1280p updated tests Intel Core i7-1280P testing with a MSI MS-14C6 (E14C6IMS.115 BIOS) and MSI Intel ADL GT2 14GB on Ubuntu 22.10 via the Phoronix Test Suite. A: Processor: Intel Core i7-1280P @ 4.80GHz (14 Cores / 20 Threads), Motherboard: MSI MS-14C6 (E14C6IMS.115 BIOS), Chipset: Intel Alder Lake PCH, Memory: 16GB, Disk: 1024GB Micron_3400_MTFDKBA1T0TFH, Graphics: MSI Intel ADL GT2 14GB (1450MHz), Audio: Realtek ALC274, Network: Intel Alder Lake-P PCH CNVi WiFi OS: Ubuntu 22.10, Kernel: 5.15.0-27-generic (x86_64), Desktop: GNOME Shell, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 22.1.7, Vulkan: 1.3.211, Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 1920x1080 B: Processor: Intel Core i7-1280P @ 4.80GHz (14 Cores / 20 Threads), Motherboard: MSI MS-14C6 (E14C6IMS.115 BIOS), Chipset: Intel Alder Lake PCH, Memory: 16GB, Disk: 1024GB Micron_3400_MTFDKBA1T0TFH, Graphics: MSI Intel ADL GT2 14GB (1450MHz), Audio: Realtek ALC274, Network: Intel Alder Lake-P PCH CNVi WiFi OS: Ubuntu 22.10, Kernel: 5.15.0-27-generic (x86_64), Desktop: GNOME Shell, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 22.1.7, Vulkan: 1.3.211, Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 1920x1080 C: Processor: Intel Core i7-1280P @ 4.80GHz (14 Cores / 20 Threads), Motherboard: MSI MS-14C6 (E14C6IMS.115 BIOS), Chipset: Intel Alder Lake PCH, Memory: 16GB, Disk: 1024GB Micron_3400_MTFDKBA1T0TFH, Graphics: MSI Intel ADL GT2 14GB (1450MHz), Audio: Realtek ALC274, Network: Intel Alder Lake-P PCH CNVi WiFi OS: Ubuntu 22.10, Kernel: 5.15.0-27-generic (x86_64), Desktop: GNOME Shell, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 22.1.7, Vulkan: 1.3.211, Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 1920x1080 AOM AV1 3.5 Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 4K Frames Per Second > Higher Is Better A . 0.14 |===================================================================== B . 0.14 |===================================================================== C . 0.14 |===================================================================== AOM AV1 3.5 Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 4K Frames Per Second > Higher Is Better A . 4.28 |===================================================================== B . 4.29 |===================================================================== C . 4.29 |===================================================================== AOM AV1 3.5 Encoder Mode: Speed 6 Realtime - Input: Bosphorus 4K Frames Per Second > Higher Is Better A . 25.05 |==================================================================== B . 24.94 |==================================================================== C . 24.62 |=================================================================== AOM AV1 3.5 Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 4K Frames Per Second > Higher Is Better A . 7.98 |===================================================================== B . 7.97 |===================================================================== C . 7.99 |===================================================================== AOM AV1 3.5 Encoder Mode: Speed 8 Realtime - Input: Bosphorus 4K Frames Per Second > Higher Is Better A . 41.75 |==================================================================== B . 41.71 |==================================================================== C . 41.60 |==================================================================== AOM AV1 3.5 Encoder Mode: Speed 9 Realtime - Input: Bosphorus 4K Frames Per Second > Higher Is Better A . 62.37 |==================================================================== B . 62.54 |==================================================================== C . 61.95 |=================================================================== AOM AV1 3.5 Encoder Mode: Speed 10 Realtime - Input: Bosphorus 4K Frames Per Second > Higher Is Better A . 64.09 |==================================================================== B . 63.79 |==================================================================== C . 62.03 |================================================================== AOM AV1 3.5 Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 1080p Frames Per Second > Higher Is Better A . 0.43 |===================================================================== B . 0.43 |===================================================================== C . 0.43 |===================================================================== AOM AV1 3.5 Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 1080p Frames Per Second > Higher Is Better A . 11.27 |==================================================================== B . 11.26 |==================================================================== C . 11.27 |==================================================================== AOM AV1 3.5 Encoder Mode: Speed 6 Realtime - Input: Bosphorus 1080p Frames Per Second > Higher Is Better A . 52.90 |================================================================ B . 55.37 |=================================================================== C . 56.04 |==================================================================== AOM AV1 3.5 Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 1080p Frames Per Second > Higher Is Better A . 29.31 |==================================================================== B . 29.38 |==================================================================== C . 29.41 |==================================================================== AOM AV1 3.5 Encoder Mode: Speed 8 Realtime - Input: Bosphorus 1080p Frames Per Second > Higher Is Better A . 94.58 |================================================================== B . 97.52 |==================================================================== C . 95.40 |=================================================================== AOM AV1 3.5 Encoder Mode: Speed 9 Realtime - Input: Bosphorus 1080p Frames Per Second > Higher Is Better A . 117.33 |================================================================= B . 121.41 |=================================================================== C . 118.15 |================================================================= AOM AV1 3.5 Encoder Mode: Speed 10 Realtime - Input: Bosphorus 1080p Frames Per Second > Higher Is Better A . 125.48 |=================================================================== B . 125.17 |=================================================================== C . 