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.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2411137-NE-PHORONIXM28
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phoronix-ml.txt
November 10
  3 Days, 15 Hours, 7 Minutes
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phoronix-machine-learning.txtOpenBenchmarking.orgPhoronix Test SuiteAMD Ryzen Threadripper 7960X 24-Cores @ 7.79GHz (24 Cores / 48 Threads)Gigabyte TRX50 AERO D (FA BIOS)AMD Device 14a44 x 32GB DDR5-5200MT/s Micron MTC20F1045S1RC56BG11000GB GIGABYTE AG512K1TBSapphire AMD Radeon RX 7900 XTX 24GBAMD Device 14ccHP E273Aquantia AQC113C NBase-T/IEEE + Realtek RTL8125 2.5GbE + Qualcomm WCN785x Wi-Fi 7Ubuntu 24.046.8.0-48-generic (x86_64)GNOME Shell 46.0X Server + Wayland4.6 Mesa 24.2.0-devel (LLVM 18.1.7 DRM 3.58)OpenCL 2.1 AMD-APP (3625.0)GCC 13.2.0ext41920x1080ProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDesktopDisplay ServerOpenGLOpenCLCompilerFile-SystemScreen ResolutionPhoronix-machine-learning.txt BenchmarksSystem Logs- 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.txttensorflow: CPU - 1 - VGG-16tensorflow: GPU - 1 - VGG-16tensorflow: CPU - 1 - AlexNettensorflow: CPU - 16 - VGG-16tensorflow: CPU - 32 - VGG-16tensorflow: CPU - 64 - VGG-16tensorflow: GPU - 1 - AlexNettensorflow: GPU - 16 - VGG-16tensorflow: GPU - 32 - VGG-16tensorflow: GPU - 64 - VGG-16tensorflow: CPU - 16 - AlexNettensorflow: CPU - 256 - VGG-16tensorflow: CPU - 32 - AlexNettensorflow: CPU - 512 - VGG-16tensorflow: CPU - 64 - AlexNettensorflow: GPU - 16 - AlexNettensorflow: GPU - 256 - VGG-16tensorflow: GPU - 32 - AlexNettensorflow: GPU - 512 - VGG-16tensorflow: GPU - 64 - AlexNettensorflow: CPU - 1 - GoogLeNettensorflow: CPU - 1 - ResNet-50tensorflow: CPU - 256 - AlexNettensorflow: CPU - 512 - AlexNettensorflow: GPU - 1 - GoogLeNettensorflow: GPU - 1 - ResNet-50tensorflow: GPU - 256 - AlexNettensorflow: GPU - 512 - AlexNettensorflow: CPU - 16 - GoogLeNettensorflow: CPU - 16 - ResNet-50tensorflow: CPU - 32 - GoogLeNettensorflow: CPU - 32 - ResNet-50tensorflow: CPU - 64 - GoogLeNettensorflow: CPU - 64 - ResNet-50tensorflow: GPU - 16 - GoogLeNettensorflow: GPU - 16 - ResNet-50tensorflow: GPU - 32 - GoogLeNettensorflow: GPU - 32 - ResNet-50tensorflow: GPU - 64 - GoogLeNettensorflow: GPU - 64 - ResNet-50tensorflow: CPU - 256 - GoogLeNettensorflow: CPU - 256 - ResNet-50tensorflow: CPU - 512 - GoogLeNettensorflow: CPU - 512 - ResNet-50tensorflow: GPU - 256 - GoogLeNettensorflow: GPU - 256 - ResNet-50tensorflow: GPU - 512 - GoogLeNettensorflow: GPU - 512 - ResNet-50lczero: BLASscikit-learn: GLMscikit-learn: SAGAscikit-learn: Treescikit-learn: Lassoscikit-learn: Sparsifyscikit-learn: Plot Wardscikit-learn: MNIST Datasetscikit-learn: Plot Neighborsscikit-learn: SGD Regressionscikit-learn: SGDOneClassSVMscikit-learn: Isolation Forestscikit-learn: Text Vectorizersscikit-learn: Plot Hierarchicalscikit-learn: Plot OMP vs. LARSscikit-learn: Feature Expansionsscikit-learn: LocalOutlierFactorscikit-learn: TSNE MNIST Datasetscikit-learn: Isotonic / Logisticscikit-learn: Plot Incremental PCAscikit-learn: Hist Gradient Boostingscikit-learn: Plot Parallel Pairwisescikit-learn: Isotonic / Pathologicalscikit-learn: Sample Without Replacementscikit-learn: Covertype Dataset Benchmarkscikit-learn: Hist Gradient Boosting Adultscikit-learn: Isotonic / Perturbed Logarithmscikit-learn: Hist Gradient Boosting Threadingscikit-learn: Hist Gradient Boosting Higgs Bosonscikit-learn: 20 Newsgroups / Logistic Regressionscikit-learn: Plot Polynomial Kernel Approximationscikit-learn: Hist Gradient Boosting Categorical Onlyscikit-learn: Kernel PCA Solvers / Time vs. N Samplesscikit-learn: Kernel PCA Solvers / Time vs. N Componentsscikit-learn: Sparse Rand Projections / 100 Iterationsrbenchmark: numpy: deepspeech: CPUrnnoise: 26 Minute Long Talking Samplemnn: nasnetmnn: mobilenetV3mnn: squeezenetv1.1mnn: resnet-v2-50mnn: SqueezeNetV1.0mnn: MobileNetV2_224mnn: mobilenet-v1-1.0mnn: inception-v3onnx: yolov4 - CPU - Parallelonnx: yolov4 - CPU - Standardonnx: ZFNet-512 - CPU - Parallelonnx: ZFNet-512 - CPU - Standardonnx: T5 Encoder - CPU - Parallelonnx: T5 Encoder - CPU - Standardonnx: CaffeNet 12-int8 - CPU - Parallelonnx: CaffeNet 12-int8 - CPU - Standardonnx: fcn-resnet101-11 - CPU - Parallelonnx: fcn-resnet101-11 - CPU - Standardonnx: ResNet50 v1-12-int8 - CPU - Parallelonnx: ResNet50 v1-12-int8 - CPU - Standardonnx: super-resolution-10 - CPU - Parallelonnx: super-resolution-10 - CPU - Standardonnx: ResNet101_DUC_HDC-12 - CPU - Parallelonnx: ResNet101_DUC_HDC-12 - CPU - Standardopencv: DNN - Deep Neural Networkpytorch: CPU - 1 - ResNet-50pytorch: CPU - 1 - ResNet-152pytorch: CPU - 16 - ResNet-50pytorch: CPU - 32 - ResNet-50pytorch: CPU - 64 - ResNet-50pytorch: CPU - 16 - ResNet-152pytorch: CPU - 256 - ResNet-50pytorch: CPU - 32 - ResNet-152pytorch: CPU - 512 - ResNet-50pytorch: CPU - 64 - ResNet-152pytorch: CPU - 256 - ResNet-152pytorch: CPU - 512 - ResNet-152pytorch: CPU - 1 - Efficientnet_v2_lpytorch: CPU - 16 - Efficientnet_v2_lpytorch: CPU - 32 - Efficientnet_v2_lpytorch: CPU - 64 - Efficientnet_v2_lpytorch: CPU - 256 - Efficientnet_v2_lpytorch: CPU - 512 - Efficientnet_v2_ltensorflow-lite: SqueezeNettensorflow-lite: Inception V4tensorflow-lite: NASNet Mobiletensorflow-lite: Mobilenet Floattensorflow-lite: Mobilenet Quanttensorflow-lite: Inception ResNet V2whisper-cpp: ggml-base.en - 2016 State of the Unionwhisper-cpp: ggml-small.en - 2016 State of the Unionwhisper-cpp: ggml-medium.en - 2016 State of the Unionxnnpack: FP32MobileNetV1xnnpack: FP32MobileNetV2xnnpack: FP32MobileNetV3Largexnnpack: FP32MobileNetV3Smallxnnpack: FP16MobileNetV1xnnpack: FP16MobileNetV2xnnpack: FP16MobileNetV3Largexnnpack: FP16MobileNetV3Smallxnnpack: QS8MobileNetV2shoc: OpenCL - S3Dshoc: OpenCL - Triadshoc: OpenCL - FFT SPshoc: OpenCL - MD5 Hashshoc: OpenCL - Reductionshoc: OpenCL - GEMM SGEMM_Nshoc: OpenCL - Max SP Flopsshoc: OpenCL - Bus Speed Downloadshoc: OpenCL - Bus Speed Readbackshoc: OpenCL - Texture Read Bandwidthncnn: CPU - mobilenetncnn: CPU-v2-v2 - mobilenet-v2ncnn: CPU-v3-v3 - mobilenet-v3ncnn: CPU - shufflenet-v2ncnn: CPU - mnasnetncnn: CPU - efficientnet-b0ncnn: CPU - blazefacencnn: CPU - googlenetncnn: CPU - vgg16ncnn: CPU - resnet18ncnn: CPU - alexnetncnn: CPU - resnet50ncnn: CPUv2-yolov3v2-yolov3 - mobilenetv2-yolov3ncnn: CPU - yolov4-tinyncnn: CPU - squeezenet_ssdncnn: CPU - regnety_400mncnn: CPU - vision_transformerncnn: CPU - FastestDetncnn: Vulkan GPU - mobilenetncnn: Vulkan GPU-v2-v2 - mobilenet-v2ncnn: Vulkan GPU-v3-v3 - mobilenet-v3ncnn: Vulkan GPU - shufflenet-v2ncnn: Vulkan GPU - mnasnetncnn: Vulkan GPU - efficientnet-b0ncnn: Vulkan GPU - blazefacencnn: Vulkan GPU - googlenetncnn: Vulkan GPU - vgg16ncnn: Vulkan GPU - resnet18ncnn: Vulkan GPU - alexnetncnn: Vulkan GPU - resnet50ncnn: Vulkan GPUv2-yolov3v2-yolov3 - mobilenetv2-yolov3ncnn: Vulkan GPU - yolov4-tinyncnn: Vulkan GPU - squeezenet_ssdncnn: Vulkan GPU - regnety_400mncnn: Vulkan GPU - vision_transformerncnn: Vulkan GPU - FastestDetonednn: IP Shapes 1D - CPUonednn: IP Shapes 3D - CPUonednn: Convolution Batch Shapes Auto - CPUonednn: Deconvolution Batch shapes_1d - CPUonednn: Deconvolution Batch shapes_3d - CPUonednn: Recurrent Neural Network Training - CPUonednn: Recurrent Neural Network Inference - CPUopenvino: Face Detection FP16 - CPUopenvino: Face Detection FP16 - CPUopenvino: Person Detection FP16 - CPUopenvino: Person Detection FP16 - CPUopenvino: Person Detection FP32 - CPUopenvino: Person Detection FP32 - CPUopenvino: Vehicle Detection FP16 - CPUopenvino: Vehicle Detection FP16 - CPUopenvino: Face Detection FP16-INT8 - CPUopenvino: Face Detection FP16-INT8 - CPUopenvino: Face Detection Retail FP16 - CPUopenvino: Face Detection Retail FP16 - CPUopenvino: Road Segmentation ADAS FP16 - CPUopenvino: Road Segmentation ADAS FP16 - CPUopenvino: Vehicle Detection FP16-INT8 - CPUopenvino: Vehicle Detection FP16-INT8 - CPUopenvino: Weld Porosity Detection FP16 - CPUopenvino: Weld Porosity Detection FP16 - CPUopenvino: Face Detection Retail FP16-INT8 - CPUopenvino: Face Detection Retail FP16-INT8 - CPUopenvino: Road Segmentation ADAS FP16-INT8 - CPUopenvino: Road Segmentation ADAS FP16-INT8 - CPUopenvino: Machine Translation EN To DE FP16 - CPUopenvino: Machine Translation EN To DE FP16 - CPUopenvino: Weld Porosity Detection FP16-INT8 - CPUopenvino: Weld Porosity Detection FP16-INT8 - CPUopenvino: Person Vehicle Bike Detection FP16 - CPUopenvino: Person Vehicle Bike Detection FP16 - CPUopenvino: Noise Suppression Poconet-Like FP16 - CPUopenvino: Noise Suppression Poconet-Like FP16 - CPUopenvino: Handwritten English Recognition FP16 - CPUopenvino: Handwritten English Recognition FP16 - CPUopenvino: Person Re-Identification Retail FP16 - CPUopenvino: Person Re-Identification Retail FP16 - CPUopenvino: Age Gender Recognition Retail 0013 FP16 - CPUopenvino: Age Gender Recognition Retail 0013 FP16 - CPUopenvino: Handwritten English Recognition FP16-INT8 - CPUopenvino: Handwritten English Recognition FP16-INT8 - CPUopenvino: Age Gender Recognition Retail 0013 FP16-INT8 - CPUopenvino: Age Gender Recognition Retail 0013 FP16-INT8 - CPUonnx: yolov4 - CPU - Parallelonnx: yolov4 - CPU - Standardonnx: ZFNet-512 - CPU - Parallelonnx: ZFNet-512 - CPU - Standardonnx: T5 Encoder - CPU - Parallelonnx: T5 Encoder - CPU - Standardonnx: CaffeNet 12-int8 - CPU - Parallelonnx: CaffeNet 12-int8 - CPU - Standardonnx: fcn-resnet101-11 - CPU - Parallelonnx: fcn-resnet101-11 - CPU - Standardonnx: ResNet50 v1-12-int8 - CPU - Parallelonnx: ResNet50 v1-12-int8 - CPU - Standardonnx: super-resolution-10 - CPU - Parallelonnx: super-resolution-10 - CPU - Standardonnx: ResNet101_DUC_HDC-12 - CPU - Parallelonnx: ResNet101_DUC_HDC-12 - CPU - Standardphoronix-ml.txt9.702.2030.6627.3428.3229.0515.382.512.542.55288.7130.16409.5630.43516.1842.432.5546.142.5547.8560.9218.38627.50643.4421.036.6948.9249.28198.1162.29218.0767.94225.1670.8926.918.9427.919.1428.539.24227.0659.70185.2558.5029.039.3628.739.34184168.333669.11846.970308.225108.45542.10452.736114.83964.368233.407176.28745.340141.46341.476100.35321.616247.5561406.45231.209166.776167.9973843.25890.640320.394153.3381528.96652.72965.73610.450104.70030.18861.61131.037504.8290.1252715.5046.234757.85215.2972.5364.32718.5346.4293.2683.78436.4525.396849.6544257.8612109.863272.184172.037203.059639.9051.203394.11676108.875328.293123.73697.31771.297182.210863308060.1723.1945.5946.4246.6917.9746.0617.7845.7517.6417.9217.9914.189.909.859.9810.119.941836.2820372.633662.51381.252501.2133356.392.75261218.18523579.17077123318732465150311441495212814641398289.54313.8158752.83746.508442.94497615.3593757.324.989326.25251003.32113.856.306.458.156.118.283.1116.4225.718.025.5613.3313.8524.1414.3418.5840.599.8013.796.316.498.065.998.683.1416.0125.137.865.2814.6513.7923.6416.0418.6341.059.821.136571.395912.363173.775671.852061261.40736.40019.69607.48129.4593.29119.41100.461077.8711.1237.36320.424273.092.76465.8625.832160.575.511947.6712.256458.383.58679.6617.62187.8863.993742.776.311930.086.182052.0211.531035.2923.122523.594.7248433.920.431108.6521.5867537.770.3185.360103.57917.28759.103763.673075.814894.927681.56253834.572242.9089.182923.045518.0814910.2754770.906452.314OpenBenchmarking.org

