Apple M1 Max testing with a Apple MacBook Pro and Apple M1 Max on macOS 12.1 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 2202161-NE-MBPM1MAXM40 MBP M1 Max Machine Learning, sys76-kudu-ML - Phoronix Test Suite MBP M1 Max Machine Learning, sys76-kudu-ML Apple M1 Max testing with a Apple MacBook Pro and Apple M1 Max on macOS 12.1 via the Phoronix Test Suite.
sys76-kudu-ML: AMD Ryzen 9 5900HX testing with a System76 Kudu (1.07.09RSA1 BIOS) and AMD Cezanne on Pop 21.10 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2202161-NE-MBPM1MAXM40,2202165-NE-SYS76KUDU88&grs&export=pdf&sro&rro .
MBP M1 Max Machine Learning, sys76-kudu-ML Processor Motherboard Memory Disk Graphics Monitor Chipset Audio Network OS Kernel OpenCL Compiler File-System Screen Resolution Desktop Display Server OpenGL Vulkan MBP M1 Max Machine Learning ML Tests Apple M1 Max (10 Cores) Apple MacBook Pro 64GB 1859GB Apple M1 Max Color LCD macOS 12.1 21.2.0 (arm64) OpenCL 1.2 (Nov 13 2021 00:45:09) GCC 13.0.0 + Clang 13.0.0 APFS 3456x2234 AMD Ryzen 9 5900HX @ 3.30GHz (8 Cores / 16 Threads) System76 Kudu (1.07.09RSA1 BIOS) AMD Renoir/Cezanne 16GB Samsung SSD 970 EVO Plus 500GB AMD Cezanne (2100/400MHz) AMD Renoir Radeon HD Audio Realtek RTL8125 2.5GbE + Intel Wi-Fi 6 AX200 Pop 21.10 5.15.15-76051515-generic (x86_64) GNOME Shell 40.5 X Server 1.20.13 4.6 Mesa 21.2.2 (LLVM 12.0.1) 1.2.182 GCC 11.2.0 ext4 1920x1080 OpenBenchmarking.org Environment Details - MBP M1 Max Machine Learning: XPC_FLAGS=0x0 Python Details - MBP M1 Max Machine Learning: Python 2.7.18 + Python 3.8.9 - ML Tests: Python 3.9.7 Kernel Details - ML Tests: Transparent Huge Pages: madvise Compiler Details - ML Tests: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-11-ZPT0kp/gcc-11-11.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-ZPT0kp/gcc-11-11.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-build-config=bootstrap-lto-lean --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v Processor Details - ML Tests: Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0xa50000c Graphics Details - ML Tests: GLAMOR - BAR1 / Visible vRAM Size: 512 MB Security Details - ML Tests: itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl and seccomp + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Full AMD retpoline IBPB: conditional IBRS_FW STIBP: always-on RSB filling + srbds: Not affected + tsx_async_abort: Not affected
MBP M1 Max Machine Learning, sys76-kudu-ML ncnn: Vulkan GPU - alexnet mnn: MobileNetV2_224 ncnn: Vulkan GPU - resnet50 ncnn: Vulkan GPU - googlenet ncnn: Vulkan GPU - resnet18 ncnn: CPU - alexnet ncnn: Vulkan GPU - mobilenet ncnn: CPU - googlenet ncnn: CPU - resnet50 ncnn: CPU - efficientnet-b0 ncnn: CPU - mnasnet ncnn: Vulkan GPU - yolov4-tiny ncnn: Vulkan GPU - vgg16 ncnn: Vulkan GPU - mnasnet ncnn: CPU - blazeface ncnn: Vulkan GPU-v2-v2 - mobilenet-v2 ncnn: Vulkan GPU - regnety_400m ncnn: Vulkan GPU - squeezenet_ssd ncnn: CPU-v2-v2 - mobilenet-v2 ncnn: CPU-v3-v3 - mobilenet-v3 ncnn: CPU - mobilenet ncnn: CPU - shufflenet-v2 ncnn: Vulkan GPU - blazeface ncnn: CPU - yolov4-tiny ncnn: Vulkan GPU - efficientnet-b0 ncnn: Vulkan GPU - shufflenet-v2 ncnn: CPU - squeezenet_ssd ncnn: Vulkan GPU-v3-v3 - mobilenet-v3 ncnn: CPU - resnet18 ncnn: CPU - regnety_400m ncnn: CPU - vgg16 mlpack: scikit_linearridgeregression mlpack: scikit_svm mlpack: scikit_qda mlpack: scikit_ica ecp-candle: P3B2 ecp-candle: P3B1 ecp-candle: P1B2 plaidml: No - Inference - ResNet 50 - CPU plaidml: No - Inference - VGG16 - CPU tnn: CPU - SqueezeNet v1.1 tnn: CPU - SqueezeNet v2 tnn: CPU - MobileNet v2 tnn: CPU - DenseNet caffe: GoogleNet - CPU - 1000 caffe: GoogleNet - CPU - 200 caffe: GoogleNet - CPU - 100 caffe: AlexNet - CPU - 1000 caffe: AlexNet - CPU - 200 caffe: AlexNet - CPU - 100 tensorflow-lite: Inception ResNet V2 tensorflow-lite: Mobilenet Quant tensorflow-lite: