DNNL 9900K
Intel Core i9-9900K testing with a ASUS PRIME Z390-A (1302 BIOS) and MSI AMD Radeon RX 470/480/570/570X/580/580X 8GB on Ubuntu 19.04 via the Phoronix Test Suite.


Core i9 9900K: 

	Processor: Intel Core i9-9900K @ 5.00GHz (8 Cores / 16 Threads), Motherboard: ASUS PRIME Z390-A (1302 BIOS), Chipset: Intel Cannon Lake PCH, Memory: 16384MB, Disk: Samsung SSD 970 EVO 250GB + 2000GB SABRENT, Graphics: MSI AMD Radeon RX 470/480/570/570X/580/580X 8GB (1366/2000MHz), Audio: Realtek ALC1220, Monitor: Acer B286HK, Network: Intel I219-V

	OS: Ubuntu 19.04, Kernel: 5.4.0-999-generic (x86_64) 20191004, Desktop: GNOME Shell 3.32.2, Display Server: X Server 1.20.4, Display Driver: modesetting 1.20.4, OpenGL: 4.5 Mesa 19.3.0-devel (git-396b410 2019-10-05 disco-oibaf-ppa) (LLVM 9.0.0), Compiler: GCC 8.3.0, File-System: ext4, Screen Resolution: 3840x2160


MKL-DNN DNNL 1.1
Harness: Convolution Batch conv_googlenet_v3 - Data Type: f32
ms < Lower Is Better
Core i9 9900K . 165.97 |=======================================================


MKL-DNN DNNL 1.1
Harness: Convolution Batch conv_alexnet - Data Type: u8s8f32
ms < Lower Is Better
Core i9 9900K . 3682.60 |======================================================


MKL-DNN DNNL 1.1
Harness: Recurrent Neural Network Training - Data Type: f32
ms < Lower Is Better
Core i9 9900K . 274.51 |=======================================================


MKL-DNN DNNL 1.1
Harness: Deconvolution Batch deconv_3d - Data Type: u8s8f32
ms < Lower Is Better
Core i9 9900K . 9810.19 |======================================================


MKL-DNN DNNL 1.1
Harness: Deconvolution Batch deconv_1d - Data Type: u8s8f32
ms < Lower Is Better
Core i9 9900K . 6041.64 |======================================================


MKL-DNN DNNL 1.1
Harness: Deconvolution Batch deconv_all - Data Type: f32
ms < Lower Is Better
Core i9 9900K . 3227.73 |======================================================


MKL-DNN DNNL 1.1
Harness: Convolution Batch conv_all - Data Type: u8s8f32
ms < Lower Is Better
Core i9 9900K . 47603.10 |=====================================================


MKL-DNN DNNL 1.1
Harness: Convolution Batch conv_alexnet - Data Type: f32
ms < Lower Is Better
Core i9 9900K . 374.81 |=======================================================


MKL-DNN DNNL 1.1
Harness: Deconvolution Batch deconv_3d - Data Type: f32
ms < Lower Is Better
Core i9 9900K . 6.64 |=========================================================


MKL-DNN DNNL 1.1
Harness: Deconvolution Batch deconv_1d - Data Type: f32
ms < Lower Is Better
Core i9 9900K . 5.86 |=========================================================


MKL-DNN DNNL 1.1
Harness: Convolution Batch conv_3d - Data Type: u8s8f32
ms < Lower Is Better
Core i9 9900K . 17489.70 |=====================================================


MKL-DNN DNNL 1.1
Harness: Convolution Batch conv_all - Data Type: f32
ms < Lower Is Better
Core i9 9900K . 2948.49 |======================================================


MKL-DNN DNNL 1.1
Harness: Convolution Batch conv_3d - Data Type: f32
ms < Lower Is Better
Core i9 9900K . 23.83 |========================================================


MKL-DNN DNNL 1.1
Harness: IP Batch All - Data Type: u8s8f32
ms < Lower Is Better
Core i9 9900K . 247.61 |=======================================================


MKL-DNN DNNL 1.1
Harness: IP Batch 1D - Data Type: u8s8f32
ms < Lower Is Better
Core i9 9900K . 44.10 |========================================================


MKL-DNN DNNL 1.1
Harness: IP Batch All - Data Type: f32
ms < Lower Is Better
Core i9 9900K . 34.29 |========================================================


MKL-DNN DNNL 1.1
Harness: IP Batch 1D - Data Type: f32
ms < Lower Is Better
Core i9 9900K . 4.75 |=========================================================


MKL-DNN DNNL 1.1
Harness: Convolution Batch conv_googlenet_v3 - Data Type: u8s8f32
ms < Lower Is Better
Core i9 9900K . 5700.46 |======================================================