TensorFlow

This is a benchmark of the TensorFlow deep learning framework using the TensorFlow reference benchmarks (tensorflow/benchmarks with tf_cnn_benchmarks.py). Note with the Phoronix Test Suite there is also pts/tensorflow-lite for benchmarking the TensorFlow Lite binaries if desired for complementary metrics. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 1 - Model: VGG-16phoronix-ml.txt3691215SE +/- 0.00, N = 39.70

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 1 - Model: VGG-16phoronix-ml.txt0.4950.991.4851.982.475SE +/- 0.00, N = 32.20

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 1 - Model: AlexNetphoronix-ml.txt714212835SE +/- 0.00, N = 330.66

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: VGG-16phoronix-ml.txt612182430SE +/- 0.05, N = 327.34

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 32 - Model: VGG-16phoronix-ml.txt714212835SE +/- 0.05, N = 328.32

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 64 - Model: VGG-16phoronix-ml.txt714212835SE +/- 0.02, N = 329.05

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 1 - Model: AlexNetphoronix-ml.txt48121620SE +/- 0.14, N = 1515.38

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 16 - Model: VGG-16phoronix-ml.txt0.56481.12961.69442.25922.824SE +/- 0.00, N = 32.51

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 32 - Model: VGG-16phoronix-ml.txt0.57151.1431.71452.2862.8575SE +/- 0.00, N = 32.54

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 64 - Model: VGG-16phoronix-ml.txt0.57381.14761.72142.29522.869SE +/- 0.00, N = 32.55

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: AlexNetphoronix-ml.txt60120180240300SE +/- 0.39, N = 3288.71

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 256 - Model: VGG-16phoronix-ml.txt714212835SE +/- 0.08, N = 330.16

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 32 - Model: AlexNetphoronix-ml.txt90180270360450SE +/- 0.28, N = 3409.56

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 512 - Model: VGG-16phoronix-ml.txt714212835SE +/- 0.01, N = 330.43

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 64 - Model: AlexNetphoronix-ml.txt110220330440550SE +/- 0.16, N = 3516.18

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 16 - Model: AlexNetphoronix-ml.txt1020304050SE +/- 0.02, N = 342.43

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 256 - Model: VGG-16phoronix-ml.txt0.57381.14761.72142.29522.869SE +/- 0.01, N = 32.55

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 32 - Model: AlexNetphoronix-ml.txt1020304050SE +/- 0.04, N = 346.14

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 512 - Model: VGG-16phoronix-ml.txt0.57381.14761.72142.29522.869SE +/- 0.00, N = 32.55

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 64 - Model: AlexNetphoronix-ml.txt1122334455SE +/- 0.04, N = 347.85

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 1 - Model: GoogLeNetphoronix-ml.txt1428425670SE +/- 0.30, N = 360.92

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 1 - Model: ResNet-50phoronix-ml.txt510152025SE +/- 0.11, N = 318.38

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 256 - Model: AlexNetphoronix-ml.txt140280420560700SE +/- 0.20, N = 3627.50

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 512 - Model: AlexNetphoronix-ml.txt140280420560700SE +/- 1.19, N = 3643.44

