10600k onednn2 Intel Core i5-10600K testing with a ASUS PRIME Z490M-PLUS (1001 BIOS) and ASUS Intel UHD 630 3GB on Ubuntu 20.04 via the Phoronix Test Suite. 1: Processor: Intel Core i5-10600K @ 4.80GHz (6 Cores / 12 Threads), Motherboard: ASUS PRIME Z490M-PLUS (1001 BIOS), Chipset: Intel Comet Lake PCH, Memory: 32GB, Disk: Samsung SSD 970 EVO 500GB, Graphics: ASUS Intel UHD 630 3GB (1200MHz), Audio: Realtek ALC887-VD, Monitor: G237HL, Network: Intel OS: Ubuntu 20.04, Kernel: 5.9.0-050900daily20201012-generic (x86_64), Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 4.6 Mesa 20.0.8, Vulkan: 1.2.131, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 2: Processor: Intel Core i5-10600K @ 4.80GHz (6 Cores / 12 Threads), Motherboard: ASUS PRIME Z490M-PLUS (1001 BIOS), Chipset: Intel Comet Lake PCH, Memory: 32GB, Disk: Samsung SSD 970 EVO 500GB, Graphics: ASUS Intel UHD 630 3GB (1200MHz), Audio: Realtek ALC887-VD, Monitor: G237HL, Network: Intel OS: Ubuntu 20.04, Kernel: 5.9.0-050900daily20201012-generic (x86_64), Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 4.6 Mesa 20.0.8, Vulkan: 1.2.131, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 3: Processor: Intel Core i5-10600K @ 4.80GHz (6 Cores / 12 Threads), Motherboard: ASUS PRIME Z490M-PLUS (1001 BIOS), Chipset: Intel Comet Lake PCH, Memory: 32GB, Disk: Samsung SSD 970 EVO 500GB, Graphics: ASUS Intel UHD 630 3GB (1200MHz), Audio: Realtek ALC887-VD, Monitor: G237HL, Network: Intel OS: Ubuntu 20.04, Kernel: 5.9.0-050900daily20201012-generic (x86_64), Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 4.6 Mesa 20.0.8, Vulkan: 1.2.131, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 oneDNN 2.0 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 4.29098 |================================================================== 2 . 4.28517 |================================================================== 3 . 4.26419 |================================================================== oneDNN 2.0 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 7.97409 |================================================================== 2 . 7.93681 |================================================================== 3 . 7.93682 |================================================================== oneDNN 2.0 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 2.02164 |================================================================== 2 . 2.02484 |================================================================== 3 . 2.02170 |================================================================== oneDNN 2.0 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 2.31479 |================================================================== 2 . 2.31950 |================================================================== 3 . 2.29186 |================================================================= oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 15.15 |==================================================================== 2 . 15.15 |==================================================================== 3 . 15.14 |==================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 6.02517 |================================================================= 2 . 6.06115 |================================================================= 3 . 6.11065 |================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 8.43163 |================================================================== 2 . 8.43169 |================================================================== 3 . 8.44614 |================================================================== oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 13.79 |==================================================================== 2 . 13.74 |==================================================================== 3 . 13.69 |==================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 6.12680 |================================================================== 2 . 6.09300 |================================================================== 3 . 6.05125 |================================================================= oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3.98936 |================================================================== 2 . 3.98950 |================================================================== 3 . 3.99381 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 3998.52 |================================================================== 2 . 3999.55 |================================================================== 3 . 3997.85 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 2405.15 |================================================================== 2 . 2398.00 |================================================================== 3 . 2400.57 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3999.32 |================================================================== 2 . 3998.54 |================================================================== 3 . 3996.14 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 2400.28 |================================================================== 2 . 2393.76 |================================================================== 3 . 2400.47 |================================================================== oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 3.40204 |================================================================== 2 . 3.40182 |================================================================== 3 . 3.39173 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 3998.20 |================================================================== 2 . 3995.07 |================================================================== 3 . 3993.47 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 2396.32 |================================================================== 2 . 2400.05 |================================================================== 3 . 2396.16 |================================================================== oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 2.95832 |================================================================== 2 . 2.96088 |================================================================== 3 . 2.96592 |================================================================== Timed Clash Compilation Time To Compile Seconds < Lower Is Better 1 . 317.03 |=================================================================== 2 . 316.58 |=================================================================== 3 . 316.63 |===================================================================