Mobilenet Float tensorflow-lite: NASNet Mobile tensorflow-lite: Inception V4 tensorflow-lite: SqueezeNet rnnoise: rbenchmark: lczero: BLAS deepspeech: CPU numpy: onednn: Matrix Multiply Batch Shapes Transformer - u8s8f32 - CPU onednn: Recurrent Neural Network Inference - bf16bf16bf16 - CPU onednn: Recurrent Neural Network Training - bf16bf16bf16 - CPU onednn: Matrix Multiply Batch Shapes Transformer - f32 - CPU onednn: Recurrent Neural Network Inference - u8s8f32 - CPU onednn: Recurrent Neural Network Training - u8s8f32 - CPU onednn: Recurrent Neural Network Inference - f32 - CPU onednn: Recurrent Neural Network Training - f32 - CPU onednn: Deconvolution Batch shapes_3d - u8s8f32 - CPU onednn: Deconvolution Batch shapes_1d - u8s8f32 - CPU onednn: Convolution Batch Shapes Auto - u8s8f32 - CPU onednn: Deconvolution Batch shapes_3d - f32 - CPU onednn: Deconvolution Batch shapes_1d - f32 - CPU onednn: Convolution Batch Shapes Auto - f32 - CPU onednn: IP Shapes 3D - u8s8f32 - CPU onednn: IP Shapes 1D - u8s8f32 - CPU onednn: IP Shapes 3D - f32 - CPU onednn: IP Shapes 1D - f32 - CPU opencv: DNN - Deep Neural Network mnn: inception-v3 mnn: mobilenet-v1-1.0 mnn: SqueezeNetV1.0 mnn: resnet-v2-50 mnn: squeezenetv1.1 mnn: mobilenetV3 onednn: IP Shapes 1D - bf16bf16bf16 - CPU MBP M1 Max Machine Learning ML Tests 29.89 10.677 43.08 24.9 16.80 29.93 20.30 24.96 43.16 8.69 5.40 30.33 70.89 5.37 1.65 5.30 7.19 20.55 5.33 4.36 20.32 3.47 1.64 30.24 8.71 3.46 20.53 4.35 16.82 7.18 71.01 58.253 8.205 9.967 42.428 7.274 9.152 6.32 2.387 13.12 8.73 6.09 14.55 10.27 13.74 25.17 5.22 3.25 18.82 43.99 3.89 1.20 3.88 5.28 15.36 3.99 3.41 15.95 2.75 1.35 24.97 10.07 3.02 18.56 4.69 15.78 6.90 71.97 2.10 17.60 65.69 48.40 730.736 1463.722 37.51 6.88 12.47 222.326 55.434 249.477 2736.173 868758 173671 86567 325884 65986 33496 2479080 141174 127818 152186 2749623 189764 16.137 0.1293 563 74.44043 422.45 2.98659 2237.65 3577.00 4.59343 2228.17 3587.17 2219.13 3579.00 3.24784 2.11771 23.7674 6.74559 8.34789 22.7926 2.69210 1.62985 12.0926 4.25855 13787 31.576 2.440 4.546 22.441 2.803 1.202 OpenBenchmarking.org
NCNN Target: Vulkan GPU - Model: alexnet OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: Vulkan GPU - Model: alexnet ML Tests MBP M1 Max Machine Learning 7 14 21 28 35 SE +/- 0.03, N = 3 SE +/- 0.00, N = 3 6.32 29.89 -rdynamic -lgomp -lpthread - MIN: 5.95 / MAX: 7.49 -arch -isysroot - MIN: 29.79 / MAX: 31.07 1. (CXX) g++ options: -O3
Mobile Neural Network Model: MobileNetV2_224 OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 1.2 Model: MobileNetV2_224 ML Tests MBP M1 Max Machine Learning 3 6 9 12 15 SE +/- 0.018, N = 3 SE +/- 0.187, N = 9 2.387 10.677 -fomit-frame-pointer -rdynamic -pthread -ldl - MIN: 2.24 / MAX: 17.04 -arch -isysroot - MIN: 5.12 / MAX: 61.59 1. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions
NCNN Target: Vulkan GPU - Model: resnet50 OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: Vulkan GPU - Model: resnet50 ML Tests MBP M1 Max Machine Learning 10 20 30 40 50 SE +/- 0.06, N = 3 SE +/- 0.01, N = 3 13.12 43.08 -rdynamic -lgomp -lpthread - MIN: 12.27 / MAX: 15.04 -arch -isysroot - MIN: 42.9 / MAX: 45.66 1. (CXX) g++ options: -O3
NCNN Target: Vulkan GPU - Model: googlenet OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: Vulkan GPU - Model: googlenet ML Tests MBP M1 Max Machine Learning 6 12 18 24 30 SE +/- 0.25, N = 3 SE +/- 0.00, N = 3 8.73 24.90 -rdynamic -lgomp -lpthread - MIN: 7.89 / MAX: 10.64 -arch -isysroot - MIN: 24.82 / MAX: 25.79 1. (CXX) g++ options: -O3
NCNN Target: Vulkan GPU - Model: resnet18 OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: Vulkan GPU - Model: resnet18 ML Tests MBP M1 Max Machine Learning 4 8 12 16 20 SE +/- 0.08, N = 3 SE +/- 0.01, N = 3 6.09 16.80 -rdynamic -lgomp -lpthread - MIN: 5.63 / MAX: 7.52 -arch -isysroot - MIN: 16.69 / MAX: 18.25 1. (CXX) g++ options: -O3
