mobilenet Float OpenBenchmarking.org Microseconds, Fewer Is Better TensorFlow Lite 2022-05-18 Model: Mobilenet Float phoronix-ml.txt 300 600 900 1200 1500 SE +/- 4.96, N = 3 1381.25
TensorFlow Lite Model: NASNet Mobile OpenBenchmarking.org Microseconds, Fewer Is Better TensorFlow Lite 2022-05-18 Model: NASNet Mobile phoronix-ml.txt 7K 14K 21K 28K 35K SE +/- 419.25, N = 15 33662.5
TensorFlow Lite Model: SqueezeNet OpenBenchmarking.org Microseconds, Fewer Is Better TensorFlow Lite 2022-05-18 Model: SqueezeNet phoronix-ml.txt 400 800 1200 1600 2000 SE +/- 17.45, N = 15 1836.28
RNNoise Input: 26 Minute Long Talking Sample OpenBenchmarking.org Seconds, Fewer Is Better RNNoise 0.2 Input: 26 Minute Long Talking Sample phoronix-ml.txt 2 4 6 8 10 SE +/- 0.019, N = 3 7.852 1. (CC) gcc options: -O2 -pedantic -fvisibility=hidden
R Benchmark OpenBenchmarking.org Seconds, Fewer Is Better R Benchmark phoronix-ml.txt 0.0282 0.0564 0.0846 0.1128 0.141 SE +/- 0.0007, N = 3 0.1252
DeepSpeech Acceleration: CPU OpenBenchmarking.org Seconds, Fewer Is Better DeepSpeech 0.6 Acceleration: CPU phoronix-ml.txt 10 20 30 40 50 SE +/- 0.15, N = 3 46.23
Numpy Benchmark OpenBenchmarking.org Score, More Is Better Numpy Benchmark phoronix-ml.txt 150 300 450 600 750 SE +/- 5.45, N = 3 715.50
oneDNN Harness: Recurrent Neural Network Inference - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 3.6 Harness: Recurrent Neural Network Inference - Engine: CPU phoronix-ml.txt 160 320 480 640 800 SE +/- 8.66, N = 3 736.40 MIN: 639.28 1. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -fcf-protection=full -pie -ldl
oneDNN Harness: Recurrent Neural Network Training - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 3.6 Harness: Recurrent Neural Network Training - Engine: CPU phoronix-ml.txt 300 600 900 1200 1500 SE +/- 9.63, N = 3 1261.40 MIN: 1196.78 1. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -fcf-protection=full -pie -ldl
oneDNN Harness: Deconvolution Batch shapes_3d - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 3.6 Harness: Deconvolution Batch shapes_3d - Engine: CPU phoronix-ml.txt 0.4167 0.8334 1.2501 1.6668 2.0835 SE +/- 0.01905, N = 4 1.85206 MIN: 1.73 1. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -fcf-protection=full -pie -ldl
oneDNN Harness: Deconvolution Batch shapes_1d - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 3.6 Harness: Deconvolution Batch shapes_1d - Engine: CPU phoronix-ml.txt 0.8495 1.699 2.5485 3.398 4.2475 SE +/- 0.00801, N = 3 3.77567 MIN: 2.81 1. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -fcf-protection=full -pie -ldl
oneDNN Harness: Convolution Batch Shapes Auto - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 3.6 Harness: Convolution Batch Shapes Auto - Engine: CPU phoronix-ml.txt 0.5317 1.0634 1.5951 2.1268 2.6585 SE +/- 0.02737, N = 4 2.36317 MIN: 1.97 1. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -fcf-protection=full -pie -ldl
oneDNN Harness: IP Shapes 3D - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 3.6 Harness: IP Shapes 3D - Engine: CPU phoronix-ml.txt 0.3141 0.6282 0.9423 1.2564 1.5705 SE +/- 0.01618, N = 15 1.39591 MIN: 1.16 1. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -fcf-protection=full -pie -ldl
oneDNN Harness: IP Shapes 1D - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 3.6 Harness: IP Shapes 1D - Engine: CPU phoronix-ml.txt 0.2557 0.5114 0.7671 1.0228 1.2785 SE +/- 0.00979, N = 15 1.13657 MIN: 1.01 1. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -fcf-protection=full -pie -ldl
SHOC Scalable HeterOgeneous Computing Target: OpenCL - Benchmark: Texture Read Bandwidth OpenBenchmarking.org GB/s, More Is Better SHOC Scalable HeterOgeneous Computing 2020-04-17 Target: OpenCL - Benchmark: Texture Read Bandwidth phoronix-ml.txt 200 400 600 800 1000 SE +/- 5.65, N = 3 1003.32 1. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi
SHOC Scalable HeterOgeneous Computing Target: OpenCL - Benchmark: Bus Speed Readback OpenBenchmarking.org GB/s, More Is Better SHOC Scalable HeterOgeneous Computing 2020-04-17 Target: OpenCL - Benchmark: Bus Speed Readback phoronix-ml.txt 6 12 18 24 30 SE +/- 0.00, N = 3 26.25 1. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi
