10900K sysbench onednn Intel Core i9-10900K testing with a Gigabyte Z490 AORUS MASTER (F3 BIOS) and Gigabyte AMD Radeon RX 5500/5500M / Pro 5500M 8GB on Ubuntu 20.10 via the Phoronix Test Suite. 1: Processor: Intel Core i9-10900K @ 5.30GHz (10 Cores / 20 Threads), Motherboard: Gigabyte Z490 AORUS MASTER (F3 BIOS), Chipset: Intel Comet Lake PCH, Memory: 16GB, Disk: Samsung SSD 970 EVO 250GB, Graphics: Gigabyte AMD Radeon RX 5500/5500M / Pro 5500M 8GB (1900/875MHz), Audio: Realtek ALC1220, Monitor: ASUS MG28U, Network: Intel + Intel Wi-Fi 6 AX201 OS: Ubuntu 20.10, Kernel: 5.11.0-051100rc2daily20210106-generic (x86_64) 20210105, Desktop: GNOME Shell 3.38.1, Display Server: X Server 1.20.9, OpenGL: 4.6 Mesa 20.2.1 (LLVM 11.0.0), Vulkan: 1.2.131, Compiler: GCC 10.2.0, File-System: ext4, Screen Resolution: 3840x2160 2: Processor: Intel Core i9-10900K @ 5.30GHz (10 Cores / 20 Threads), Motherboard: Gigabyte Z490 AORUS MASTER (F3 BIOS), Chipset: Intel Comet Lake PCH, Memory: 16GB, Disk: Samsung SSD 970 EVO 250GB, Graphics: Gigabyte AMD Radeon RX 5500/5500M / Pro 5500M 8GB (1900/875MHz), Audio: Realtek ALC1220, Monitor: ASUS MG28U, Network: Intel + Intel Wi-Fi 6 AX201 OS: Ubuntu 20.10, Kernel: 5.11.0-051100rc2daily20210106-generic (x86_64) 20210105, Desktop: GNOME Shell 3.38.1, Display Server: X Server 1.20.9, OpenGL: 4.6 Mesa 20.2.1 (LLVM 11.0.0), Vulkan: 1.2.131, Compiler: GCC 10.2.0, File-System: ext4, Screen Resolution: 3840x2160 3: Processor: Intel Core i9-10900K @ 5.30GHz (10 Cores / 20 Threads), Motherboard: Gigabyte Z490 AORUS MASTER (F3 BIOS), Chipset: Intel Comet Lake PCH, Memory: 16GB, Disk: Samsung SSD 970 EVO 250GB, Graphics: Gigabyte AMD Radeon RX 5500/5500M / Pro 5500M 8GB (1900/875MHz), Audio: Realtek ALC1220, Monitor: ASUS MG28U, Network: Intel + Intel Wi-Fi 6 AX201 OS: Ubuntu 20.10, Kernel: 5.11.0-051100rc2daily20210106-generic (x86_64) 20210105, Desktop: GNOME Shell 3.38.1, Display Server: X Server 1.20.9, OpenGL: 4.6 Mesa 20.2.1 (LLVM 11.0.0), Vulkan: 1.2.131, Compiler: GCC 10.2.0, File-System: ext4, Screen Resolution: 3840x2160 4: Processor: Intel Core i9-10900K @ 5.30GHz (10 Cores / 20 Threads), Motherboard: Gigabyte Z490 AORUS MASTER (F3 BIOS), Chipset: Intel Comet Lake PCH, Memory: 16GB, Disk: Samsung SSD 970 EVO 250GB, Graphics: Gigabyte AMD Radeon RX 5500/5500M / Pro 5500M 8GB (1900/875MHz), Audio: Realtek ALC1220, Monitor: ASUS MG28U, Network: Intel + Intel Wi-Fi 6 AX201 OS: Ubuntu 20.10, Kernel: 5.11.0-051100rc2daily20210106-generic (x86_64) 20210105, Desktop: GNOME Shell 3.38.1, Display Server: X Server 1.20.9, OpenGL: 4.6 Mesa 20.2.1 (LLVM 11.0.0), Vulkan: 1.2.131, Compiler: GCC 10.2.0, File-System: ext4, Screen Resolution: 3840x2160 Sysbench 1.0.20 Test: CPU Events Per Second > Higher Is Better 1 . 26017.89 |================================================================= 2 . 26029.63 |================================================================= 3 . 26028.92 |================================================================= 4 . 26019.11 |================================================================= Sysbench 1.0.20 Test: RAM / Memory MiB/sec > Higher Is Better 1 . 34129.11 |================================================================= 2 . 34243.57 |================================================================= 3 . 34315.67 |================================================================= 4 . 34146.51 |================================================================= oneDNN 2.1.2 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 3.30456 |================================================================== 2 . 3.30545 |================================================================== 3 . 3.30228 |================================================================== 4 . 3.30052 |================================================================== oneDNN 2.1.2 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 12.22 |==================================================================== 2 . 12.18 |==================================================================== 3 . 12.16 |==================================================================== 4 . 12.17 |==================================================================== oneDNN 2.1.2 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 1.17248 |================================================================== 2 . 1.17465 |================================================================== 3 . 1.17258 |================================================================== 4 . 1.17424 |================================================================== oneDNN 2.1.2 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 2.45003 |================================================================= 2 . 2.42769 |================================================================= 3 . 2.47206 |================================================================== 4 . 2.45468 |================================================================== oneDNN 2.1.2 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 21.36 |==================================================================== 2 . 21.33 |==================================================================== 3 . 21.33 |==================================================================== 4 . 21.33 |==================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 6.67724 |================================================================ 2 . 6.85465 |================================================================== 3 . 6.62239 |================================================================ 4 . 6.59741 |================================================================ oneDNN 2.1.2 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 4.82270 |================================================================== 2 . 4.81353 |================================================================== 3 . 4.82441 |================================================================== 4 . 4.83458 |================================================================== oneDNN 2.1.2 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 17.93 |==================================================================== 2 . 17.65 |=================================================================== 3 . 17.69 |=================================================================== 4 . 17.78 |=================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 1.49138 |================================================================== 2 . 1.49445 |================================================================== 3 . 1.49196 |================================================================== 4 . 1.49279 |================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3.76517 |================================================================== 2 . 3.76150 |================================================================== 3 . 3.77453 |================================================================== 4 . 3.74976 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 2843.83 |================================================================== 2 . 2837.76 |================================================================== 3 . 2854.28 |================================================================== 4 . 2818.99 |================================================================= oneDNN 2.1.2 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 1745.36 |================================================================= 2 . 1729.05 |================================================================= 3 . 1742.27 |================================================================= 4 . 1764.34 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 2847.38 |================================================================== 2 . 2837.57 |================================================================== 3 . 2818.43 |================================================================= 4 . 2844.56 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 1779.52 |================================================================== 2 . 1732.40 |================================================================ 3 . 1754.38 |================================================================= 4 . 1744.70 |================================================================= oneDNN 2.1.2 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 3.94807 |================================================================== 2 . 3.92455 |================================================================== 3 . 3.89827 |================================================================= 4 . 3.89701 |================================================================= oneDNN 2.1.2 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 2834.49 |================================================================== 2 . 2836.92 |================================================================== 3 . 2836.90 |================================================================== 4 . 2832.25 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 1748.03 |================================================================== 2 . 1742.26 |================================================================== 3 . 1725.15 |================================================================= 4 . 1749.05 |================================================================== oneDNN 2.1.2 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 1.91115 |================================================================= 2 . 1.93276 |================================================================== 3 . 1.91319 |================================================================= 4 . 1.93351 |==================================================================