5600x s AMD Ryzen 5 5600X 6-Core testing with a ASUS TUF GAMING B550M-PLUS (WI-FI) (1216 BIOS) and XFX AMD Radeon R9 285/380 2GB on Ubuntu 21.04 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 Western Digital WD_BLACK SN850 1TB, Graphics: XFX AMD Radeon R9 285/380 2GB (918/1375MHz), Audio: AMD Tonga HDMI Audio, Monitor: LG Ultra HD, Network: Realtek RTL8125 2.5GbE + Intel Wi-Fi 6 AX200 OS: Ubuntu 21.04, Kernel: 5.10.0-14-generic (x86_64), Desktop: GNOME Shell 3.38.3, Display Server: X Server 1.20.9 + Wayland, OpenGL: 4.6 Mesa 20.3.4 (LLVM 11.0.1), Compiler: GCC 10.2.1 20210306, File-System: ext4, Screen Resolution: 3840x2160 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 Western Digital WD_BLACK SN850 1TB, Graphics: XFX AMD Radeon R9 285/380 2GB (918/1375MHz), Audio: AMD Tonga HDMI Audio, Monitor: LG Ultra HD, Network: Realtek RTL8125 2.5GbE + Intel Wi-Fi 6 AX200 OS: Ubuntu 21.04, Kernel: 5.10.0-14-generic (x86_64), Desktop: GNOME Shell 3.38.3, Display Server: X Server 1.20.9 + Wayland, OpenGL: 4.6 Mesa 20.3.4 (LLVM 11.0.1), Compiler: GCC 10.2.1 20210306, File-System: ext4, Screen Resolution: 3840x2160 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 Western Digital WD_BLACK SN850 1TB, Graphics: XFX AMD Radeon R9 285/380 2GB (918/1375MHz), Audio: AMD Tonga HDMI Audio, Monitor: LG Ultra HD, Network: Realtek RTL8125 2.5GbE + Intel Wi-Fi 6 AX200 OS: Ubuntu 21.04, Kernel: 5.10.0-14-generic (x86_64), Desktop: GNOME Shell 3.38.3, Display Server: X Server 1.20.9 + Wayland, OpenGL: 4.6 Mesa 20.3.4 (LLVM 11.0.1), Compiler: GCC 10.2.1 20210306, File-System: ext4, Screen Resolution: 3840x2160 oneDNN 2.1.2 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 4.21332 |================================================================== 2 . 4.19463 |================================================================== 3 . 4.19329 |================================================================== oneDNN 2.1.2 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 10.36 |==================================================================== 2 . 10.30 |==================================================================== 3 . 10.30 |==================================================================== oneDNN 2.1.2 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 1.79837 |================================================================== 2 . 1.79867 |================================================================== 3 . 1.79549 |================================================================== oneDNN 2.1.2 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 1.76426 |================================================================== 2 . 1.75512 |================================================================== 3 . 1.75632 |================================================================== oneDNN 2.1.2 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 21.03 |==================================================================== 2 . 20.99 |==================================================================== 3 . 20.96 |==================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 6.54830 |============================================================ 2 . 6.68868 |============================================================= 3 . 7.23395 |================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 7.43471 |================================================================== 2 . 7.44044 |================================================================== 3 . 7.43837 |================================================================== oneDNN 2.1.2 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 16.99 |==================================================================== 2 . 16.99 |==================================================================== 3 . 16.96 |==================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 2.34701 |================================================================== 2 . 2.34952 |================================================================== 3 . 2.34982 |================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3.86561 |================================================================== 2 . 3.88981 |================================================================== 3 . 3.87551 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 3845.26 |================================================================== 2 . 3847.97 |================================================================== 3 . 3843.00 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 2202.60 |================================================================== 2 . 2197.83 |================================================================== 3 . 2200.55 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3852.59 |================================================================== 2 . 3849.16 |================================================================== 3 . 3855.04 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 2199.43 |================================================================== 2 . 2198.47 |================================================================== 3 . 2202.12 |================================================================== oneDNN 2.1.2 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 2.50845 |================================================================== 2 . 2.49156 |================================================================== 3 . 2.47878 |================================================================= oneDNN 2.1.2 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 3853.33 |================================================================== 2 . 3850.92 |================================================================== 3 . 3847.38 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 2198.80 |================================================================== 2 . 2197.21 |================================================================== 3 . 2199.59 |================================================================== oneDNN 2.1.2 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3.09227 |================================================================== 2 . 3.09458 |================================================================== 3 . 3.09158 |================================================================== Sysbench 1.0.20 Test: RAM / Memory MiB/sec > Higher Is Better 1 . 22058.62 |================================================================= 2 . 21958.50 |================================================================= 3 . 22037.31 |================================================================= Sysbench 1.0.20 Test: CPU Events Per Second > Higher Is Better 1 . 35668.15 |================================================================= 2 . 35670.71 |================================================================= 3 . 35672.42 |=================================================================