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 1 - Model: GoogLeNetphoronix-ml.txt510152025SE +/- 0.15, N = 321.03

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 1 - Model: ResNet-50phoronix-ml.txt246810SE +/- 0.03, N = 36.69

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 256 - Model: AlexNetphoronix-ml.txt1122334455SE +/- 0.03, N = 348.92

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 512 - Model: AlexNetphoronix-ml.txt1122334455SE +/- 0.09, N = 349.28

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: GoogLeNetphoronix-ml.txt4080120160200SE +/- 0.22, N = 3198.11

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: ResNet-50phoronix-ml.txt1428425670SE +/- 0.06, N = 362.29

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 32 - Model: GoogLeNetphoronix-ml.txt50100150200250SE +/- 0.23, N = 3218.07

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 32 - Model: ResNet-50phoronix-ml.txt1530456075SE +/- 0.07, N = 367.94

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 64 - Model: GoogLeNetphoronix-ml.txt50100150200250SE +/- 0.29, N = 3225.16

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 64 - Model: ResNet-50phoronix-ml.txt1632486480SE +/- 0.33, N = 370.89

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 16 - Model: GoogLeNetphoronix-ml.txt612182430SE +/- 0.01, N = 326.91

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 16 - Model: ResNet-50phoronix-ml.txt246810SE +/- 0.01, N = 38.94

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 32 - Model: GoogLeNetphoronix-ml.txt714212835SE +/- 0.04, N = 327.91

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 32 - Model: ResNet-50phoronix-ml.txt3691215SE +/- 0.01, N = 39.14

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 64 - Model: GoogLeNetphoronix-ml.txt714212835SE +/- 0.02, N = 328.53

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 64 - Model: ResNet-50phoronix-ml.txt3691215SE +/- 0.00, N = 39.24

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 256 - Model: GoogLeNetphoronix-ml.txt50100150200250SE +/- 0.25, N = 3227.06

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 256 - Model: ResNet-50phoronix-ml.txt1326395265SE +/- 0.91, N = 959.70

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 512 - Model: GoogLeNetphoronix-ml.txt4080120160200SE +/- 1.69, N = 7185.25

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 512 - Model: ResNet-50phoronix-ml.txt1326395265SE +/- 0.33, N = 358.50

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 256 - Model: GoogLeNetphoronix-ml.txt714212835SE +/- 0.01, N = 329.03

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 256 - Model: ResNet-50phoronix-ml.txt3691215SE +/- 0.01, N = 39.36

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 512 - Model: GoogLeNetphoronix-ml.txt714212835SE +/- 0.10, N = 328.73

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 512 - Model: ResNet-50phoronix-ml.txt3691215SE +/- 0.03, N = 39.34

PlaidML

This test profile uses PlaidML deep learning framework developed by Intel for offering up various benchmarks. Learn more via the OpenBenchmarking.org test page.

FP16: No - Mode: Inference - Network: VGG16 - Device: CPU

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FP16: No - Mode: Inference - Network: ResNet 50 - Device: CPU

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LeelaChessZero

OpenBenchmarking.orgNodes Per Second, More Is BetterLeelaChessZero 0.31.1Backend: BLASphoronix-ml.txt4080120160200SE +/- 12.39, N = 91841. (CXX) g++ options: -flto -pthread

Numenta Anomaly Benchmark

Numenta Anomaly Benchmark (NAB) is a benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is comprised of over 50 labeled real-world and artificial time-series data files plus a novel scoring mechanism designed for real-time applications. This test profile currently measures the time to run various detectors. Learn more via the OpenBenchmarking.org test page.

Detector: KNN CAD

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Detector: Relative Entropy

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Detector: Windowed Gaussian

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Detector: Earthgecko Skyline

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Detector: Bayesian Changepoint

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Detector: Contextual Anomaly Detector OSE

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Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: GLMphoronix-ml.txt4080120160200SE +/- 0.81, N = 3168.331. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SAGAphoronix-ml.txt140280420560700SE +/- 3.66, N = 3669.121. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Treephoronix-ml.txt1122334455SE +/- 0.48, N = 546.971. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Lassophoronix-ml.txt70140210280350SE +/- 0.07, N = 3308.231. (F9X) gfortran options: -O0

Benchmark: Glmnet

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OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sparsifyphoronix-ml.txt20406080100SE +/- 0.30, N = 3108.461. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Wardphoronix-ml.txt1020304050SE +/- 0.35, N = 842.101. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: MNIST Datasetphoronix-ml.txt1224364860SE +/- 0.42, N = 352.741. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Neighborsphoronix-ml.txt306090120150SE +/- 0.47, N = 3114.841. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SGD Regressionphoronix-ml.txt1428425670SE +/- 0.08, N = 364.371. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SGDOneClassSVMphoronix-ml.txt50100150200250SE +/- 0.33, N = 3233.411. (F9X) gfortran options: -O0

Benchmark: Plot Lasso Path

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OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Isolation Forestphoronix-ml.txt4080120160200SE +/- 0.54, N = 3176.291. (F9X) gfortran options: -O0

Benchmark: Plot Fast KMeans

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OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Text Vectorizersphoronix-ml.txt1020304050SE +/- 0.18, N = 345.341. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Hierarchicalphoronix-ml.txt306090120150SE +/- 0.41, N = 3141.461. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot OMP vs. LARSphoronix-ml.txt918273645SE +/- 0.09, N = 341.481. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Feature Expansionsphoronix-ml.txt20406080100SE +/- 0.56, N = 3100.351. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: LocalOutlierFactorphoronix-ml.txt510152025SE +/- 0.13, N = 321.621. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: TSNE MNIST Datasetphoronix-ml.txt50100150200250SE +/- 0.66, N = 3247.561. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Isotonic / Logisticphoronix-ml.txt30060090012001500SE +/- 0.82, N = 31406.451. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Incremental PCAphoronix-ml.txt714212835SE +/- 0.08, N = 331.211. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boostingphoronix-ml.txt4080120160200SE +/- 0.90, N = 3166.781. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Parallel Pairwisephoronix-ml.txt4080120160200SE +/- 4.47, N = 9168.001. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Isotonic / Pathologicalphoronix-ml.txt8001600240032004000SE +/- 9.06, N = 33843.261. (F9X) gfortran options: -O0

Benchmark: RCV1 Logreg Convergencet

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OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sample Without Replacementphoronix-ml.txt20406080100SE +/- 0.76, N = 390.641. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Covertype Dataset Benchmarkphoronix-ml.txt70140210280350SE +/- 0.61, N = 3320.391. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Adultphoronix-ml.txt306090120150SE +/- 1.23, N = 12153.341. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Isotonic / Perturbed Logarithmphoronix-ml.txt30060090012001500SE +/- 2.36, N = 31528.971. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Threadingphoronix-ml.txt1224364860SE +/- 0.61, N = 452.731. (F9X) gfortran options: -O0

Benchmark: Plot Singular Value Decomposition

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OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Higgs Bosonphoronix-ml.txt1530456075SE +/- 0.83, N = 365.741. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: 20 Newsgroups / Logistic Regressionphoronix-ml.txt3691215SE +/- 0.06, N = 310.451. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Polynomial Kernel Approximationphoronix-ml.txt20406080100SE +/- 0.04, N = 3104.701. (F9X) gfortran options: -O0

Benchmark: Plot Non-Negative Matrix Factorization

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OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Categorical Onlyphoronix-ml.txt714212835SE +/- 0.30, N = 1530.191. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Kernel PCA Solvers / Time vs. N Samplesphoronix-ml.txt1428425670SE +/- 0.24, N = 361.611. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Kernel PCA Solvers / Time vs. N Componentsphoronix-ml.txt714212835SE +/- 0.33, N = 331.041. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sparse Random Projections / 100 Iterationsphoronix-ml.txt110220330440550SE +/- 2.62, N = 3504.831. (F9X) gfortran options: -O0

R Benchmark

This test is a quick-running survey of general R performance Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterR Benchmarkphoronix-ml.txt0.02820.05640.08460.11280.141SE +/- 0.0007, N = 30.1252

Numpy Benchmark

This is a test to obtain the general Numpy performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgScore, More Is BetterNumpy Benchmarkphoronix-ml.txt150300450600750SE +/- 5.45, N = 3715.50

DeepSpeech

Mozilla DeepSpeech is a speech-to-text engine powered by TensorFlow for machine learning and derived from Baidu's Deep Speech research paper. This test profile times the speech-to-text process for a roughly three minute audio recording. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterDeepSpeech 0.6Acceleration: CPUphoronix-ml.txt1020304050SE +/- 0.15, N = 346.23

RNNoise

RNNoise is a recurrent neural network for audio noise reduction developed by Mozilla and Xiph.Org. This test profile is a single-threaded test measuring the time to denoise a sample 26 minute long 16-bit RAW audio file using this recurrent neural network noise suppression library. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterRNNoise 0.2Input: 26 Minute Long Talking Samplephoronix-ml.txt246810SE +/- 0.019, N = 37.8521. (CC) gcc options: -O2 -pedantic -fvisibility=hidden

AI Benchmark Alpha

AI Benchmark Alpha is a Python library for evaluating artificial intelligence (AI) performance on diverse hardware platforms and relies upon the TensorFlow machine learning library. Learn more via the OpenBenchmarking.org test page.

