MKL DNN UPDATED

Intel Core i9-9900K testing with a ASUS PRIME Z390-A (0802 BIOS) and AMD Radeon RX 64 8GB 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 1904199-PTS-MKLDNNUP03
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Result
Identifier
Performance Per
Dollar
Date
Run
  Test
  Duration
Intel Core i9-9900K
April 18 2019
  3 Hours, 21 Minutes
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MKL DNN UPDATED Intel Core i9-9900K testing with a ASUS PRIME Z390-A (0802 BIOS) and AMD Radeon RX 64 8GB on Ubuntu 19.04 via the Phoronix Test Suite. ,,"Intel Core i9-9900K" Processor,,Intel Core i9-9900K @ 5.00GHz (8 Cores / 16 Threads) Motherboard,,ASUS PRIME Z390-A (0802 BIOS) Chipset,,Intel Cannon Lake PCH Memory,,16384MB Disk,,Samsung SSD 970 EVO 250GB + 2000GB SABRENT Graphics,,AMD Radeon RX 64 8GB (1630/945MHz) Audio,,Realtek ALC1220 Monitor,,Acer B286HK Network,,Intel I219-V OS,,Ubuntu 19.04 Kernel,,5.0.0-11-generic (x86_64) Desktop,,GNOME Shell 3.32.0 Display Server,,X Server 1.20.4 Display Driver,,amdgpu 19.0.1 OpenGL,,4.5 Mesa 19.0.2 (LLVM 8.0.0) Vulkan,,1.1.90 Compiler,,GCC 8.3.0 File-System,,ext4 Screen Resolution,,3840x2160 ,,"Intel Core i9-9900K" "MKL-DNN - Harness: IP Batch 1D - Data Type: f32 (ms)",LIB,8.37 "MKL-DNN - Harness: IP Batch All - Data Type: f32 (ms)",LIB,110.92 "MKL-DNN - Harness: IP Batch 1D - Data Type: u8s8u8s32 (ms)",LIB,4.91 "MKL-DNN - Harness: IP Batch 1D - Data Type: u8s8f32s32 (ms)",LIB,4.82 "MKL-DNN - Harness: IP Batch All - Data Type: u8s8u8s32 (ms)",LIB,58.41 "MKL-DNN - Harness: IP Batch All - Data Type: u8s8f32s32 (ms)",LIB,57.96 "MKL-DNN - Harness: Convolution Batch conv_3d - Data Type: f32 (ms)",LIB,22.88 "MKL-DNN - Harness: Convolution Batch conv_all - Data Type: f32 (ms)",LIB,2847.67 "MKL-DNN - Harness: Deconvolution Batch deconv_1d - Data Type: f32 (ms)",LIB,5.72 "MKL-DNN - Harness: Deconvolution Batch deconv_3d - Data Type: f32 (ms)",LIB,6.66 "MKL-DNN - Harness: Convolution Batch conv_alexnet - Data Type: f32 (ms)",LIB,359.73 "MKL-DNN - Harness: Deconvolution Batch deconv_all - Data Type: f32 (ms)",LIB,2979.74 "MKL-DNN - Harness: Convolution Batch conv_3d - Data Type: u8s8u8s32 (ms)",LIB,16864.30 "MKL-DNN - Harness: Convolution Batch conv_3d - Data Type: u8s8f32s32 (ms)",LIB,16694.07 "MKL-DNN - Harness: Convolution Batch conv_all - Data Type: u8s8u8s32 (ms)",LIB,18517.23 "MKL-DNN - Harness: Convolution Batch conv_all - Data Type: u8s8f32s32 (ms)",LIB,18168.07 "MKL-DNN - Harness: Convolution Batch conv_googlenet_v3 - Data Type: f32 (ms)",LIB,161.01 "MKL-DNN - Harness: Deconvolution Batch deconv_1d - Data Type: u8s8u8s32 (ms)",LIB,6248.42 "MKL-DNN - Harness: Deconvolution Batch deconv_3d - Data Type: u8s8u8s32 (ms)",LIB,10567.83 "MKL-DNN - Harness: Convolution Batch conv_alexnet - Data Type: u8s8u8s32 (ms)",LIB,404.68 "MKL-DNN - Harness: Deconvolution Batch deconv_1d - Data Type: u8s8f32s32 (ms)",LIB,5589.26 "MKL-DNN - Harness: Deconvolution Batch deconv_3d - Data Type: u8s8f32s32 (ms)",LIB,9604.28 "MKL-DNN - Harness: Deconvolution Batch deconv_all - Data Type: u8s8u8s32 (ms)",LIB,19713.03 "MKL-DNN - Harness: Convolution Batch conv_alexnet - Data Type: u8s8f32s32 (ms)",LIB,380.52 "MKL-DNN - Harness: Convolution Batch conv_googlenet_v3 - Data Type: u8s8u8s32 (ms)",LIB,435.77 "MKL-DNN - Harness: Convolution Batch conv_googlenet_v3 - Data Type: u8s8f32s32 (ms)",LIB,415.07