Cascadelake MKL-DNN 2 x Intel Xeon Platinum 8280 testing with a GIGABYTE MD61-SC2-00 v01000100 (T15 BIOS) and ASPEED Family on Ubuntu 18.04 via the Phoronix Test Suite.
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phoronix-test-suite benchmark 1904193-HV-CASCADELA65 2 x 8280 Processor: 2 x Intel Xeon Platinum 8280 @ 4.00GHz (56 Cores / 112 Threads), Motherboard: GIGABYTE MD61-SC2-00 v01000100 (T15 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 386048MB, Disk: Samsung SSD 970 PRO 512GB, Graphics: ASPEED Family, Monitor: VE228, Network: 2 x Intel X722 for 1GbE + 2 x QLogic FastLinQ QL41000 10/25/40/50GbE
OS: Ubuntu 18.04, Kernel: 5.1.0-999-generic (x86_64) 20190416, Desktop: GNOME Shell 3.28.3, Display Server: X Server 1.20.1, Display Driver: modesetting 1.20.1, Compiler: GCC 7.3.0, File-System: ext4, Screen Resolution: 1920x1080
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --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++ --enable-libmpx --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none --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 --with-tune=generic --without-cuda-driver -vProcessor Notes: Scaling Governor: intel_pstate powersaveSecurity Notes: __user pointer sanitization + Enhanced IBRS IBPB: conditional RSB filling + SSB disabled via prctl and seccomp
Cascadelake MKL-DNN OpenBenchmarking.org Phoronix Test Suite 2 x Intel Xeon Platinum 8280 @ 4.00GHz (56 Cores / 112 Threads) GIGABYTE MD61-SC2-00 v01000100 (T15 BIOS) Intel Sky Lake-E DMI3 Registers 386048MB Samsung SSD 970 PRO 512GB ASPEED Family VE228 2 x Intel X722 for 1GbE + 2 x QLogic FastLinQ QL41000 10/25/40/50GbE Ubuntu 18.04 5.1.0-999-generic (x86_64) 20190416 GNOME Shell 3.28.3 X Server 1.20.1 modesetting 1.20.1 GCC 7.3.0 ext4 1920x1080 Processor Motherboard Chipset Memory Disk Graphics Monitor Network OS Kernel Desktop Display Server Display Driver Compiler File-System Screen Resolution Cascadelake MKL-DNN Benchmarks System Logs - --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --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++ --enable-libmpx --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none --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 --with-tune=generic --without-cuda-driver -v - Scaling Governor: intel_pstate powersave - __user pointer sanitization + Enhanced IBRS IBPB: conditional RSB filling + SSB disabled via prctl and seccomp
Cascadelake MKL-DNN mkl-dnn: Convolution Batch conv_googlenet_v3 - u8s8f32s32 mkl-dnn: Convolution Batch conv_googlenet_v3 - u8s8u8s32 mkl-dnn: Convolution Batch conv_alexnet - u8s8f32s32 mkl-dnn: Deconvolution Batch deconv_all - u8s8u8s32 mkl-dnn: Deconvolution Batch deconv_3d - u8s8f32s32 mkl-dnn: Deconvolution Batch deconv_1d - u8s8f32s32 mkl-dnn: Convolution Batch conv_alexnet - u8s8u8s32 mkl-dnn: Deconvolution Batch deconv_3d - u8s8u8s32 mkl-dnn: Deconvolution Batch deconv_1d - u8s8u8s32 mkl-dnn: Convolution Batch conv_googlenet_v3 - f32 mkl-dnn: Convolution Batch conv_all - u8s8f32s32 mkl-dnn: Convolution Batch conv_all - u8s8u8s32 mkl-dnn: Convolution Batch conv_3d - u8s8f32s32 mkl-dnn: Convolution Batch conv_3d - u8s8u8s32 mkl-dnn: Deconvolution Batch deconv_all - f32 mkl-dnn: Convolution Batch conv_alexnet - f32 mkl-dnn: Deconvolution Batch deconv_3d - f32 mkl-dnn: Deconvolution Batch deconv_1d - f32 mkl-dnn: Convolution Batch conv_all - f32 mkl-dnn: Convolution Batch conv_3d - f32 mkl-dnn: IP Batch All - u8s8f32s32 mkl-dnn: IP Batch All - u8s8u8s32 mkl-dnn: IP Batch 1D - u8s8f32s32 mkl-dnn: IP Batch 1D - u8s8u8s32 mkl-dnn: IP Batch All - f32 mkl-dnn: IP Batch 1D - f32 2 x 8280 6.16 5.71 19.24 3982.35 2181.05 0.23 14.06 2256.19 0.23 21.13 1300.71 1260 2633.71 2607.67 984 48.42 1.10 0.95 382 3.29 20.38 20.45 3.63 3.54 101.17 11.65 OpenBenchmarking.org
MKL-DNN This is a test of the Intel MKL-DNN as the Intel Math Kernel Library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Convolution Batch conv_googlenet_v3 - Data Type: u8s8f32s32 2 x 8280 2 4 6 8 10 SE +/- 0.02, N = 3 6.16 MIN: 5.89 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Convolution Batch conv_googlenet_v3 - Data Type: u8s8u8s32 2 x 8280 1.2848 2.5696 3.8544 5.1392 6.424 SE +/- 0.01, N = 3 5.71 MIN: 5.52 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Convolution Batch conv_alexnet - Data Type: u8s8f32s32 2 x 8280 5 10 15 20 25 SE +/- 0.05, N = 3 19.24 MIN: 18.45 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Deconvolution Batch deconv_all - Data Type: u8s8u8s32 2 x 8280 900 1800 2700 3600 4500 SE +/- 6.44, N = 3 3982.35 MIN: 3926.33 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Deconvolution Batch deconv_3d - Data Type: u8s8f32s32 2 x 8280 500 1000 1500 2000 