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Mobile Neural Network

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2.9.b11b7037dModel: nasnetphoronix-ml.txt48121620SE +/- 0.02, N = 315.30MIN: 14.65 / MAX: 21.591. (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

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2.9.b11b7037dModel: mobilenetV3phoronix-ml.txt0.57061.14121.71182.28242.853SE +/- 0.008, N = 32.536MIN: 2.4 / MAX: 3.471. (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

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2.9.b11b7037dModel: squeezenetv1.1phoronix-ml.txt0.97361.94722.92083.89444.868SE +/- 0.117, N = 34.327MIN: 3.96 / MAX: 6.651. (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

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2.9.b11b7037dModel: resnet-v2-50phoronix-ml.txt510152025SE +/- 0.11, N = 318.53MIN: 18.26 / MAX: 28.91. (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

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2.9.b11b7037dModel: SqueezeNetV1.0phoronix-ml.txt246810SE +/- 0.200, N = 36.429MIN: 5.97 / MAX: 7.141. (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

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2.9.b11b7037dModel: MobileNetV2_224phoronix-ml.txt0.73531.47062.20592.94123.6765SE +/- 0.042, N = 33.268MIN: 3.15 / MAX: 5.051. (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

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2.9.b11b7037dModel: mobilenet-v1-1.0phoronix-ml.txt0.85141.70282.55423.40564.257SE +/- 0.007, N = 33.784MIN: 3.71 / MAX: 6.581. (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

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2.9.b11b7037dModel: inception-v3phoronix-ml.txt816243240SE +/- 0.04, N = 336.45MIN: 36.16 / MAX: 50.971. (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

Neural Magic DeepSparse

This is a benchmark of Neural Magic's DeepSparse using its built-in deepsparse.benchmark utility and various models from their SparseZoo (https://sparsezoo.neuralmagic.com/). Learn more via the OpenBenchmarking.org test page.

Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream

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Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream

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Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream

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Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream

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Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream

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Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream

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Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream

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Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream

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Model: Llama2 Chat 7b Quantized - Scenario: Asynchronous Multi-Stream

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Model: Llama2 Chat 7b Quantized - Scenario: Synchronous Single-Stream

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Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream

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Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream

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Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream

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Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream

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Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream

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Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream

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Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream

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Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream

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Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream

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Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream

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Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream

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Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream

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ONNX Runtime

Model: GPT-2 - Device: CPU - Executor: Parallel

phoronix-ml.txt: The test quit with a non-zero exit status. E: onnxruntime/onnxruntime/test/onnx/onnx_model_info.cc:45 void OnnxModelInfo::InitOnnxModelInfo(const std::filesystem::__cxx11::path&) open file "GPT2/model.onnx" failed: No such file or directory

Model: GPT-2 - Device: CPU - Executor: Standard

phoronix-ml.txt: The test quit with a non-zero exit status. E: onnxruntime/onnxruntime/test/onnx/onnx_model_info.cc:45 void OnnxModelInfo::InitOnnxModelInfo(const std::filesystem::__cxx11::path&) open file "GPT2/model.onnx" failed: No such file or directory

OpenBenchmarking.orgInferences Per Second, More Is BetterONNX Runtime 1.19Model: yolov4 - Device: CPU - Executor: Parallelphoronix-ml.txt1.21432.42863.64294.85726.0715SE +/- 0.07557, N = 35.396841. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt

OpenBenchmarking.orgInferences Per Second, More Is BetterONNX Runtime 1.19Model: yolov4 - Device: CPU - Executor: Standardphoronix-ml.txt3691215SE +/- 0.03544, N = 39.654421. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt

OpenBenchmarking.orgInferences Per Second, More Is BetterONNX Runtime 1.19Model: ZFNet-512 - Device: CPU - Executor: Parallelphoronix-ml.txt1326395265SE +/- 0.67, N = 457.861. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt

OpenBenchmarking.orgInferences Per Second, More Is BetterONNX Runtime 1.19Model: ZFNet-512 - Device: CPU - Executor: Standardphoronix-ml.txt20406080100SE +/- 1.32, N = 4109.861. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt

OpenBenchmarking.orgInferences Per Second, More Is BetterONNX Runtime 1.19Model: T5 Encoder - Device: CPU - Executor: Parallelphoronix-ml.txt60120180240300SE +/- 1.41, N = 3272.181. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt

OpenBenchmarking.orgInferences Per Second, More Is BetterONNX Runtime 1.19Model: T5 Encoder - Device: CPU - Executor: Standardphoronix-ml.txt4080120160200SE +/- 2.15, N = 4172.041. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt

Model: bertsquad-12 - Device: CPU - Executor: Parallel

phoronix-ml.txt: The test quit with a non-zero exit status. E: onnxruntime/onnxruntime/test/onnx/onnx_model_info.cc:45 void OnnxModelInfo::InitOnnxModelInfo(const std::filesystem::__cxx11::path&) open file "bertsquad-12/bertsquad-12.onnx" failed: No such file or directory

Model: bertsquad-12 - Device: CPU - Executor: Standard

phoronix-ml.txt: The test quit with a non-zero exit status. E: onnxruntime/onnxruntime/test/onnx/onnx_model_info.cc:45 void OnnxModelInfo::InitOnnxModelInfo(const std::filesystem::__cxx11::path&) open file "bertsquad-12/bertsquad-12.onnx" failed: No such file or directory

OpenBenchmarking.orgInferences Per Second, More Is BetterONNX Runtime 1.19Model: CaffeNet 12-int8 - Device: CPU - Executor: Parallelphoronix-ml.txt4080120160200SE +/- 1.63, N = 15203.061. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt

OpenBenchmarking.orgInferences Per Second, More Is BetterONNX Runtime 1.19Model: CaffeNet 12-int8 - Device: CPU - Executor: Standardphoronix-ml.txt140280420560700SE +/- 7.24, N = 3639.911. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt

OpenBenchmarking.orgInferences Per Second, More Is BetterONNX Runtime 1.19Model: fcn-resnet101-11 - Device: CPU - Executor: Parallelphoronix-ml.txt0.27080.54160.81241.08321.354SE +/- 0.02346, N = 121.203391. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt

OpenBenchmarking.orgInferences Per Second, More Is BetterONNX Runtime 1.19Model: fcn-resnet101-11 - Device: CPU - Executor: Standardphoronix-ml.txt0.92631.85262.77893.70524.6315SE +/- 0.00482, N = 34.116761. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt

Model: ArcFace ResNet-100 - Device: CPU - Executor: Parallel

phoronix-ml.txt: The test quit with a non-zero exit status. E: onnxruntime/onnxruntime/test/onnx/onnx_model_info.cc:45 void OnnxModelInfo::InitOnnxModelInfo(const std::filesystem::__cxx11::path&) open file "resnet100/resnet100.onnx" failed: No such file or directory

Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard

phoronix-ml.txt: The test quit with a non-zero exit status. E: onnxruntime/onnxruntime/test/onnx/onnx_model_info.cc:45 void OnnxModelInfo::InitOnnxModelInfo(const std::filesystem::__cxx11::path&) open file "resnet100/resnet100.onnx" failed: No such file or directory

OpenBenchmarking.orgInferences Per Second, More Is BetterONNX Runtime 1.19Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Parallelphoronix-ml.txt20406080100SE +/- 0.36, N = 3108.881. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt

OpenBenchmarking.orgInferences Per Second, More Is BetterONNX Runtime 1.19Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standardphoronix-ml.txt70140210280350SE +/- 0.68, N = 3328.291. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt

OpenBenchmarking.orgInferences Per Second, More Is BetterONNX Runtime 1.19Model: super-resolution-10 - Device: CPU - Executor: Parallelphoronix-ml.txt306090120150SE +/- 0.93, N = 3123.741. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt

OpenBenchmarking.orgInferences Per Second, More Is BetterONNX Runtime 1.19Model: super-resolution-10 - Device: CPU - Executor: Standardphoronix-ml.txt20406080100SE +/- 0.21, N = 397.321. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt

OpenBenchmarking.orgInferences Per Second, More Is BetterONNX Runtime 1.19Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Parallelphoronix-ml.txt0.29190.58380.87571.16761.4595SE +/- 0.00341, N = 31.297181. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt

OpenBenchmarking.orgInferences Per Second, More Is BetterONNX Runtime 1.19Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Standardphoronix-ml.txt0.49740.99481.49221.98962.487SE +/- 0.00518, N = 32.210861. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt

Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Parallel

phoronix-ml.txt: The test quit with a non-zero exit status. E: onnxruntime/onnxruntime/test/onnx/onnx_model_info.cc:45 void OnnxModelInfo::InitOnnxModelInfo(const std::filesystem::__cxx11::path&) open file "FasterRCNN-12-int8/FasterRCNN-12-int8.onnx" failed: No such file or directory

Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Standard

phoronix-ml.txt: The test quit with a non-zero exit status. E: onnxruntime/onnxruntime/test/onnx/onnx_model_info.cc:45 void OnnxModelInfo::InitOnnxModelInfo(const std::filesystem::__cxx11::path&) open file "FasterRCNN-12-int8/FasterRCNN-12-int8.onnx" failed: No such file or directory

OpenCV

This is a benchmark of the OpenCV (Computer Vision) library's built-in performance tests. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterOpenCV 4.7Test: DNN - Deep Neural Networkphoronix-ml.txt7K14K21K28K35KSE +/- 1066.17, N = 15330801. (CXX) g++ options: -fsigned-char -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections -msse -msse2 -msse3 -fvisibility=hidden -O3 -ldl -lm -lpthread -lrt