124.41 |================================================================== Y-Cruncher 0.7.10.9513 Pi Digits To Calculate: 1B Seconds < Lower Is Better A . 67.68 |==================================================================== B . 67.10 |=================================================================== C . 67.42 |==================================================================== Y-Cruncher 0.7.10.9513 Pi Digits To Calculate: 500M Seconds < Lower Is Better A . 30.15 |==================================================================== B . 30.02 |==================================================================== C . 30.09 |==================================================================== oneDNN 2.7 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 5.28144 |================================================================== B . 5.01446 |=============================================================== C . 5.02586 |=============================================================== oneDNN 2.7 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 5.21808 |================================================================== B . 5.22143 |================================================================== C . 5.23973 |================================================================== oneDNN 2.7 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 1.79199 |================================================================= B . 1.78788 |================================================================= C . 1.82388 |================================================================== oneDNN 2.7 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 1.52868 |================================================================== B . 1.51192 |================================================================= C . 1.51142 |================================================================= oneDNN 2.7 Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.7 Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.7 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 8.21618 |================================================================== B . 8.27076 |================================================================== C . 8.23214 |================================================================== oneDNN 2.7 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 11.49 |==================================================================== B . 11.48 |==================================================================== C . 11.46 |==================================================================== oneDNN 2.7 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 9.57951 |================================================================== B . 9.34973 |================================================================ C . 9.36376 |================================================================= oneDNN 2.7 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 8.75402 |================================================================== B . 8.80184 |================================================================== C . 8.79739 |================================================================== oneDNN 2.7 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 2.45659 |================================================================== B . 2.41655 |================================================================= C . 2.43213 |================================================================= oneDNN 2.7 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 3.90174 |================================================================= B . 3.79839 |================================================================ C . 3.94028 |================================================================== oneDNN 2.7 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 8158.33 |================================================================== B . 8137.02 |================================================================== C . 8167.11 |================================================================== oneDNN 2.7 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 4209.35 |================================================================== B . 4216.74 |================================================================== C . 4212.55 |================================================================== oneDNN 2.7 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 8166.74 |================================================================== B . 8175.17 |================================================================== C . 8175.11 |================================================================== oneDNN 2.7 Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.7 Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.7 Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.7 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 4188.56 |================================================================== B . 4201.12 |================================================================== C . 4190.20 |================================================================== oneDNN 2.7 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 2.22134 |================================================================== B . 2.20960 |================================================================== C . 2.14263 |================================================================ oneDNN 2.7 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better A . 8169.22 |================================================================== B . 8164.49 |================================================================== C . 8166.46 |================================================================== oneDNN 2.7 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better A . 4219.47 |================================================================== B . 4192.76 |================================================================== C . 4205.35 |================================================================== oneDNN 2.7 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 1.32245 |================================================================= B . 1.34589 |================================================================== C . 1.31272 |================================================================ oneDNN 2.7 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better