baseline
AMD Ryzen 7 1700 Eight-Core testing with a ASUS PRIME B350-PLUS (5007 BIOS) and eVGA NVIDIA GeForce GTX 1080 Ti 11GB on Arch rolling via the Phoronix Test Suite.


mainlinelinuxai: 

	Processor: AMD Ryzen 7 1700 Eight-Core @ 3.00GHz (8 Cores / 16 Threads), Motherboard: ASUS PRIME B350-PLUS (5007 BIOS), Chipset: AMD 17h, Memory: 32GB, Disk: Samsung SSD 960 EVO 250GB + 3001GB Seagate ST3000DM008-2DM1 + 2000GB Western Digital WD20EFRX-68E + 2 x 2000GB Seagate ST2000DM008-2FR1, Graphics: eVGA NVIDIA GeForce GTX 1080 Ti 11GB (1556/5508MHz), Audio: NVIDIA GP102 HDMI Audio, Monitor: U2777B, Network: Realtek RTL8111/8168/8411

	OS: Arch rolling, Kernel: 5.7.8-arch1-1 (x86_64), Display Server: X Server 1.20.8, Display Driver: NVIDIA 450.57, OpenGL: 4.6.0, Compiler: GCC 10.1.0 + Clang 10.0.0 + LLVM 10.0.0 + ICC + CUDA 10.2, File-System: ext4, Screen Resolution: 11520x2160


Timed FFmpeg Compilation 4.2.2
Time To Compile
Seconds < Lower Is Better
mainlinelinuxai . 91.24 |======================================================


oneDNN 1.5
Harness: IP Batch 1D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
mainlinelinuxai . 10.85 |======================================================


oneDNN 1.5
Harness: IP Batch All - Data Type: f32 - Engine: CPU
ms < Lower Is Better
mainlinelinuxai . 137.04 |=====================================================


oneDNN 1.5
Harness: IP Batch 1D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
mainlinelinuxai . 7.81265 |====================================================


oneDNN 1.5
Harness: IP Batch All - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
mainlinelinuxai . 91.61 |======================================================


oneDNN 1.5
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
ms < Lower Is Better
mainlinelinuxai . 21.06 |======================================================


oneDNN 1.5
Harness: Deconvolution Batch deconv_1d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
mainlinelinuxai . 12.25 |======================================================


oneDNN 1.5
Harness: Deconvolution Batch deconv_3d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
mainlinelinuxai . 18.15 |======================================================


oneDNN 1.5
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
mainlinelinuxai . 21.85 |======================================================


oneDNN 1.5
Harness: Deconvolution Batch deconv_1d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
mainlinelinuxai . 17.89 |======================================================


oneDNN 1.5
Harness: Deconvolution Batch deconv_3d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
mainlinelinuxai . 16.16 |======================================================


oneDNN 1.5
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
ms < Lower Is Better
mainlinelinuxai . 1064.85 |====================================================


oneDNN 1.5
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
ms < Lower Is Better
mainlinelinuxai . 282.76 |=====================================================


oneDNN 1.5
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU
ms < Lower Is Better
mainlinelinuxai . 5.96066 |====================================================


oneDNN 1.5
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
mainlinelinuxai . 6.74795 |====================================================