MKL-DNN Cascade Lake

2 x Intel Xeon Platinum 8280 testing with a GIGABYTE MD61-SC2-00 v01000100 (T15 BIOS) and ASPEED on Ubuntu 19.04 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 1905231-HV-MKLDNNCAS40
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Result
Identifier
Performance Per
Dollar
Date
Run
  Test
  Duration
2 x Intel Xeon Platinum 8280
May 23 2019
  3 Hours, 2 Minutes
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MKL-DNN Cascade Lake 2 x Intel Xeon Platinum 8280 testing with a GIGABYTE MD61-SC2-00 v01000100 (T15 BIOS) and ASPEED on Ubuntu 19.04 via the Phoronix Test Suite. ,,"2 x Intel Xeon Platinum 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 Monitor,,VE228 Network,,2 x Intel X722 for 1GbE + 2 x QLogic FastLinQ QL41000 10/25/40/50GbE OS,,Ubuntu 19.04 Kernel,,5.0.0-15-generic (x86_64) Desktop,,GNOME Shell 3.32.0 Display Server,,X Server 1.20.4 Display Driver,,modesetting 1.20.4 Compiler,,GCC 8.3.0 File-System,,ext4 Screen Resolution,,1920x1080 ,,"2 x Intel Xeon Platinum 8280" "MKL-DNN - Harness: Convolution Batch conv_all - Data Type: u8s8u8s32 (ms)",LIB,1716.72 "MKL-DNN - Harness: Convolution Batch conv_all - Data Type: u8s8f32s32 (ms)",LIB,1754.68 "MKL-DNN - Harness: Convolution Batch conv_all - Data Type: f32 (ms)",LIB,388.65 "MKL-DNN - Harness: Deconvolution Batch deconv_all - Data Type: u8s8u8s32 (ms)",LIB,3670.07 "MKL-DNN - Harness: Deconvolution Batch deconv_all - Data Type: f32 (ms)",LIB,1035.47 "MKL-DNN - Harness: Convolution Batch conv_googlenet_v3 - Data Type: u8s8u8s32 (ms)",LIB,5.80 "MKL-DNN - Harness: Convolution Batch conv_googlenet_v3 - Data Type: u8s8f32s32 (ms)",LIB,6.23 "MKL-DNN - Harness: Convolution Batch conv_googlenet_v3 - Data Type: f32 (ms)",LIB,21.60 "MKL-DNN - Harness: IP Batch 1D - Data Type: u8s8u8s32 (ms)",LIB,3.53 "MKL-DNN - Harness: Convolution Batch conv_3d - Data Type: u8s8u8s32 (ms)",LIB,3547.34 "MKL-DNN - Harness: Convolution Batch conv_3d - Data Type: u8s8f32s32 (ms)",LIB,3536.77 "MKL-DNN - Harness: IP Batch All - Data Type: f32 (ms)",LIB,98.64 "MKL-DNN - Harness: IP Batch All - Data Type: u8s8f32s32 (ms)",LIB,20.15 "MKL-DNN - Harness: IP Batch All - Data Type: u8s8u8s32 (ms)",LIB,20.20 "MKL-DNN - Harness: Convolution Batch conv_3d - Data Type: f32 (ms)",LIB,3.32 "MKL-DNN - Harness: IP Batch 1D - Data Type: f32 (ms)",LIB,11.75 "MKL-DNN - Harness: Deconvolution Batch deconv_1d - Data Type: u8s8u8s32 (ms)",LIB,0.24 "MKL-DNN - Harness: Deconvolution Batch deconv_1d - Data Type: u8s8f32s32 (ms)",LIB,0.23 "MKL-DNN - Harness: Deconvolution Batch deconv_1d - Data Type: f32 (ms)",LIB,0.96 "MKL-DNN - Harness: Convolution Batch conv_alexnet - Data Type: u8s8u8s32 (ms)",LIB,14.38 "MKL-DNN - Harness: Convolution Batch conv_alexnet - Data Type: u8s8f32s32 (ms)",LIB,19.03 "MKL-DNN - Harness: Convolution Batch conv_alexnet - Data Type: f32 (ms)",LIB,48.89 "MKL-DNN - Harness: IP Batch 1D - Data Type: u8s8f32s32 (ms)",LIB,3.62 "MKL-DNN - Harness: Deconvolution Batch deconv_3d - Data Type: u8s8u8s32 (ms)",LIB,2040.83 "MKL-DNN - Harness: Deconvolution Batch deconv_3d - Data Type: u8s8f32s32 (ms)",LIB,1891.74 "MKL-DNN - Harness: Deconvolution Batch deconv_3d - Data Type: f32 (ms)",LIB,1.11