NCNN Target: CPU - Model: alexnet OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: CPU - Model: alexnet ML Tests MBP M1 Max Machine Learning 7 14 21 28 35 SE +/- 0.06, N = 3 SE +/- 0.05, N = 3 14.55 29.93 -rdynamic -lgomp -lpthread - MIN: 13.9 / MAX: 33.49 -arch -isysroot - MIN: 29.79 / MAX: 31.03 1. (CXX) g++ options: -O3
NCNN Target: Vulkan GPU - Model: mobilenet OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: Vulkan GPU - Model: mobilenet ML Tests MBP M1 Max Machine Learning 5 10 15 20 25 SE +/- 0.09, N = 3 SE +/- 0.02, N = 3 10.27 20.30 -rdynamic -lgomp -lpthread - MIN: 9.59 / MAX: 17.84 -arch -isysroot - MIN: 20.23 / MAX: 21.48 1. (CXX) g++ options: -O3
NCNN Target: CPU - Model: googlenet OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: CPU - Model: googlenet ML Tests MBP M1 Max Machine Learning 6 12 18 24 30 SE +/- 0.28, N = 3 SE +/- 0.07, N = 3 13.74 24.96 -rdynamic -lgomp -lpthread - MIN: 12.47 / MAX: 28.56 -arch -isysroot - MIN: 24.82 / MAX: 25.91 1. (CXX) g++ options: -O3
NCNN Target: CPU - Model: resnet50 OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: CPU - Model: resnet50 ML Tests MBP M1 Max Machine Learning 10 20 30 40 50 SE +/- 0.06, N = 3 SE +/- 0.07, N = 3 25.17 43.16 -rdynamic -lgomp -lpthread - MIN: 23.91 / MAX: 41.27 -arch -isysroot - MIN: 42.92 / MAX: 44.81 1. (CXX) g++ options: -O3
NCNN Target: CPU - Model: efficientnet-b0 OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: CPU - Model: efficientnet-b0 ML Tests MBP M1 Max Machine Learning 2 4 6 8 10 SE +/- 0.01, N = 3 SE +/- 0.04, N = 3 5.22 8.69 -rdynamic -lgomp -lpthread - MIN: 4.86 / MAX: 20.63 -arch -isysroot - MIN: 8.59 / MAX: 9.15 1. (CXX) g++ options: -O3
NCNN Target: CPU - Model: mnasnet OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: CPU - Model: mnasnet ML Tests MBP M1 Max Machine Learning 1.215 2.43 3.645 4.86 6.075 SE +/- 0.03, N = 3 SE +/- 0.03, N = 3 3.25 5.40 -rdynamic -lgomp -lpthread - MIN: 2.82 / MAX: 16.82 -arch -isysroot - MIN: 5.35 / MAX: 5.68 1. (CXX) g++ options: -O3
NCNN Target: Vulkan GPU - Model: yolov4-tiny OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: Vulkan GPU - Model: yolov4-tiny ML Tests MBP M1 Max Machine Learning 7 14 21 28 35 SE +/- 0.42, N = 3 SE +/- 0.07, N = 3 18.82 30.33 -rdynamic -lgomp -lpthread - MIN: 17.12 / MAX: 24.45 -arch -isysroot - MIN: 29.85 / MAX: 32.58 1. (CXX) g++ options: -O3
NCNN Target: Vulkan GPU - Model: vgg16 OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: Vulkan GPU - Model: vgg16 ML Tests MBP M1 Max Machine Learning 16 32 48 64 80 SE +/- 0.07, N = 3 SE +/- 0.02, N = 3 43.99 70.89 -rdynamic -lgomp -lpthread - MIN: 43.17 / MAX: 45.59 -arch -isysroot - MIN: 70.59 / MAX: 73.62 1. (CXX) g++ options: -O3
NCNN Target: Vulkan GPU - Model: mnasnet OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: Vulkan GPU - Model: mnasnet ML Tests MBP M1 Max Machine Learning 1.2083 2.4166 3.6249 4.8332 6.0415 SE +/- 0.11, N = 2 SE +/- 0.00, N = 3 3.89 5.37 -rdynamic -lgomp -lpthread - MIN: 3.56 / MAX: 5.01 -arch -isysroot - MIN: 5.35 / MAX: 5.62 1. (CXX) g++ options: -O3
NCNN Target: CPU - Model: blazeface OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: CPU - Model: blazeface ML Tests MBP M1 Max Machine Learning 0.3713 0.7426 1.1139 1.4852 1.8565 SE +/- 0.01, N = 3 SE +/- 0.01, N = 3 1.20 1.65 -rdynamic -lgomp -lpthread - MIN: 1.16 / MAX: 1.78 -arch -isysroot - MIN: 1.64 / MAX: 1.72 1. (CXX) g++ options: -O3
NCNN Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2 OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2 ML Tests MBP M1 Max Machine Learning 1.1925 2.385 3.5775 4.77 5.9625 SE +/- 0.06, N = 3 SE +/- 0.01, N = 3 3.88 5.30 -rdynamic -lgomp -lpthread - MIN: 3.49 / MAX: 5.25 -arch -isysroot - MIN: 5.28 / MAX: 5.98 1. (CXX) g++ options: -O3