PyTorch

This is a benchmark of PyTorch making use of pytorch-benchmark [https://github.com/LukasHedegaard/pytorch-benchmark]. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: ResNet-50phoronix-ml.txt1326395265SE +/- 0.03, N = 360.17MIN: 49.64 / MAX: 62.8

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: ResNet-152phoronix-ml.txt612182430SE +/- 0.14, N = 323.19MIN: 19.02 / MAX: 24.35

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-50phoronix-ml.txt1020304050SE +/- 0.20, N = 345.59MIN: 38.82 / MAX: 46.87

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: ResNet-50phoronix-ml.txt1122334455SE +/- 0.47, N = 346.42MIN: 41.66 / MAX: 47.49

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: ResNet-50phoronix-ml.txt1122334455SE +/- 0.10, N = 346.69MIN: 42.52 / MAX: 47.36

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-152phoronix-ml.txt48121620SE +/- 0.07, N = 317.97MIN: 14.56 / MAX: 18.28

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: ResNet-50phoronix-ml.txt1020304050SE +/- 0.23, N = 346.06MIN: 38.74 / MAX: 46.92

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: ResNet-152phoronix-ml.txt48121620SE +/- 0.03, N = 317.78MIN: 15.02 / MAX: 18.13

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 512 - Model: ResNet-50phoronix-ml.txt1020304050SE +/- 0.29, N = 345.75MIN: 41.27 / MAX: 46.82

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: ResNet-152phoronix-ml.txt48121620SE +/- 0.08, N = 317.64MIN: 14.41 / MAX: 18.1

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: ResNet-152phoronix-ml.txt48121620SE +/- 0.06, N = 317.92MIN: 14.68 / MAX: 18.36

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 512 - Model: ResNet-152phoronix-ml.txt48121620SE +/- 0.07, N = 317.99MIN: 14.67 / MAX: 18.38

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_lphoronix-ml.txt48121620SE +/- 0.19, N = 314.18MIN: 12.11 / MAX: 14.81

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_lphoronix-ml.txt3691215SE +/- 0.09, N = 39.90MIN: 8.07 / MAX: 10.35

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_lphoronix-ml.txt3691215SE +/- 0.07, N = 39.85MIN: 8.27 / MAX: 10.3

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_lphoronix-ml.txt3691215SE +/- 0.05, N = 39.98MIN: 7.98 / MAX: 10.29

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_lphoronix-ml.txt3691215SE +/- 0.05, N = 310.11MIN: 8.31 / MAX: 10.38

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_lphoronix-ml.txt3691215SE +/- 0.09, N = 129.94MIN: 7.81 / MAX: 10.45

spaCy

The spaCy library is an open-source solution for advanced neural language processing (NLP). The spaCy library leverages Python and is a leading neural language processing solution. This test profile times the spaCy CPU performance with various models. Learn more via the OpenBenchmarking.org test page.

phoronix-ml.txt: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'tqdm'

TensorFlow Lite

This is a benchmark of the TensorFlow Lite implementation focused on TensorFlow machine learning for mobile, IoT, edge, and other cases. The current Linux support is limited to running on CPUs. This test profile is measuring the average inference time. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2022-05-18Model: SqueezeNetphoronix-ml.txt400800120016002000SE +/- 17.45, N = 151836.28

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2022-05-18Model: Inception V4phoronix-ml.txt4K8K12K16K20KSE +/- 520.83, N = 1520372.6

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2022-05-18Model: NASNet Mobilephoronix-ml.txt7K14K21K28K35KSE +/- 419.25, N = 1533662.5

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2022-05-18Model: Mobilenet Floatphoronix-ml.txt30060090012001500SE +/- 4.96, N = 31381.25

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2022-05-18Model: Mobilenet Quantphoronix-ml.txt5001000150020002500SE +/- 12.96, N = 32501.21

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2022-05-18Model: Inception ResNet V2phoronix-ml.txt7K14K21K28K35KSE +/- 434.39, N = 333356.3

TNN

TNN is an open-source deep learning reasoning framework developed by Tencent. Learn more via the OpenBenchmarking.org test page.

Target: CPU - Model: DenseNet

phoronix-ml.txt: The test quit with a non-zero exit status. E: ./tnn: 3: ./test/TNNTest: not found

Target: CPU - Model: MobileNet v2

phoronix-ml.txt: The test quit with a non-zero exit status. E: ./tnn: 3: ./test/TNNTest: not found

Target: CPU - Model: SqueezeNet v2

phoronix-ml.txt: The test quit with a non-zero exit status. E: ./tnn: 3: ./test/TNNTest: not found

Target: CPU - Model: SqueezeNet v1.1

phoronix-ml.txt: The test quit with a non-zero exit status. E: ./tnn: 3: ./test/TNNTest: not found

Whisper.cpp

OpenBenchmarking.orgSeconds, Fewer Is BetterWhisper.cpp 1.6.2Model: ggml-base.en - Input: 2016 State of the Unionphoronix-ml.txt20406080100SE +/- 0.44, N = 392.751. (CXX) g++ options: -O3 -std=c++11 -fPIC -pthread -msse3 -mssse3 -mavx -mf16c -mfma -mavx2 -mavx512f -mavx512cd -mavx512vl -mavx512dq -mavx512bw -mavx512vbmi -mavx512vnni

OpenBenchmarking.orgSeconds, Fewer Is BetterWhisper.cpp 1.6.2Model: ggml-small.en - Input: 2016 State of the Unionphoronix-ml.txt50100150200250SE +/- 0.41, N = 3218.191. (CXX) g++ options: -O3 -std=c++11 -fPIC -pthread -msse3 -mssse3 -mavx -mf16c -mfma -mavx2 -mavx512f -mavx512cd -mavx512vl -mavx512dq -mavx512bw -mavx512vbmi -mavx512vnni

OpenBenchmarking.orgSeconds, Fewer Is BetterWhisper.cpp 1.6.2Model: ggml-medium.en - Input: 2016 State of the Unionphoronix-ml.txt130260390520650SE +/- 1.41, N = 3579.171. (CXX) g++ options: -O3 -std=c++11 -fPIC -pthread -msse3 -mssse3 -mavx -mf16c -mfma -mavx2 -mavx512f -mavx512cd -mavx512vl -mavx512dq -mavx512bw -mavx512vbmi -mavx512vnni

XNNPACK

OpenBenchmarking.orgus, Fewer Is BetterXNNPACK b7b048Model: FP32MobileNetV1phoronix-ml.txt30060090012001500SE +/- 2.52, N = 312331. (CXX) g++ options: -O3 -lrt -lm

OpenBenchmarking.orgus, Fewer Is BetterXNNPACK b7b048Model: FP32MobileNetV2phoronix-ml.txt400800120016002000SE +/- 14.40, N = 318731. (CXX) g++ options: -O3 -lrt -lm

OpenBenchmarking.orgus, Fewer Is BetterXNNPACK b7b048Model: FP32MobileNetV3Largephoronix-ml.txt5001000150020002500SE +/- 12.67, N = 324651. (CXX) g++ options: -O3 -lrt -lm

OpenBenchmarking.orgus, Fewer Is BetterXNNPACK b7b048Model: FP32MobileNetV3Smallphoronix-ml.txt30060090012001500SE +/- 3.61, N = 315031. (CXX) g++ options: -O3 -lrt -lm

OpenBenchmarking.orgus, Fewer Is BetterXNNPACK b7b048Model: FP16MobileNetV1phoronix-ml.txt2004006008001000SE +/- 6.56, N = 311441. (CXX) g++ options: -O3 -lrt -lm

OpenBenchmarking.orgus, Fewer Is BetterXNNPACK b7b048Model: FP16MobileNetV2phoronix-ml.txt30060090012001500SE +/- 15.14, N = 314951. (CXX) g++ options: -O3 -lrt -lm

OpenBenchmarking.orgus, Fewer Is BetterXNNPACK b7b048Model: FP16MobileNetV3Largephoronix-ml.txt5001000150020002500SE +/- 6.66, N = 321281. (CXX) g++ options: -O3 -lrt -lm

OpenBenchmarking.orgus, Fewer Is BetterXNNPACK b7b048Model: FP16MobileNetV3Smallphoronix-ml.txt30060090012001500SE +/- 5.55, N = 314641. (CXX) g++ options: -O3 -lrt -lm

OpenBenchmarking.orgus, Fewer Is BetterXNNPACK b7b048Model: QS8MobileNetV2phoronix-ml.txt30060090012001500SE +/- 7.84, N = 313981. (CXX) g++ options: -O3 -lrt -lm

Llama.cpp

Model: llama-2-7b.Q4_0.gguf

phoronix-ml.txt: The test quit with a non-zero exit status. E: main: error: unable to load model

Model: llama-2-13b.Q4_0.gguf

phoronix-ml.txt: The test quit with a non-zero exit status. E: main: error: unable to load model

Model: llama-2-70b-chat.Q5_0.gguf

phoronix-ml.txt: The test quit with a non-zero exit status. E: main: error: unable to load model

Llamafile

Test: llava-v1.5-7b-q4 - Acceleration: CPU

phoronix-ml.txt: The test quit with a non-zero exit status. E: ./run-llava: line 2: ./llava-v1.6-mistral-7b.Q8_0.llamafile.86: No such file or directory

Test: mistral-7b-instruct-v0.2.Q8_0 - Acceleration: CPU

phoronix-ml.txt: The test quit with a non-zero exit status. E: ./run-mistral: line 2: ./mistral-7b-instruct-v0.2.Q5_K_M.llamafile.86: No such file or directory

Test: wizardcoder-python-34b-v1.0.Q6_K - Acceleration: CPU

phoronix-ml.txt: The test quit with a non-zero exit status. E: ./run-wizardcoder: line 2: ./wizardcoder-python-34b-v1.0.Q6_K.llamafile.86: No such file or directory

Caffe

This is a benchmark of the Caffe deep learning framework and currently supports the AlexNet and Googlenet model and execution on both CPUs and NVIDIA GPUs. Learn more via the OpenBenchmarking.org test page.