2500 SE +/- 2.64, N = 3 2181.05 MIN: 2170.15 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Deconvolution Batch deconv_1d - Data Type: u8s8f32s32 2 x 8280 0.0518 0.1036 0.1554 0.2072 0.259 SE +/- 0.00, N = 3 0.23 MIN: 0.21 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Convolution Batch conv_alexnet - Data Type: u8s8u8s32 2 x 8280 4 8 12 16 20 SE +/- 0.00, N = 3 14.06 MIN: 13.8 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Deconvolution Batch deconv_3d - Data Type: u8s8u8s32 2 x 8280 500 1000 1500 2000 2500 SE +/- 1.11, N = 3 2256.19 MIN: 2251.87 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Deconvolution Batch deconv_1d - Data Type: u8s8u8s32 2 x 8280 0.0518 0.1036 0.1554 0.2072 0.259 SE +/- 0.00, N = 3 0.23 MIN: 0.21 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Convolution Batch conv_googlenet_v3 - Data Type: f32 2 x 8280 5 10 15 20 25 SE +/- 0.05, N = 3 21.13 MIN: 20.67 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Convolution Batch conv_all - Data Type: u8s8f32s32 2 x 8280 300 600 900 1200 1500 SE +/- 2.20, N = 3 1300.71 MIN: 1292.31 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Convolution Batch conv_all - Data Type: u8s8u8s32 2 x 8280 300 600 900 1200 1500 SE +/- 1.87, N = 3 1260 MIN: 1253.93 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Convolution Batch conv_3d - Data Type: u8s8f32s32 2 x 8280 600 1200 1800 2400 3000 SE +/- 2.40, N = 3 2633.71 MIN: 2625.77 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Convolution Batch conv_3d - Data Type: u8s8u8s32 2 x 8280 600 1200 1800 2400 3000 SE +/- 0.25, N = 3 2607.67 MIN: 2602.21 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Deconvolution Batch deconv_all - Data Type: f32 2 x 8280 200 400 600 800 1000 SE +/- 9.53, N = 3 984 MIN: 921.29 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Convolution Batch conv_alexnet - Data Type: f32 2 x 8280 11 22 33 44 55 SE +/- 0.07, N = 3 48.42 MIN: 47.5 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Deconvolution Batch deconv_3d - Data Type: f32 2 x 8280 0.2475 0.495 0.7425 0.99 1.2375 SE +/- 0.01, N = 3 1.10 MIN: 1.06 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Deconvolution Batch deconv_1d - Data Type: f32 2 x 8280 0.2138 0.4276 0.6414 0.8552 1.069 SE +/- 0.00, N = 3 0.95 MIN: 0.91 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Convolution Batch conv_all - Data Type: f32 2 x 8280 80 160 240 320 400 SE +/- 0.51, N = 3 382 MIN: 375.61 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Convolution Batch conv_3d - Data Type: f32 2 x 8280 0.7403 1.4806 2.2209 2.9612 3.7015 SE +/- 0.01, N = 3 3.29 MIN: 3.15 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: IP Batch All - Data Type: u8s8f32s32 2 x 8280 5 10 15 20 25 SE +/- 0.28, N = 3 20.38 MIN: 13.21 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: IP Batch All - Data Type: u8s8u8s32 2 x 8280 5 10 15 20 25 SE +/- 0.04, N = 3 20.45 MIN: 13.03 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: IP Batch 1D - Data Type: u8s8f32s32 2 x 8280 0.8168 1.6336 2.4504 3.2672 4.084 SE +/- 0.04, N = 7 3.63 MIN: 2.28 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: IP Batch 1D - Data Type: u8s8u8s32 2 x 8280 0.7965 1.593 2.3895 3.186 3.9825 SE +/- 0.04, N = 8 3.54 MIN: 2.16 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: IP Batch All - Data Type: f32 2 x 8280 20 40 60 80 100 SE +/- 0.52, N = 3 101.17 MIN: 59.17 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: IP Batch 1D - Data Type: f32 2 x 8280 3 6 9 12 15 SE +/- 0.17, N = 3 11.65 MIN: 6.62 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
2 x 8280 Processor: 2 x Intel Xeon Platinum 8280 @ 4.00GHz (56 Cores / 112 Threads), Motherboard: GIGABYTE MD61-SC2-00 v01000100 (T15 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 386048MB, Disk: Samsung SSD 970 PRO 512GB, Graphics: ASPEED Family, Monitor: VE228, Network: 2 x Intel X722 for 1GbE + 2 x QLogic FastLinQ QL41000 10/25/40/50GbE
OS: Ubuntu 18.04, Kernel: 5.1.0-999-generic (x86_64) 20190416, Desktop: GNOME Shell 3.28.3, Display Server: X Server 1.20.1, Display Driver: modesetting 1.20.1, Compiler: GCC 7.3.0, File-System: ext4, Screen Resolution: 1920x1080
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --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++ --enable-libmpx --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none --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 --with-tune=generic --without-cuda-driver -vProcessor Notes: Scaling Governor: intel_pstate powersaveSecurity Notes: __user pointer sanitization + Enhanced IBRS IBPB: conditional RSB filling + SSB disabled via prctl and seccomp
Testing initiated at 19 April 2019 07:36 by user phoronix.