NCNN Target: Vulkan GPU - Model: regnety_400m OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: Vulkan GPU - Model: regnety_400m ML Tests MBP M1 Max Machine Learning 2 4 6 8 10 SE +/- 0.06, N = 3 SE +/- 0.00, N = 3 5.28 7.19 -rdynamic -lgomp -lpthread - MIN: 4.68 / MAX: 6.44 -arch -isysroot - MIN: 7.15 / MAX: 7.72 1. (CXX) g++ options: -O3
NCNN Target: Vulkan GPU - Model: squeezenet_ssd OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: Vulkan GPU - Model: squeezenet_ssd ML Tests MBP M1 Max Machine Learning 5 10 15 20 25 SE +/- 0.36, N = 3 SE +/- 0.05, N = 3 15.36 20.55 -rdynamic -lgomp -lpthread - MIN: 14.17 / MAX: 22.53 -arch -isysroot - MIN: 20.39 / MAX: 22.13 1. (CXX) g++ options: -O3
NCNN Target: CPU-v2-v2 - Model: mobilenet-v2 OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: CPU-v2-v2 - Model: mobilenet-v2 ML Tests MBP M1 Max Machine Learning 1.1993 2.3986 3.5979 4.7972 5.9965 SE +/- 0.02, N = 3 SE +/- 0.03, N = 3 3.99 5.33 -rdynamic -lgomp -lpthread - MIN: 3.71 / MAX: 19.11 -arch -isysroot - MIN: 5.27 / MAX: 5.61 1. (CXX) g++ options: -O3
NCNN Target: CPU-v3-v3 - Model: mobilenet-v3 OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: CPU-v3-v3 - Model: mobilenet-v3 ML Tests MBP M1 Max Machine Learning 0.981 1.962 2.943 3.924 4.905 SE +/- 0.02, N = 3 SE +/- 0.03, N = 3 3.41 4.36 -rdynamic -lgomp -lpthread - MIN: 3.11 / MAX: 17.47 -arch -isysroot - MIN: 4.32 / MAX: 4.61 1. (CXX) g++ options: -O3
NCNN Target: CPU - Model: mobilenet OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: CPU - Model: mobilenet ML Tests MBP M1 Max Machine Learning 5 10 15 20 25 SE +/- 0.09, N = 3 SE +/- 0.02, N = 3 15.95 20.32 -rdynamic -lgomp -lpthread - MIN: 14.92 / MAX: 35.66 -arch -isysroot - MIN: 20.23 / MAX: 21.33 1. (CXX) g++ options: -O3
NCNN Target: CPU - Model: shufflenet-v2 OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: CPU - Model: shufflenet-v2 ML Tests MBP M1 Max Machine Learning 0.7808 1.5616 2.3424 3.1232 3.904 SE +/- 0.04, N = 3 SE +/- 0.02, N = 3 2.75 3.47 -rdynamic -lgomp -lpthread - MIN: 2.48 / MAX: 16.13 -arch -isysroot - MIN: 3.43 / MAX: 3.84 1. (CXX) g++ options: -O3
NCNN Target: Vulkan GPU - Model: blazeface OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: Vulkan GPU - Model: blazeface ML Tests MBP M1 Max Machine Learning 0.369 0.738 1.107 1.476 1.845 SE +/- 0.01, N = 3 SE +/- 0.00, N = 3 1.35 1.64 -rdynamic -lgomp -lpthread - MIN: 1.17 / MAX: 2.43 -arch -isysroot - MIN: 1.63 / MAX: 1.79 1. (CXX) g++ options: -O3
NCNN Target: CPU - Model: yolov4-tiny OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: CPU - Model: yolov4-tiny ML Tests MBP M1 Max Machine Learning 7 14 21 28 35 SE +/- 0.12, N = 3 SE +/- 0.03, N = 3 24.97 30.24 -rdynamic -lgomp -lpthread - MIN: 23.9 / MAX: 38.98 -arch -isysroot - MIN: 29.85 / MAX: 31.87 1. (CXX) g++ options: -O3
NCNN Target: Vulkan GPU - Model: efficientnet-b0 OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: Vulkan GPU - Model: efficientnet-b0 ML Tests MBP M1 Max Machine Learning 3 6 9 12 15 SE +/- 0.06, N = 3 SE +/- 0.02, N = 3 10.07 8.71 -rdynamic -lgomp -lpthread - MIN: 9.06 / MAX: 11.43 -arch -isysroot - MIN: 8.6 / MAX: 9.43 1. (CXX) g++ options: -O3
NCNN Target: Vulkan GPU - Model: shufflenet-v2 OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: Vulkan GPU - Model: shufflenet-v2 ML Tests MBP M1 Max Machine Learning 0.7785 1.557 2.3355 3.114 3.8925 SE +/- 0.07, N = 3 SE +/- 0.01, N = 3 3.02 3.46 -rdynamic -lgomp -lpthread - MIN: 2.54 / MAX: 4.38 -arch -isysroot - MIN: 3.44 / MAX: 3.82 1. (CXX) g++ options: -O3
NCNN Target: CPU - Model: squeezenet_ssd OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: CPU - Model: squeezenet_ssd ML Tests MBP M1 Max Machine Learning 5 10 15 20 25 SE +/- 0.15, N = 3 SE +/- 0.05, N = 3 18.56 20.53 -rdynamic -lgomp -lpthread - MIN: 17.64 / MAX: 34.93 -arch -isysroot - MIN: 20.37 / MAX: 21.53 1. (CXX) g++ options: -O3
