Core i5 9400F Pre Xmas Intel Core i5-9400F testing with a MSI B360M GAMING PLUS (MS-7B19) v1.0 (1.10 BIOS) and MSI NVIDIA NV106 1GB on Ubuntu 20.04 via the Phoronix Test Suite. 1: Processor: Intel Core i5-9400F @ 4.10GHz (6 Cores), Motherboard: MSI B360M GAMING PLUS (MS-7B19) v1.0 (1.10 BIOS), Chipset: Intel Cannon Lake PCH, Memory: 16GB, Disk: 256GB SAMSUNG MZVPW256HEGL-000H7, Graphics: MSI NVIDIA NV106 1GB, Audio: Realtek ALC887-VD, Monitor: G237HL, Network: Intel I219-V OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc7daily20200928-generic (x86_64) 20200927, Desktop: GNOME Shell 3.36.0, Display Server: X Server 1.20.7, Display Driver: modesetting 1.20.7, OpenGL: 4.3 Mesa 20.0.2, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 2: Processor: Intel Core i5-9400F @ 4.10GHz (6 Cores), Motherboard: MSI B360M GAMING PLUS (MS-7B19) v1.0 (1.10 BIOS), Chipset: Intel Cannon Lake PCH, Memory: 16GB, Disk: 256GB SAMSUNG MZVPW256HEGL-000H7, Graphics: MSI NVIDIA NV106 1GB, Audio: Realtek ALC887-VD, Monitor: G237HL, Network: Intel I219-V OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc7daily20200928-generic (x86_64) 20200927, Desktop: GNOME Shell 3.36.0, Display Server: X Server 1.20.7, Display Driver: modesetting 1.20.7, OpenGL: 4.3 Mesa 20.0.2, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 3: Processor: Intel Core i5-9400F @ 4.10GHz (6 Cores), Motherboard: MSI B360M GAMING PLUS (MS-7B19) v1.0 (1.10 BIOS), Chipset: Intel Cannon Lake PCH, Memory: 16GB, Disk: 256GB SAMSUNG MZVPW256HEGL-000H7, Graphics: MSI NVIDIA NV106 1GB, Audio: Realtek ALC887-VD, Monitor: G237HL, Network: Intel I219-V OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc7daily20200928-generic (x86_64) 20200927, Desktop: GNOME Shell 3.36.0, Display Server: X Server 1.20.7, Display Driver: modesetting 1.20.7, OpenGL: 4.3 Mesa 20.0.2, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 Compile Bench 0.6 Test: Compile MB/s > Higher Is Better 1 . 1763.65 |================================================================== 2 . 1680.28 |=============================================================== 3 . 1629.56 |============================================================= Compile Bench 0.6 Test: Initial Create MB/s > Higher Is Better 1 . 456.64 |================================================================== 2 . 455.91 |================================================================== 3 . 464.63 |=================================================================== Compile Bench 0.6 Test: Read Compiled Tree MB/s > Higher Is Better 1 . 2701.13 |================================================================== 2 . 2656.93 |================================================================= 3 . 2692.23 |================================================================== Timed HMMer Search 3.3.1 Pfam Database Search Seconds < Lower Is Better 1 . 117.81 |=================================================================== 2 . 117.75 |=================================================================== 3 . 117.71 |=================================================================== Timed MAFFT Alignment 7.471 Multiple Sequence Alignment - LSU RNA Seconds < Lower Is Better 1 . 11.33 |==================================================================== 2 . 11.29 |==================================================================== 3 . 11.31 |==================================================================== oneDNN 2.0 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 6.11794 |================================================================== 2 . 6.07429 |================================================================== 3 . 6.05641 |================================================================= oneDNN 2.0 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 13.01 |==================================================================== 2 . 12.97 |==================================================================== 3 . 12.79 |=================================================================== oneDNN 2.0 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3.67769 |================================================================== 2 . 3.67910 |================================================================== 3 . 3.67563 |================================================================== oneDNN 2.0 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 2.53499 |================================================================== 2 . 2.52914 |================================================================== 3 . 2.48908 |================================================================= oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 25.05 |==================================================================== 2 . 25.05 |==================================================================== 3 . 24.89 |==================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 7.20243 |================================================================== 2 . 7.21840 |================================================================== 3 . 7.18729 |================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 9.33947 |================================================================== 2 . 9.32873 |================================================================== 3 . 9.29262 |================================================================== oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 19.24 |==================================================================== 2 . 19.13 |==================================================================== 3 . 18.77 |================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 9.14533 |================================================================== 2 . 9.13444 |================================================================== 3 . 9.14041 |================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 7.80879 |================================================================== 2 . 7.77972 |================================================================== 3 . 7.77705 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 4764.32 |================================================================== 2 . 4765.69 |================================================================== 3 . 4759.10 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 2769.71 |================================================================== 2 . 2768.84 |================================================================== 3 . 2765.50 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 4774.04 |================================================================== 2 . 4770.81 |================================================================== 3 . 4783.74 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 2764.10 |================================================================== 2 . 2769.92 |================================================================== 3 . 2768.11 |================================================================== oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 4.59041 |================================================================== 2 . 4.59102 |================================================================== 3 . 4.52540 |================================================================= oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 4773.39 |================================================================== 2 . 4776.65 |================================================================== 3 . 4762.84 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 2771.07 |================================================================== 2 . 2768.56 |================================================================== 3 . 2769.08 |================================================================== oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 5.18673 |================================================================== 2 . 5.12277 |================================================================= 3 . 5.16122 |================================================================== Coremark 1.0 CoreMark Size 666 - Iterations Per Second Iterations/Sec > Higher Is Better 1 . 167535.95 |================================================================ 2 . 162320.72 |============================================================== 3 . 165907.24 |=============================================================== asmFish 2018-07-23 1024 Hash Memory, 26 Depth Nodes/second > Higher Is Better 1 . 14534332 |================================================================= 2 . 14384571 |================================================================ 3 . 14382845 |================================================================ Timed FFmpeg Compilation 4.2.2 Time To Compile Seconds < Lower Is Better 1 . 97.02 |==================================================================== 3 . 97.12 |==================================================================== SQLite Speedtest 3.30 Timed Time - Size 1,000 Seconds < Lower Is Better 1 . 69.78 |=================================================================== 3 . 70.34 |==================================================================== PHPBench 0.8.1 PHP Benchmark Suite Score > Higher Is Better 1 . 680606 |=================================================================== 3 . 682487 |===================================================================