Model: AlexNet - Acceleration: CPU - Iterations: 100

phoronix-ml.txt: The test quit with a non-zero exit status. E: ./caffe: 3: ./tools/caffe: not found

Model: AlexNet - Acceleration: CPU - Iterations: 200

phoronix-ml.txt: The test quit with a non-zero exit status. E: ./caffe: 3: ./tools/caffe: not found

Model: AlexNet - Acceleration: CPU - Iterations: 1000

phoronix-ml.txt: The test quit with a non-zero exit status. E: ./caffe: 3: ./tools/caffe: not found

Model: GoogleNet - Acceleration: CPU - Iterations: 100

phoronix-ml.txt: The test quit with a non-zero exit status. E: ./caffe: 3: ./tools/caffe: not found

Model: GoogleNet - Acceleration: CPU - Iterations: 200

phoronix-ml.txt: The test quit with a non-zero exit status. E: ./caffe: 3: ./tools/caffe: not found

Model: GoogleNet - Acceleration: CPU - Iterations: 1000

phoronix-ml.txt: The test quit with a non-zero exit status. E: ./caffe: 3: ./tools/caffe: not found

SHOC Scalable HeterOgeneous Computing

The CUDA and OpenCL version of Vetter's Scalable HeterOgeneous Computing benchmark suite. SHOC provides a number of different benchmark programs for evaluating the performance and stability of compute devices. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgGFLOPS, More Is BetterSHOC Scalable HeterOgeneous Computing 2020-04-17Target: OpenCL - Benchmark: S3Dphoronix-ml.txt70140210280350SE +/- 3.81, N = 15298.491. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi

OpenBenchmarking.orgGB/s, More Is BetterSHOC Scalable HeterOgeneous Computing 2020-04-17Target: OpenCL - Benchmark: Triadphoronix-ml.txt612182430SE +/- 0.23, N = 623.051. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi

OpenBenchmarking.orgGFLOPS, More Is BetterSHOC Scalable HeterOgeneous Computing 2020-04-17Target: OpenCL - Benchmark: FFT SPphoronix-ml.txt6001200180024003000SE +/- 2.81, N = 32703.371. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi

OpenBenchmarking.orgGHash/s, More Is BetterSHOC Scalable HeterOgeneous Computing 2020-04-17Target: OpenCL - Benchmark: MD5 Hashphoronix-ml.txt1122334455SE +/- 0.68, N = 349.641. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi

OpenBenchmarking.orgGB/s, More Is BetterSHOC Scalable HeterOgeneous Computing 2020-04-17Target: OpenCL - Benchmark: Reductionphoronix-ml.txt130260390520650SE +/- 0.41, N = 3595.051. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi

OpenBenchmarking.orgGFLOPS, More Is BetterSHOC Scalable HeterOgeneous Computing 2020-04-17Target: OpenCL - Benchmark: GEMM SGEMM_Nphoronix-ml.txt2K4K6K8K10KSE +/- 23.01, N = 38470.021. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi

OpenBenchmarking.orgGFLOPS, More Is BetterSHOC Scalable HeterOgeneous Computing 2020-04-17Target: OpenCL - Benchmark: Max SP Flopsphoronix-ml.txt20K40K60K80K100KSE +/- 230.38, N = 393757.31. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi

OpenBenchmarking.orgGB/s, More Is BetterSHOC Scalable HeterOgeneous Computing 2020-04-17Target: OpenCL - Benchmark: Bus Speed Downloadphoronix-ml.txt612182430SE +/- 0.00, N = 324.991. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi

OpenBenchmarking.orgGB/s, More Is BetterSHOC Scalable HeterOgeneous Computing 2020-04-17Target: OpenCL - Benchmark: Bus Speed Readbackphoronix-ml.txt612182430SE +/- 0.00, N = 326.251. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi

OpenBenchmarking.orgGB/s, More Is BetterSHOC Scalable HeterOgeneous Computing 2020-04-17Target: OpenCL - Benchmark: Texture Read Bandwidthphoronix-ml.txt2004006008001000SE +/- 5.65, N = 31003.321. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi

NCNN

NCNN is a high performance neural network inference framework optimized for mobile and other platforms developed by Tencent. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: mobilenetphoronix-ml.txt48121620SE +/- 0.12, N = 1513.85MIN: 12.83 / MAX: 247.131. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU-v2-v2 - Model: mobilenet-v2phoronix-ml.txt246810SE +/- 0.05, N = 156.30MIN: 5.63 / MAX: 33.151. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU-v3-v3 - Model: mobilenet-v3phoronix-ml.txt246810SE +/- 0.04, N = 156.45MIN: 5.96 / MAX: 63.771. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: shufflenet-v2phoronix-ml.txt246810SE +/- 0.09, N = 158.15MIN: 7.52 / MAX: 291.181. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: mnasnetphoronix-ml.txt246810SE +/- 0.11, N = 156.11MIN: 5.26 / MAX: 321.291. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: efficientnet-b0phoronix-ml.txt246810SE +/- 0.09, N = 158.28MIN: 7.57 / MAX: 296.151. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: blazefacephoronix-ml.txt0.69981.39962.09942.79923.499SE +/- 0.02, N = 153.11MIN: 2.85 / MAX: 11.531. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: googlenetphoronix-ml.txt48121620SE +/- 0.15, N = 1516.42MIN: 15.38 / MAX: 271.341. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: vgg16phoronix-ml.txt612182430SE +/- 0.34, N = 1525.71MIN: 22.56 / MAX: 344.21. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: resnet18phoronix-ml.txt246810SE +/- 0.07, N = 158.02MIN: 7.54 / MAX: 17.961. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: alexnetphoronix-ml.txt1.2512.5023.7535.0046.255SE +/- 0.07, N = 155.56MIN: 5.07 / MAX: 35.351. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: resnet50phoronix-ml.txt3691215SE +/- 0.13, N = 1513.33MIN: 11.94 / MAX: 281.31. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPUv2-yolov3v2-yolov3 - Model: mobilenetv2-yolov3phoronix-ml.txt48121620SE +/- 0.12, N = 1513.85MIN: 12.83 / MAX: 247.131. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: yolov4-tinyphoronix-ml.txt612182430SE +/- 0.11, N = 1524.14MIN: 21.58 / MAX: 105.121. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: squeezenet_ssdphoronix-ml.txt48121620SE +/- 0.12, N = 1514.34MIN: 13.13 / MAX: 263.271. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: regnety_400mphoronix-ml.txt510152025SE +/- 0.12, N = 1518.58MIN: 17.32 / MAX: 295.211. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: vision_transformerphoronix-ml.txt918273645SE +/- 0.18, N = 1540.59MIN: 37.83 / MAX: 299.431. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: FastestDetphoronix-ml.txt3691215SE +/- 0.30, N = 159.80MIN: 6.73 / MAX: 273.491. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: mobilenetphoronix-ml.txt48121620SE +/- 0.10, N = 313.79MIN: 13.15 / MAX: 23.261. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2phoronix-ml.txt246810SE +/- 0.02, N = 36.31MIN: 5.98 / MAX: 14.561. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3phoronix-ml.txt246810SE +/- 0.03, N = 36.49MIN: 6.21 / MAX: 15.751. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: shufflenet-v2phoronix-ml.txt246810SE +/- 0.05, N = 38.06MIN: 7.83 / MAX: 15.081. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: mnasnetphoronix-ml.txt1.34782.69564.04345.39126.739SE +/- 0.01, N = 35.99MIN: 5.68 / MAX: 14.251. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: efficientnet-b0phoronix-ml.txt246810SE +/- 0.37, N = 38.68MIN: 7.76 / MAX: 202.361. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: blazefacephoronix-ml.txt0.70651.4132.11952.8263.5325SE +/- 0.00, N = 33.14MIN: 3.01 / MAX: 8.51. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: googlenetphoronix-ml.txt48121620SE +/- 0.04, N = 316.01MIN: 15.51 / MAX: 26.581. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: vgg16phoronix-ml.txt612182430SE +/- 0.31, N = 325.13MIN: 23.1 / MAX: 139.051. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: resnet18phoronix-ml.txt246810SE +/- 0.08, N = 37.86MIN: 7.54 / MAX: 15.181. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: alexnetphoronix-ml.txt1.1882.3763.5644.7525.94SE +/- 0.02, N = 35.28MIN: 5.1 / MAX: 15.431. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: resnet50phoronix-ml.txt48121620SE +/- 1.16, N = 314.65MIN: 12.41 / MAX: 377.471. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPUv2-yolov3v2-yolov3 - Model: mobilenetv2-yolov3phoronix-ml.txt48121620SE +/- 0.10, N = 313.79MIN: 13.15 / MAX: 23.261. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: yolov4-tinyphoronix-ml.txt612182430SE +/- 0.41, N = 323.64MIN: 22.17 / MAX: 39.941. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: squeezenet_ssdphoronix-ml.txt48121620SE +/- 1.72, N = 316.04MIN: 13.26 / MAX: 580.791. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: regnety_400mphoronix-ml.txt510152025SE +/- 0.09, N = 318.63MIN: 18.07 / MAX: 108.41. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: vision_transformerphoronix-ml.txt918273645SE +/- 0.27, N = 341.05MIN: 38.99 / MAX: 101.631. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: FastestDetphoronix-ml.txt3691215SE +/- 0.39, N = 39.82MIN: 8.74 / MAX: 19.331. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

Mlpack Benchmark

Mlpack benchmark scripts for machine learning libraries Learn more via the OpenBenchmarking.org test page.