NCNN Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3 OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3 ML Tests MBP M1 Max Machine Learning 1.0553 2.1106 3.1659 4.2212 5.2765 SE +/- 0.12, N = 3 SE +/- 0.00, N = 3 4.69 4.35 -rdynamic -lgomp -lpthread - MIN: 4.29 / MAX: 5.94 -arch -isysroot - MIN: 4.32 / MAX: 4.63 1. (CXX) g++ options: -O3
NCNN Target: CPU - Model: resnet18 OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: CPU - Model: resnet18 ML Tests MBP M1 Max Machine Learning 4 8 12 16 20 SE +/- 0.43, N = 3 SE +/- 0.04, N = 3 15.78 16.82 -rdynamic -lgomp -lpthread - MIN: 14.59 / MAX: 30.81 -arch -isysroot - MIN: 16.69 / MAX: 17.58 1. (CXX) g++ options: -O3
NCNN Target: CPU - Model: regnety_400m OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: CPU - Model: regnety_400m ML Tests MBP M1 Max Machine Learning 2 4 6 8 10 SE +/- 0.04, N = 3 SE +/- 0.00, N = 3 6.90 7.18 -rdynamic -lgomp -lpthread - MIN: 6.35 / MAX: 21.53 -arch -isysroot - MIN: 7.14 / MAX: 8.13 1. (CXX) g++ options: -O3
NCNN Target: CPU - Model: vgg16 OpenBenchmarking.org ms, Fewer Is Better NCNN 20210720 Target: CPU - Model: vgg16 ML Tests MBP M1 Max Machine Learning 16 32 48 64 80 SE +/- 0.16, N = 3 SE +/- 0.15, N = 3 71.97 71.01 -rdynamic -lgomp -lpthread - MIN: 69.95 / MAX: 94.76 -arch -isysroot - MIN: 70.58 / MAX: 74.44 1. (CXX) g++ options: -O3
Mlpack Benchmark Benchmark: scikit_linearridgeregression OpenBenchmarking.org Seconds, Fewer Is Better Mlpack Benchmark Benchmark: scikit_linearridgeregression ML Tests 0.4725 0.945 1.4175 1.89 2.3625 SE +/- 0.01, N = 3 2.10
Mlpack Benchmark Benchmark: scikit_svm OpenBenchmarking.org Seconds, Fewer Is Better Mlpack Benchmark Benchmark: scikit_svm ML Tests 4 8 12 16 20 SE +/- 0.02, N = 3 17.60
Mlpack Benchmark Benchmark: scikit_qda OpenBenchmarking.org Seconds, Fewer Is Better Mlpack Benchmark Benchmark: scikit_qda ML Tests 15 30 45 60 75 SE +/- 0.03, N = 3 65.69
Mlpack Benchmark Benchmark: scikit_ica OpenBenchmarking.org Seconds, Fewer Is Better Mlpack Benchmark Benchmark: scikit_ica ML Tests 11 22 33 44 55 SE +/- 0.12, N = 3 48.40
ECP-CANDLE Benchmark: P3B2 OpenBenchmarking.org Seconds, Fewer Is Better ECP-CANDLE 0.4 Benchmark: P3B2 ML Tests 160 320 480 640 800 730.74
ECP-CANDLE Benchmark: P3B1 OpenBenchmarking.org Seconds, Fewer Is Better ECP-CANDLE 0.4 Benchmark: P3B1 ML Tests 300 600 900 1200 1500 1463.72
ECP-CANDLE Benchmark: P1B2 OpenBenchmarking.org Seconds, Fewer Is Better ECP-CANDLE 0.4 Benchmark: P1B2 ML Tests 9 18 27 36 45 37.51
PlaidML FP16: No - Mode: Inference - Network: ResNet 50 - Device: CPU OpenBenchmarking.org FPS, More Is Better PlaidML FP16: No - Mode: Inference - Network: ResNet 50 - Device: CPU ML Tests 2 4 6 8 10 SE +/- 0.02, N = 3 6.88
PlaidML FP16: No - Mode: Inference - Network: VGG16 - Device: CPU OpenBenchmarking.org FPS, More Is Better PlaidML FP16: No - Mode: Inference - Network: VGG16 - Device: CPU ML Tests 3 6 9 12 15 SE +/- 0.07, N = 3 12.47
TNN Target: CPU - Model: SqueezeNet v1.1 OpenBenchmarking.org ms, Fewer Is Better TNN 0.3 Target: CPU - Model: SqueezeNet v1.1 ML Tests 50 100 150 200 250 SE +/- 0.13, N = 3 222.33 MIN: 221.49 / MAX: 224.65 1. (CXX) g++ options: -fopenmp -pthread -fvisibility=hidden -fvisibility=default -O3 -rdynamic -ldl
TNN Target: CPU - Model: SqueezeNet v2 OpenBenchmarking.org ms, Fewer Is Better TNN 0.3 Target: CPU - Model: SqueezeNet v2 ML Tests 12 24 36 48 60 SE +/- 0.62, N = 3 55.43 MIN: 54.24 / MAX: 57.06 1. (CXX) g++ options: -fopenmp -pthread -fvisibility=hidden -fvisibility=default -O3 -rdynamic -ldl
TNN Target: CPU - Model: MobileNet v2 OpenBenchmarking.org ms, Fewer Is Better TNN 0.3 Target: CPU - Model: MobileNet v2 ML Tests 50 100 150 200 250 SE +/- 0.40, N = 3 249.48 MIN: 247.22 / MAX: 255.16 1. (CXX) g++ options: -fopenmp -pthread -fvisibility=hidden -fvisibility=default -O3 -rdynamic -ldl