SHOC Scalable HeterOgeneous Computing Target: OpenCL - Benchmark: Bus Speed Download OpenBenchmarking.org GB/s, More Is Better SHOC Scalable HeterOgeneous Computing 2020-04-17 Target: OpenCL - Benchmark: Bus Speed Download phoronix-ml.txt 6 12 18 24 30 SE +/- 0.00, N = 3 24.99 1. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi
SHOC Scalable HeterOgeneous Computing Target: OpenCL - Benchmark: Max SP Flops OpenBenchmarking.org GFLOPS, More Is Better SHOC Scalable HeterOgeneous Computing 2020-04-17 Target: OpenCL - Benchmark: Max SP Flops phoronix-ml.txt 20K 40K 60K 80K 100K SE +/- 230.38, N = 3 93757.3 1. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi
OpenCV Test: DNN - Deep Neural Network OpenBenchmarking.org ms, Fewer Is Better OpenCV 4.7 Test: DNN - Deep Neural Network phoronix-ml.txt 7K 14K 21K 28K 35K SE +/- 1066.17, N = 15 33080 1. (CXX) g++ options: -fsigned-char -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections -msse -msse2 -msse3 -fvisibility=hidden -O3 -ldl -lm -lpthread -lrt
Scikit-Learn Benchmark: Plot Parallel Pairwise OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot Parallel Pairwise phoronix-ml.txt 40 80 120 160 200 SE +/- 4.47, N = 9 168.00 1. (F9X) gfortran options: -O0
ONNX Runtime Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Standard phoronix-ml.txt 100 200 300 400 500 SE +/- 1.06, N = 3 452.31 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Parallel phoronix-ml.txt 170 340 510 680 850 SE +/- 2.03, N = 3 770.91 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: super-resolution-10 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: super-resolution-10 - Device: CPU - Executor: Standard phoronix-ml.txt 3 6 9 12 15 SE +/- 0.02, N = 3 10.28 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: super-resolution-10 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: super-resolution-10 - Device: CPU - Executor: Parallel phoronix-ml.txt 2 4 6 8 10 SE +/- 0.06021, N = 3 8.08149 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard phoronix-ml.txt 0.6852 1.3704 2.0556 2.7408 3.426 SE +/- 0.00627, N = 3 3.04551 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Parallel phoronix-ml.txt 3 6 9 12 15 SE +/- 0.03096, N = 3 9.18292 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: fcn-resnet101-11 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: fcn-resnet101-11 - Device: CPU - Executor: Standard phoronix-ml.txt 50 100 150 200 250 SE +/- 0.28, N = 3 242.91 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: fcn-resnet101-11 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: fcn-resnet101-11 - Device: CPU - Executor: Parallel phoronix-ml.txt 200 400 600 800 1000 SE +/- 16.79, N = 12 834.57 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: fcn-resnet101-11 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: fcn-resnet101-11 - Device: CPU - Executor: Parallel phoronix-ml.txt 0.2708 0.5416 0.8124 1.0832 1.354 SE +/- 0.02346, N = 12 1.20339 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard phoronix-ml.txt 0.3516 0.7032 1.0548 1.4064 1.758 SE +/- 0.01747, N = 3 1.56253 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: CaffeNet 12-int8 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: CaffeNet 12-int8 - Device: CPU - Executor: Parallel phoronix-ml.txt 1.1087 2.2174 3.3261 4.4348 5.5435 SE +/- 0.04054, N = 15 4.92768 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: T5 Encoder - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: T5 Encoder - Device: CPU - Executor: Standard phoronix-ml.txt 1.3084 2.6168 3.9252 5.2336 6.542 SE +/- 0.07434, N = 4 5.81489 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: T5 Encoder - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: T5 Encoder - Device: CPU - Executor: Parallel phoronix-ml.txt 0.8264 1.6528 2.4792 3.3056 4.132 SE +/- 0.01909, N = 3 3.67307 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ZFNet-512 