Benchmark: scikit_ica

phoronix-ml.txt: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'imp'

Benchmark: scikit_qda

phoronix-ml.txt: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'imp'

Benchmark: scikit_svm

phoronix-ml.txt: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'imp'

Benchmark: scikit_linearridgeregression

phoronix-ml.txt: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'imp'

oneDNN

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.6Harness: IP Shapes 1D - Engine: CPUphoronix-ml.txt0.25570.51140.76711.02281.2785SE +/- 0.00979, N = 151.13657MIN: 1.011. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -fcf-protection=full -pie -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.6Harness: IP Shapes 3D - Engine: CPUphoronix-ml.txt0.31410.62820.94231.25641.5705SE +/- 0.01618, N = 151.39591MIN: 1.161. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -fcf-protection=full -pie -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.6Harness: Convolution Batch Shapes Auto - Engine: CPUphoronix-ml.txt0.53171.06341.59512.12682.6585SE +/- 0.02737, N = 42.36317MIN: 1.971. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -fcf-protection=full -pie -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.6Harness: Deconvolution Batch shapes_1d - Engine: CPUphoronix-ml.txt0.84951.6992.54853.3984.2475SE +/- 0.00801, N = 33.77567MIN: 2.811. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -fcf-protection=full -pie -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.6Harness: Deconvolution Batch shapes_3d - Engine: CPUphoronix-ml.txt0.41670.83341.25011.66682.0835SE +/- 0.01905, N = 41.85206MIN: 1.731. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -fcf-protection=full -pie -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.6Harness: Recurrent Neural Network Training - Engine: CPUphoronix-ml.txt30060090012001500SE +/- 9.63, N = 31261.40MIN: 1196.781. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -fcf-protection=full -pie -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.6Harness: Recurrent Neural Network Inference - Engine: CPUphoronix-ml.txt160320480640800SE +/- 8.66, N = 3736.40MIN: 639.281. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -fcf-protection=full -pie -ldl

OpenVINO

This is a test of the Intel OpenVINO, a toolkit around neural networks, using its built-in benchmarking support and analyzing the throughput and latency for various models. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Face Detection FP16 - Device: CPUphoronix-ml.txt510152025SE +/- 0.09, N = 319.691. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Face Detection FP16 - Device: CPUphoronix-ml.txt130260390520650SE +/- 2.89, N = 3607.48MIN: 575.29 / MAX: 657.061. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Person Detection FP16 - Device: CPUphoronix-ml.txt306090120150SE +/- 3.20, N = 15129.451. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Person Detection FP16 - Device: CPUphoronix-ml.txt20406080100SE +/- 2.03, N = 1593.29MIN: 31.12 / MAX: 185.661. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Person Detection FP32 - Device: CPUphoronix-ml.txt306090120150SE +/- 1.07, N = 15119.411. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Person Detection FP32 - Device: CPUphoronix-ml.txt20406080100SE +/- 0.82, N = 15100.46MIN: 32.5 / MAX: 161.811. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Vehicle Detection FP16 - Device: CPUphoronix-ml.txt2004006008001000SE +/- 15.55, N = 151077.871. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Vehicle Detection FP16 - Device: CPUphoronix-ml.txt3691215SE +/- 0.14, N = 1511.12MIN: 4.52 / MAX: 34.071. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Face Detection FP16-INT8 - Device: CPUphoronix-ml.txt918273645SE +/- 0.02, N = 337.361. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Face Detection FP16-INT8 - Device: CPUphoronix-ml.txt70140210280350SE +/- 0.15, N = 3320.42MIN: 299.06 / MAX: 380.181. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Face Detection Retail FP16 - Device: CPUphoronix-ml.txt9001800270036004500SE +/- 13.60, N = 34273.091. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Face Detection Retail FP16 - Device: CPUphoronix-ml.txt0.6211.2421.8632.4843.105SE +/- 0.01, N = 32.76MIN: 1.41 / MAX: 15.611. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Road Segmentation ADAS FP16 - Device: CPUphoronix-ml.txt100200300400500SE +/- 9.05, N = 15465.861. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Road Segmentation ADAS FP16 - Device: CPUphoronix-ml.txt612182430SE +/- 0.43, N = 1525.83MIN: 10.2 / MAX: 57.071. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Vehicle Detection FP16-INT8 - Device: CPUphoronix-ml.txt5001000150020002500SE +/- 3.34, N = 32160.571. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Vehicle Detection FP16-INT8 - Device: CPUphoronix-ml.txt1.23982.47963.71944.95926.199SE +/- 0.01, N = 35.51MIN: 2.97 / MAX: 19.151. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Weld Porosity Detection FP16 - Device: CPUphoronix-ml.txt400800120016002000SE +/- 1.95, N = 31947.671. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Weld Porosity Detection FP16 - Device: CPUphoronix-ml.txt3691215SE +/- 0.01, N = 312.25MIN: 6.32 / MAX: 26.61. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Face Detection Retail FP16-INT8 - Device: CPUphoronix-ml.txt14002800420056007000SE +/- 8.36, N = 36458.381. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Face Detection Retail FP16-INT8 - Device: CPUphoronix-ml.txt0.80551.6112.41653.2224.0275SE +/- 0.00, N = 33.58MIN: 1.95 / MAX: 16.911. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Road Segmentation ADAS FP16-INT8 - Device: CPUphoronix-ml.txt150300450600750SE +/- 2.47, N = 3679.661. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Road Segmentation ADAS FP16-INT8 - Device: CPUphoronix-ml.txt48121620SE +/- 0.06, N = 317.62MIN: 9.01 / MAX: 33.691. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Machine Translation EN To DE FP16 - Device: CPUphoronix-ml.txt4080120160200SE +/- 3.37, N = 12187.881. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Machine Translation EN To DE FP16 - Device: CPUphoronix-ml.txt1428425670SE +/- 1.08, N = 1263.99MIN: 29.61 / MAX: 110.091. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Weld Porosity Detection FP16-INT8 - Device: CPUphoronix-ml.txt8001600240032004000SE +/- 3.08, N = 33742.771. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Weld Porosity Detection FP16-INT8 - Device: CPUphoronix-ml.txt246810SE +/- 0.01, N = 36.31MIN: 3.31 / MAX: 21.371. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Person Vehicle Bike Detection FP16 - Device: CPUphoronix-ml.txt400800120016002000SE +/- 3.67, N = 31930.081. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Person Vehicle Bike Detection FP16 - Device: CPUphoronix-ml.txt246810SE +/- 0.01, N = 36.18MIN: 3.57 / MAX: 20.461. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Noise Suppression Poconet-Like FP16 - Device: CPUphoronix-ml.txt400800120016002000SE +/- 35.59, N = 152052.021. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Noise Suppression Poconet-Like FP16 - Device: CPUphoronix-ml.txt3691215SE +/- 0.18, N = 1511.53MIN: 5.76 / MAX: 42.761. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Handwritten English Recognition FP16 - Device: CPUphoronix-ml.txt2004006008001000SE +/- 4.12, N = 31035.291. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Handwritten English Recognition FP16 - Device: CPUphoronix-ml.txt612182430SE +/- 0.09, N = 323.12MIN: 14.9 / MAX: 38.921. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Person Re-Identification Retail FP16 - Device: CPUphoronix-ml.txt5001000150020002500SE +/- 3.45, N = 32523.591. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Person Re-Identification Retail FP16 - Device: CPUphoronix-ml.txt1.0622.1243.1864.2485.31SE +/- 0.01, N = 34.72MIN: 2.72 / MAX: 17.211. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Age Gender Recognition Retail 0013 FP16 - Device: CPUphoronix-ml.txt10K20K30K40K50KSE +/- 17.34, N = 348433.921. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Age Gender Recognition Retail 0013 FP16 - Device: CPUphoronix-ml.txt0.09680.19360.29040.38720.484SE +/- 0.00, N = 30.43MIN: 0.23 / MAX: 11.961. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Handwritten English Recognition FP16-INT8 - Device: CPUphoronix-ml.txt2004006008001000SE +/- 4.28, N = 31108.651. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Handwritten English Recognition FP16-INT8 - Device: CPUphoronix-ml.txt510152025SE +/- 0.08, N = 321.58MIN: 16.4 / MAX: 43.471. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2024.0Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPUphoronix-ml.txt14K28K42K56K70KSE +/- 38.55, N = 367537.771. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2024.0Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPUphoronix-ml.txt0.06750.1350.20250.270.3375SE +/- 0.00, N = 30.3MIN: 0.17 / MAX: 9.441. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