TNN Target: CPU - Model: DenseNet OpenBenchmarking.org ms, Fewer Is Better TNN 0.3 Target: CPU - Model: DenseNet ML Tests 600 1200 1800 2400 3000 SE +/- 0.83, N = 3 2736.17 MIN: 2687.97 / MAX: 2827.52 1. (CXX) g++ options: -fopenmp -pthread -fvisibility=hidden -fvisibility=default -O3 -rdynamic -ldl
Caffe Model: GoogleNet - Acceleration: CPU - Iterations: 1000 OpenBenchmarking.org Milli-Seconds, Fewer Is Better Caffe 2020-02-13 Model: GoogleNet - Acceleration: CPU - Iterations: 1000 ML Tests 200K 400K 600K 800K 1000K SE +/- 470.76, N = 3 868758 1. (CXX) g++ options: -fPIC -O3 -rdynamic -lglog -lgflags -lprotobuf -lpthread -lsz -lz -ldl -lm -llmdb -lopenblas
Caffe Model: GoogleNet - Acceleration: CPU - Iterations: 200 OpenBenchmarking.org Milli-Seconds, Fewer Is Better Caffe 2020-02-13 Model: GoogleNet - Acceleration: CPU - Iterations: 200 ML Tests 40K 80K 120K 160K 200K SE +/- 318.11, N = 3 173671 1. (CXX) g++ options: -fPIC -O3 -rdynamic -lglog -lgflags -lprotobuf -lpthread -lsz -lz -ldl -lm -llmdb -lopenblas
Caffe Model: GoogleNet - Acceleration: CPU - Iterations: 100 OpenBenchmarking.org Milli-Seconds, Fewer Is Better Caffe 2020-02-13 Model: GoogleNet - Acceleration: CPU - Iterations: 100 ML Tests 20K 40K 60K 80K 100K SE +/- 103.35, N = 3 86567 1. (CXX) g++ options: -fPIC -O3 -rdynamic -lglog -lgflags -lprotobuf -lpthread -lsz -lz -ldl -lm -llmdb -lopenblas
Caffe Model: AlexNet - Acceleration: CPU - Iterations: 1000 OpenBenchmarking.org Milli-Seconds, Fewer Is Better Caffe 2020-02-13 Model: AlexNet - Acceleration: CPU - Iterations: 1000 ML Tests 70K 140K 210K 280K 350K SE +/- 469.87, N = 3 325884 1. (CXX) g++ options: -fPIC -O3 -rdynamic -lglog -lgflags -lprotobuf -lpthread -lsz -lz -ldl -lm -llmdb -lopenblas
Caffe Model: AlexNet - Acceleration: CPU - Iterations: 200 OpenBenchmarking.org Milli-Seconds, Fewer Is Better Caffe 2020-02-13 Model: AlexNet - Acceleration: CPU - Iterations: 200 ML Tests 14K 28K 42K 56K 70K SE +/- 167.60, N = 3 65986 1. (CXX) g++ options: -fPIC -O3 -rdynamic -lglog -lgflags -lprotobuf -lpthread -lsz -lz -ldl -lm -llmdb -lopenblas
Caffe Model: AlexNet - Acceleration: CPU - Iterations: 100 OpenBenchmarking.org Milli-Seconds, Fewer Is Better Caffe 2020-02-13 Model: AlexNet - Acceleration: CPU - Iterations: 100 ML Tests 7K 14K 21K 28K 35K SE +/- 37.32, N = 3 33496 1. (CXX) g++ options: -fPIC -O3 -rdynamic -lglog -lgflags -lprotobuf -lpthread -lsz -lz -ldl -lm -llmdb -lopenblas
TensorFlow Lite Model: Inception ResNet V2 OpenBenchmarking.org Microseconds, Fewer Is Better TensorFlow Lite 2020-08-23 Model: Inception ResNet V2 ML Tests 500K 1000K 1500K 2000K 2500K SE +/- 1189.89, N = 3 2479080
TensorFlow Lite Model: Mobilenet Quant OpenBenchmarking.org Microseconds, Fewer Is Better TensorFlow Lite 2020-08-23 Model: Mobilenet Quant ML Tests 30K 60K 90K 120K 150K SE +/- 38.25, N = 3 141174
TensorFlow Lite Model: Mobilenet Float OpenBenchmarking.org Microseconds, Fewer Is Better TensorFlow Lite 2020-08-23 Model: Mobilenet Float ML Tests 30K 60K 90K 120K 150K SE +/- 174.79, N = 3 127818
TensorFlow Lite Model: NASNet Mobile OpenBenchmarking.org Microseconds, Fewer Is Better TensorFlow Lite 2020-08-23 Model: NASNet Mobile ML Tests 30K 60K 90K 120K 150K SE +/- 344.97, N = 3 152186
TensorFlow Lite Model: Inception V4 OpenBenchmarking.org Microseconds, Fewer Is Better TensorFlow Lite 2020-08-23 Model: Inception V4 ML Tests 600K 1200K 1800K 2400K 3000K SE +/- 1719.91, N = 3 2749623
TensorFlow Lite Model: SqueezeNet OpenBenchmarking.org Microseconds, Fewer Is Better TensorFlow Lite 2020-08-23 Model: SqueezeNet ML Tests 40K 80K 120K 160K 200K SE +/- 108.90, N = 3 189764
RNNoise OpenBenchmarking.org Seconds, Fewer Is Better RNNoise 2020-06-28 ML Tests 4 8 12 16 20 SE +/- 0.02, N = 3 16.14 1. (CC) gcc options: -O2 -pedantic -fvisibility=hidden