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: ZFNet-512 - Device: CPU - Executor: Standard phoronix-ml.txt 3 6 9 12 15 SE +/- 0.10702, N = 4 9.10376 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ZFNet-512 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: ZFNet-512 - Device: CPU - Executor: Parallel phoronix-ml.txt 4 8 12 16 20 SE +/- 0.20, N = 4 17.29 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: yolov4 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: yolov4 - Device: CPU - Executor: Standard phoronix-ml.txt 20 40 60 80 100 SE +/- 0.38, N = 3 103.58 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: yolov4 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: yolov4 - Device: CPU - Executor: Parallel phoronix-ml.txt 40 80 120 160 200 SE +/- 2.57, N = 3 185.36 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
OpenVINO Model: Noise Suppression Poconet-Like FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Noise Suppression Poconet-Like FP16 - Device: CPU phoronix-ml.txt 400 800 1200 1600 2000 SE +/- 35.59, N = 15 2052.02 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Machine Translation EN To DE FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Machine Translation EN To DE FP16 - Device: CPU phoronix-ml.txt 40 80 120 160 200 SE +/- 3.37, N = 12 187.88 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Road Segmentation ADAS FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Road Segmentation ADAS FP16 - Device: CPU phoronix-ml.txt 6 12 18 24 30 SE +/- 0.43, N = 15 25.83 MIN: 10.2 / MAX: 57.07 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Road Segmentation ADAS FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Road Segmentation ADAS FP16 - Device: CPU phoronix-ml.txt 100 200 300 400 500 SE +/- 9.05, N = 15 465.86 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Person Detection FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Person Detection FP16 - Device: CPU phoronix-ml.txt 20 40 60 80 100 SE +/- 2.03, N = 15 93.29 MIN: 31.12 / MAX: 185.66 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Person Detection FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Person Detection FP16 - Device: CPU phoronix-ml.txt 30 60 90 120 150 SE +/- 3.20, N = 15 129.45 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
NCNN Target: Vulkan GPU - Model: FastestDet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: FastestDet phoronix-ml.txt 3 6 9 12 15 SE +/- 0.39, N = 3 9.82 MIN: 8.74 / MAX: 19.33 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: squeezenet_ssd OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: squeezenet_ssd phoronix-ml.txt 4 8 12 16 20 SE +/- 1.72, N = 3 16.04 MIN: 13.26 / MAX: 580.79 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: resnet50 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: resnet50 phoronix-ml.txt 4 8 12 16 20 SE +/- 1.16, N = 3 14.65 MIN: 12.41 / MAX: 377.47 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: efficientnet-b0 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: efficientnet-b0 phoronix-ml.txt 2 4 6 8 10 SE +/- 0.37, N = 3 8.68 MIN: 7.76 / MAX: 202.36 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: FastestDet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: FastestDet phoronix-ml.txt 3 6 9 12 15 SE +/- 0.30, N = 15 9.80 MIN: 6.73 / MAX: 273.49 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: mnasnet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: mnasnet phoronix-ml.txt 2 4 6 8 10 SE +/- 0.11, N = 15 6.11 MIN: 5.26 / MAX: 321.29 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
TensorFlow Lite Model: Inception V4 OpenBenchmarking.org Microseconds, Fewer Is Better TensorFlow Lite 2022-05-18 Model: Inception V4 phoronix-ml.txt 4K 8K 12K 16K 20K SE +/- 520.83, N = 15 20372.6
LeelaChessZero Backend: BLAS OpenBenchmarking.org Nodes Per Second, More Is Better LeelaChessZero 0.31.1 Backend: BLAS phoronix-ml.txt 40 80 120 160 200 SE +/- 12.39, N = 9 184 1. (CXX) g++ options: -flto -pthread
Phoronix Test Suite v10.8.5