241 Results Shown

TensorFlow:
  CPU - 1 - VGG-16
  GPU - 1 - VGG-16
  CPU - 1 - AlexNet
  CPU - 16 - VGG-16
  CPU - 32 - VGG-16
  CPU - 64 - VGG-16
  GPU - 1 - AlexNet
  GPU - 16 - VGG-16
  GPU - 32 - VGG-16
  GPU - 64 - VGG-16
  CPU - 16 - AlexNet
  CPU - 256 - VGG-16
  CPU - 32 - AlexNet
  CPU - 512 - VGG-16
  CPU - 64 - AlexNet
  GPU - 16 - AlexNet
  GPU - 256 - VGG-16
  GPU - 32 - AlexNet
  GPU - 512 - VGG-16
  GPU - 64 - AlexNet
  CPU - 1 - GoogLeNet
  CPU - 1 - ResNet-50
  CPU - 256 - AlexNet
  CPU - 512 - AlexNet
  GPU - 1 - GoogLeNet
  GPU - 1 - ResNet-50
  GPU - 256 - AlexNet
  GPU - 512 - AlexNet
  CPU - 16 - GoogLeNet
  CPU - 16 - ResNet-50
  CPU - 32 - GoogLeNet
  CPU - 32 - ResNet-50
  CPU - 64 - GoogLeNet
  CPU - 64 - ResNet-50
  GPU - 16 - GoogLeNet
  GPU - 16 - ResNet-50
  GPU - 32 - GoogLeNet
  GPU - 32 - ResNet-50
  GPU - 64 - GoogLeNet
  GPU - 64 - ResNet-50
  CPU - 256 - GoogLeNet
  CPU - 256 - ResNet-50
  CPU - 512 - GoogLeNet
  CPU - 512 - ResNet-50
  GPU - 256 - GoogLeNet
  GPU - 256 - ResNet-50
  GPU - 512 - GoogLeNet
  GPU - 512 - ResNet-50
LeelaChessZero
Scikit-Learn:
  GLM
  SAGA
  Tree
  Lasso
  Sparsify
  Plot Ward
  MNIST Dataset
  Plot Neighbors
  SGD Regression
  SGDOneClassSVM
  Isolation Forest
  Text Vectorizers
  Plot Hierarchical
  Plot OMP vs. LARS
  Feature Expansions
  LocalOutlierFactor
  TSNE MNIST Dataset
  Isotonic / Logistic
  Plot Incremental PCA
  Hist Gradient Boosting
  Plot Parallel Pairwise
  Isotonic / Pathological
  Sample Without Replacement
  Covertype Dataset Benchmark
  Hist Gradient Boosting Adult
  Isotonic / Perturbed Logarithm
  Hist Gradient Boosting Threading
  Hist Gradient Boosting Higgs Boson
  20 Newsgroups / Logistic Regression
  Plot Polynomial Kernel Approximation
  Hist Gradient Boosting Categorical Only
  Kernel PCA Solvers / Time vs. N Samples
  Kernel PCA Solvers / Time vs. N Components
  Sparse Rand Projections / 100 Iterations
R Benchmark
Numpy Benchmark
DeepSpeech
RNNoise
Mobile Neural Network:
  nasnet
  mobilenetV3
  squeezenetv1.1
  resnet-v2-50
  SqueezeNetV1.0
  MobileNetV2_224
  mobilenet-v1-1.0
  inception-v3
ONNX Runtime:
  yolov4 - CPU - Parallel
  yolov4 - CPU - Standard
  ZFNet-512 - CPU - Parallel
  ZFNet-512 - CPU - Standard
  T5 Encoder - CPU - Parallel
  T5 Encoder - CPU - Standard
  CaffeNet 12-int8 - CPU - Parallel
  CaffeNet 12-int8 - CPU - Standard
  fcn-resnet101-11 - CPU - Parallel
  fcn-resnet101-11 - CPU - Standard
  ResNet50 v1-12-int8 - CPU - Parallel
  ResNet50 v1-12-int8 - CPU - Standard
  super-resolution-10 - CPU - Parallel
  super-resolution-10 - CPU - Standard
  ResNet101_DUC_HDC-12 - CPU - Parallel
  ResNet101_DUC_HDC-12 - CPU - Standard
OpenCV
PyTorch:
  CPU - 1 - ResNet-50
  CPU - 1 - ResNet-152
  CPU - 16 - ResNet-50
  CPU - 32 - ResNet-50
  CPU - 64 - ResNet-50
  CPU - 16 - ResNet-152
  CPU - 256 - ResNet-50
  CPU - 32 - ResNet-152
  CPU - 512 - ResNet-50
  CPU - 64 - ResNet-152
  CPU - 256 - ResNet-152
  CPU - 512 - ResNet-152
  CPU - 1 - Efficientnet_v2_l
  CPU - 16 - Efficientnet_v2_l
  CPU - 32 - Efficientnet_v2_l
  CPU - 64 - Efficientnet_v2_l
  CPU - 256 - Efficientnet_v2_l
  CPU - 512 - Efficientnet_v2_l
TensorFlow Lite:
  SqueezeNet
  Inception V4
  NASNet Mobile
  Mobilenet Float
  Mobilenet Quant
  Inception ResNet V2
Whisper.cpp:
  ggml-base.en - 2016 State of the Union
  ggml-small.en - 2016 State of the Union
  ggml-medium.en - 2016 State of the Union
XNNPACK:
  FP32MobileNetV1
  FP32MobileNetV2
  FP32MobileNetV3Large
  FP32MobileNetV3Small
  FP16MobileNetV1
  FP16MobileNetV2
  FP16MobileNetV3Large
  FP16MobileNetV3Small
  QS8MobileNetV2
SHOC Scalable HeterOgeneous Computing:
  OpenCL - S3D
  OpenCL - Triad
  OpenCL - FFT SP
  OpenCL - MD5 Hash
  OpenCL - Reduction
  OpenCL - GEMM SGEMM_N
  OpenCL - Max SP Flops
  OpenCL - Bus Speed Download
  OpenCL - Bus Speed Readback
  OpenCL - Texture Read Bandwidth
NCNN:
  CPU - mobilenet
  CPU-v2-v2 - mobilenet-v2
  CPU-v3-v3 - mobilenet-v3
  CPU - shufflenet-v2
  CPU - mnasnet
  CPU - efficientnet-b0
  CPU - blazeface
  CPU - googlenet
  CPU - vgg16
  CPU - resnet18
  CPU - alexnet
  CPU - resnet50
  CPUv2-yolov3v2-yolov3 - mobilenetv2-yolov3
  CPU - yolov4-tiny
  CPU - squeezenet_ssd
  CPU - regnety_400m
  CPU - vision_transformer
  CPU - FastestDet
  Vulkan GPU - mobilenet
  Vulkan GPU-v2-v2 - mobilenet-v2
  Vulkan GPU-v3-v3 - mobilenet-v3
  Vulkan GPU - shufflenet-v2
  Vulkan GPU - mnasnet
  Vulkan GPU - efficientnet-b0
  Vulkan GPU - blazeface
  Vulkan GPU - googlenet
  Vulkan GPU - vgg16
  Vulkan GPU - resnet18
  Vulkan GPU - alexnet
  Vulkan GPU - resnet50
  Vulkan GPUv2-yolov3v2-yolov3 - mobilenetv2-yolov3
  Vulkan GPU - yolov4-tiny
  Vulkan GPU - squeezenet_ssd
  Vulkan GPU - regnety_400m
  Vulkan GPU - vision_transformer
  Vulkan GPU - FastestDet
oneDNN:
  IP Shapes 1D - CPU
  IP Shapes 3D - CPU
  Convolution Batch Shapes Auto - CPU
  Deconvolution Batch shapes_1d - CPU
  Deconvolution Batch shapes_3d - CPU
  Recurrent Neural Network Training - CPU
  Recurrent Neural Network Inference - CPU
OpenVINO:
  Face Detection FP16 - CPU:
    FPS
    ms
  Person Detection FP16 - CPU:
    FPS
    ms
  Person Detection FP32 - CPU:
    FPS
    ms
  Vehicle Detection FP16 - CPU:
    FPS
    ms
  Face Detection FP16-INT8 - CPU:
    FPS
    ms
  Face Detection Retail FP16 - CPU:
    FPS
    ms
  Road Segmentation ADAS FP16 - CPU:
    FPS
    ms
  Vehicle Detection FP16-INT8 - CPU:
    FPS
    ms
  Weld Porosity Detection FP16 - CPU:
    FPS
    ms
  Face Detection Retail FP16-INT8 - CPU:
    FPS
    ms
  Road Segmentation ADAS FP16-INT8 - CPU:
    FPS
    ms
  Machine Translation EN To DE FP16 - CPU:
    FPS
    ms
  Weld Porosity Detection FP16-INT8 - CPU:
    FPS
    ms
  Person Vehicle Bike Detection FP16 - CPU:
    FPS
    ms
  Noise Suppression Poconet-Like FP16 - CPU:
    FPS
    ms
  Handwritten English Recognition FP16 - CPU:
    FPS
    ms
  Person Re-Identification Retail FP16 - CPU:
    FPS
    ms
  Age Gender Recognition Retail 0013 FP16 - CPU:
    FPS
    ms
  Handwritten English Recognition FP16-INT8 - CPU:
    FPS
    ms
  Age Gender Recognition Retail 0013 FP16-INT8 - CPU:
    FPS
    ms