R Benchmark OpenBenchmarking.org Seconds, Fewer Is Better R Benchmark ML Tests 0.0291 0.0582 0.0873 0.1164 0.1455 SE +/- 0.0003, N = 3 0.1293 1. R scripting front-end version 4.0.4 (2021-02-15)
LeelaChessZero Backend: BLAS OpenBenchmarking.org Nodes Per Second, More Is Better LeelaChessZero 0.28 Backend: BLAS ML Tests 120 240 360 480 600 SE +/- 5.14, N = 7 563 1. (CXX) g++ options: -flto -pthread
DeepSpeech Acceleration: CPU OpenBenchmarking.org Seconds, Fewer Is Better DeepSpeech 0.6 Acceleration: CPU ML Tests 20 40 60 80 100 SE +/- 0.17, N = 3 74.44
Numpy Benchmark OpenBenchmarking.org Score, More Is Better Numpy Benchmark ML Tests 90 180 270 360 450 SE +/- 0.84, N = 3 422.45
oneDNN Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 2.1.2 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ML Tests 0.672 1.344 2.016 2.688 3.36 SE +/- 0.01068, N = 3 2.98659 MIN: 2.72 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
oneDNN Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 2.1.2 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ML Tests 500 1000 1500 2000 2500 SE +/- 14.65, N = 14 2237.65 MIN: 2174.76 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
oneDNN Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 2.1.2 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ML Tests 800 1600 2400 3200 4000 SE +/- 4.93, N = 3 3577.00 MIN: 3514.72 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
oneDNN Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 2.1.2 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ML Tests 1.0335 2.067 3.1005 4.134 5.1675 SE +/- 0.00541, N = 3 4.59343 MIN: 4.39 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
oneDNN Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 2.1.2 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ML Tests 500 1000 1500 2000 2500 SE +/- 6.34, N = 3 2228.17 MIN: 2189.62 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
oneDNN Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 2.1.2 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ML Tests 800 1600 2400 3200 4000 SE +/- 4.16, N = 3 3587.17 MIN: 3527.15 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
oneDNN Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 2.1.2 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ML Tests 500 1000 1500 2000 2500 SE +/- 1.17, N = 3 2219.13 MIN: 2182.15 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
oneDNN Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 2.1.2 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ML Tests 800 1600 2400 3200 4000 SE +/- 7.39, N = 3 3579.00 MIN: 3519.93 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
oneDNN Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 2.1.2 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ML Tests 0.7308 1.4616 2.1924 2.9232 3.654 SE +/- 0.02694, N = 3 3.24784 MIN: 2.76 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
oneDNN Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 2.1.2 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ML Tests 0.4765 0.953 1.4295 1.906 2.3825 SE +/- 0.00458, N = 3 2.11771 MIN: 1.91 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
oneDNN Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 2.1.2 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ML Tests 6 12 18 24 30 SE +/- 0.02, N = 3 23.77 MIN: 22.9 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
oneDNN Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 2.1.2 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ML Tests 2 4 6 8 10 SE +/- 0.01002, N = 3 6.74559 MIN: 6.52 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
oneDNN Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 2.1.2 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ML Tests 2 4 6 8 10 SE +/- 0.02843, N = 3 8.34789 MIN: 4.75 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
