i7-8700K
Intel Core i7-8700K testing with a Gigabyte Z370 AORUS Gaming 5-CF (F11 BIOS) and ASUS NVIDIA GeForce GTX 1080 Ti 11GB on Ubuntu 18.04 via the Phoronix Test Suite.


Intel Core i7-8700K: 

	Processor: Intel Core i7-8700K @ 4.70GHz (6 Cores / 12 Threads), Motherboard: Gigabyte Z370 AORUS Gaming 5-CF (F11 BIOS), Chipset: Intel 8th Gen Core, Memory: 32768MB, Disk: Samsung SSD 970 EVO 250GB + 1000GB Crucial_CT1024MX, Graphics: ASUS NVIDIA GeForce GTX 1080 Ti 11GB, Audio: Realtek ALC1220, Network: Intel I219-V + Intel 3165

	OS: Ubuntu 18.04, Kernel: 5.0.0-36-generic (x86_64), Display Server: X Server 1.20.4, Compiler: GCC 6.5.0 20181026 + CUDA 10.0, File-System: ext4, Screen Resolution: 800x600


MKL-DNN DNNL 1.1
Harness: IP Batch 1D - Data Type: f32
ms < Lower Is Better
Intel Core i7-8700K . 6.05417 |================================================


MKL-DNN DNNL 1.1
Harness: IP Batch All - Data Type: f32
ms < Lower Is Better
Intel Core i7-8700K . 24.65 |==================================================


MKL-DNN DNNL 1.1
Harness: IP Batch 1D - Data Type: u8s8f32
ms < Lower Is Better
Intel Core i7-8700K . 62.76 |==================================================


MKL-DNN DNNL 1.1
Harness: IP Batch All - Data Type: u8s8f32
ms < Lower Is Better
Intel Core i7-8700K . 348.89 |=================================================


MKL-DNN DNNL 1.1
Harness: Convolution Batch conv_3d - Data Type: f32
ms < Lower Is Better
Intel Core i7-8700K . 27.56 |==================================================


MKL-DNN DNNL 1.1
Harness: Convolution Batch conv_all - Data Type: f32
ms < Lower Is Better
Intel Core i7-8700K . 3671.91 |================================================


MKL-DNN DNNL 1.1
Harness: Convolution Batch conv_3d - Data Type: u8s8f32
ms < Lower Is Better
Intel Core i7-8700K . 21379.0 |================================================


MKL-DNN DNNL 1.1
Harness: Deconvolution Batch deconv_1d - Data Type: f32
ms < Lower Is Better
Intel Core i7-8700K . 8.43441 |================================================


MKL-DNN DNNL 1.1
Harness: Deconvolution Batch deconv_3d - Data Type: f32
ms < Lower Is Better
Intel Core i7-8700K . 9.84934 |================================================


MKL-DNN DNNL 1.1
Harness: Convolution Batch conv_alexnet - Data Type: f32
ms < Lower Is Better
Intel Core i7-8700K . 479.28 |=================================================


MKL-DNN DNNL 1.1
Harness: Convolution Batch conv_all - Data Type: u8s8f32
ms < Lower Is Better
Intel Core i7-8700K . 64218.4 |================================================


MKL-DNN DNNL 1.1
Harness: Deconvolution Batch deconv_all - Data Type: f32
ms < Lower Is Better
Intel Core i7-8700K . 3647.96 |================================================


MKL-DNN DNNL 1.1
Harness: Deconvolution Batch deconv_1d - Data Type: u8s8f32
ms < Lower Is Better
Intel Core i7-8700K . 12734.1 |================================================


MKL-DNN DNNL 1.1
Harness: Deconvolution Batch deconv_3d - Data Type: u8s8f32
ms < Lower Is Better
Intel Core i7-8700K . 16490.6 |================================================


MKL-DNN DNNL 1.1
Harness: Recurrent Neural Network Training - Data Type: f32
ms < Lower Is Better
Intel Core i7-8700K . 359.40 |=================================================


MKL-DNN DNNL 1.1
Harness: Convolution Batch conv_alexnet - Data Type: u8s8f32
ms < Lower Is Better
Intel Core i7-8700K . 5821.65 |================================================


MKL-DNN DNNL 1.1
Harness: Convolution Batch conv_googlenet_v3 - Data Type: f32
ms < Lower Is Better
Intel Core i7-8700K . 215.80 |=================================================


MKL-DNN DNNL 1.1
Harness: Convolution Batch conv_googlenet_v3 - Data Type: u8s8f32
ms < Lower Is Better
Intel Core i7-8700K . 3013.31 |================================================