Ryzen 5 5600X AMD Ryzen 5 5600X 6-Core testing with a ASUS TUF GAMING B550M-PLUS (WI-FI) (1216 BIOS) and NVIDIA NV166 6GB on Ubuntu 20.10 via the Phoronix Test Suite. 1: Processor: AMD Ryzen 5 5600X 6-Core @ 3.70GHz (6 Cores / 12 Threads), Motherboard: ASUS TUF GAMING B550M-PLUS (WI-FI) (1216 BIOS), Chipset: AMD Starship/Matisse, Memory: 16GB, Disk: 1000GB Samsung SSD 980 PRO 1TB, Graphics: NVIDIA NV166 6GB, Audio: NVIDIA TU106 HD Audio, Monitor: G237HL, Network: Realtek RTL8125 2.5GbE + Intel Wi-Fi 6 AX200 OS: Ubuntu 20.10, Kernel: 5.9.10-050910-generic (x86_64), Desktop: GNOME Shell 3.38.1, Display Server: X Server 1.20.9, Display Driver: modesetting 1.20.9, OpenGL: 4.3 Mesa 20.2.1, Compiler: GCC 10.2.0, File-System: ext4, Screen Resolution: 1920x1080 2: Processor: AMD Ryzen 5 5600X 6-Core @ 3.70GHz (6 Cores / 12 Threads), Motherboard: ASUS TUF GAMING B550M-PLUS (WI-FI) (1216 BIOS), Chipset: AMD Starship/Matisse, Memory: 16GB, Disk: 1000GB Samsung SSD 980 PRO 1TB, Graphics: NVIDIA NV166 6GB, Audio: NVIDIA TU106 HD Audio, Monitor: G237HL, Network: Realtek RTL8125 2.5GbE + Intel Wi-Fi 6 AX200 OS: Ubuntu 20.10, Kernel: 5.9.10-050910-generic (x86_64), Desktop: GNOME Shell 3.38.1, Display Server: X Server 1.20.9, Display Driver: modesetting 1.20.9, OpenGL: 4.3 Mesa 20.2.1, Compiler: GCC 10.2.0, File-System: ext4, Screen Resolution: 1920x1080 3: Processor: AMD Ryzen 5 5600X 6-Core @ 3.70GHz (6 Cores / 12 Threads), Motherboard: ASUS TUF GAMING B550M-PLUS (WI-FI) (1216 BIOS), Chipset: AMD Starship/Matisse, Memory: 16GB, Disk: 1000GB Samsung SSD 980 PRO 1TB, Graphics: NVIDIA NV166 6GB, Audio: NVIDIA TU106 HD Audio, Monitor: G237HL, Network: Realtek RTL8125 2.5GbE + Intel Wi-Fi 6 AX200 OS: Ubuntu 20.10, Kernel: 5.9.10-050910-generic (x86_64), Desktop: GNOME Shell 3.38.1, Display Server: X Server 1.20.9, Display Driver: modesetting 1.20.9, OpenGL: 4.3 Mesa 20.2.1, Compiler: GCC 10.2.0, File-System: ext4, Screen Resolution: 1920x1080 oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 15.96 |=================================================================== 2 . 16.28 |==================================================================== 3 . 16.17 |==================================================================== oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 2.54205 |================================================================== 2 . 2.53107 |================================================================== 3 . 2.51502 |================================================================= oneDNN 2.0 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 1.90790 |================================================================== 2 . 1.91613 |================================================================== 3 . 1.89870 |================================================================= oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 5.60692 |================================================================= 2 . 5.65211 |================================================================== 3 . 5.65539 |================================================================== oneDNN 2.0 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 10.38 |==================================================================== 2 . 10.31 |==================================================================== 3 . 10.31 |==================================================================== oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 20.95 |==================================================================== 2 . 21.08 |==================================================================== 3 . 21.03 |==================================================================== oneDNN 2.0 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 1.70309 |================================================================== 2 . 1.71015 |================================================================== 3 . 1.71271 |================================================================== oneDNN 2.0 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 4.41650 |================================================================== 2 . 4.42871 |================================================================== 3 . 4.40609 |================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3.40352 |================================================================== 2 . 3.41444 |================================================================== 3 . 3.41929 |================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 5.21935 |================================================================== 2 . 5.24175 |================================================================== 3 . 5.23835 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 2241.50 |================================================================== 2 . 2233.39 |================================================================== 3 . 2237.51 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 2236.45 |================================================================== 2 . 2239.18 |================================================================== 3 . 2231.20 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 3922.55 |================================================================== 2 . 3933.03 |================================================================== 3 . 3924.44 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 2231.84 |================================================================== 2 . 2235.83 |================================================================== 3 . 2232.21 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3932.25 |================================================================== 2 . 3935.58 |================================================================== 3 . 3930.91 |================================================================== oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3.47200 |================================================================== 2 . 3.46802 |================================================================== 3 . 3.46915 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 3934.05 |================================================================== 2 . 3931.97 |================================================================== 3 . 3936.22 |================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 7.62028 |================================================================== 2 . 7.62532 |================================================================== 3 . 7.62753 |==================================================================