oneDNN Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 2.1.2 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ML Tests 5 10 15 20 25 SE +/- 0.03, N = 3 22.79 MIN: 21.94 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
oneDNN Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 2.1.2 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ML Tests 0.6057 1.2114 1.8171 2.4228 3.0285 SE +/- 0.00222, N = 3 2.69210 MIN: 2.57 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
oneDNN Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 2.1.2 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ML Tests 0.3667 0.7334 1.1001 1.4668 1.8335 SE +/- 0.00920, N = 3 1.62985 MIN: 1.49 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
oneDNN Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 2.1.2 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ML Tests 3 6 9 12 15 SE +/- 0.02, N = 3 12.09 MIN: 11.92 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
oneDNN Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 2.1.2 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ML Tests 0.9582 1.9164 2.8746 3.8328 4.791 SE +/- 0.03780, N = 7 4.25855 MIN: 3.88 1. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
OpenCV Test: DNN - Deep Neural Network OpenBenchmarking.org ms, Fewer Is Better OpenCV 4.5.4 Test: DNN - Deep Neural Network ML Tests 3K 6K 9K 12K 15K SE +/- 269.19, N = 15 13787 1. (CXX) g++ options: -fPIC -fsigned-char -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections -msse -msse2 -msse3 -fvisibility=hidden -O3 -shared
Mobile Neural Network Model: inception-v3 OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 1.2 Model: inception-v3 ML Tests MBP M1 Max Machine Learning 13 26 39 52 65 SE +/- 0.42, N = 3 SE +/- 6.12, N = 9 31.58 58.25 -fomit-frame-pointer -rdynamic -pthread -ldl - MIN: 29.6 / MAX: 48.32 -arch -isysroot - MIN: 30.46 / MAX: 200.21 1. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions
Mobile Neural Network Model: mobilenet-v1-1.0 OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 1.2 Model: mobilenet-v1-1.0 ML Tests MBP M1 Max Machine Learning 2 4 6 8 10 SE +/- 0.019, N = 3 SE +/- 0.384, N = 9 2.440 8.205 -fomit-frame-pointer -rdynamic -pthread -ldl - MIN: 2.17 / MAX: 18 -arch -isysroot - MIN: 4.27 / MAX: 48.5 1. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions
Mobile Neural Network Model: SqueezeNetV1.0 OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 1.2 Model: SqueezeNetV1.0 ML Tests MBP M1 Max Machine Learning 3 6 9 12 15 SE +/- 0.040, N = 3 SE +/- 0.664, N = 9 4.546 9.967 -fomit-frame-pointer -rdynamic -pthread -ldl - MIN: 4.32 / MAX: 20.48 -arch -isysroot - MIN: 4.34 / MAX: 49.52 1. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions
Mobile Neural Network Model: resnet-v2-50 OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 1.2 Model: resnet-v2-50 ML Tests MBP M1 Max Machine Learning 10 20 30 40 50 SE +/- 0.09, N = 3 SE +/- 4.17, N = 9 22.44 42.43 -fomit-frame-pointer -rdynamic -pthread -ldl - MIN: 21.5 / MAX: 43.07 -arch -isysroot - MIN: 24 / MAX: 197.77 1. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions
Mobile Neural Network Model: squeezenetv1.1 OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 1.2 Model: squeezenetv1.1 ML Tests MBP M1 Max Machine Learning 2 4 6 8 10 SE +/- 0.009, N = 3 SE +/- 0.345, N = 9 2.803 7.274 -fomit-frame-pointer -rdynamic -pthread -ldl - MIN: 2.6 / MAX: 17.21 -arch -isysroot - MIN: 2.75 / MAX: 117.92 1. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions
Mobile Neural Network Model: mobilenetV3 OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 1.2 Model: mobilenetV3 ML Tests MBP M1 Max Machine Learning 3 6 9 12 15 SE +/- 0.005, N = 3 SE +/- 0.487, N = 9 1.202 9.152 -arch -isysroot - MIN: 3.37 / MAX: 58.79 1. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions
Phoronix Test Suite v10.8.4