8400 m

Intel Core i5-8400 testing with a MSI Z370M MORTAR (MS-7B54) v1.0 (1.80 BIOS) and MSI Intel UHD 630 3GB on Ubuntu 20.04 via the Phoronix Test Suite.

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March 21 2021
  1 Hour, 47 Minutes
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March 21 2021
  2 Hours, 3 Minutes
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March 21 2021
  1 Hour, 46 Minutes
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8400 m Intel Core i5-8400 testing with a MSI Z370M MORTAR (MS-7B54) v1.0 (1.80 BIOS) and MSI Intel UHD 630 3GB on Ubuntu 20.04 via the Phoronix Test Suite. 1: Processor: Intel Core i5-8400 @ 4.00GHz (6 Cores), Motherboard: MSI Z370M MORTAR (MS-7B54) v1.0 (1.80 BIOS), Chipset: Intel 8th Gen Core, Memory: 8GB, Disk: 512GB INTEL SSDPEKNW512G8, Graphics: MSI Intel UHD 630 3GB (1050MHz), Audio: Realtek ALC892, Monitor: DELL S2409W, Network: Intel I219-V OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc7daily20200929-generic (x86_64) 20200928, Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.9, OpenGL: 4.6 Mesa 20.0.8, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 2: Processor: Intel Core i5-8400 @ 4.00GHz (6 Cores), Motherboard: MSI Z370M MORTAR (MS-7B54) v1.0 (1.80 BIOS), Chipset: Intel 8th Gen Core, Memory: 8GB, Disk: 512GB INTEL SSDPEKNW512G8, Graphics: MSI Intel UHD 630 3GB (1050MHz), Audio: Realtek ALC892, Monitor: DELL S2409W, Network: Intel I219-V OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc7daily20200929-generic (x86_64) 20200928, Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.9, OpenGL: 4.6 Mesa 20.0.8, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 3: Processor: Intel Core i5-8400 @ 4.00GHz (6 Cores), Motherboard: MSI Z370M MORTAR (MS-7B54) v1.0 (1.80 BIOS), Chipset: Intel 8th Gen Core, Memory: 8GB, Disk: 512GB INTEL SSDPEKNW512G8, Graphics: MSI Intel UHD 630 3GB (1050MHz), Audio: Realtek ALC892, Monitor: DELL S2409W, Network: Intel I219-V OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc7daily20200929-generic (x86_64) 20200928, Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.9, OpenGL: 4.6 Mesa 20.0.8, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 Xcompact3d Incompact3d 2021-03-11 Input: input.i3d 129 Cells Per Direction Seconds < Lower Is Better 1 . 43.87 |==================================================================== 2 . 43.01 |=================================================================== 3 . 43.82 |==================================================================== Xcompact3d Incompact3d 2021-03-11 Input: input.i3d 193 Cells Per Direction Seconds < Lower Is Better 1 . 150.89 |=================================================================== 2 . 149.56 |================================================================== 3 . 150.50 |=================================================================== SVT-HEVC 1.5.0 Tuning: 1 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 4.31 |===================================================================== 2 . 4.32 |===================================================================== 3 . 4.28 |==================================================================== SVT-HEVC 1.5.0 Tuning: 7 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 63.17 |==================================================================== 2 . 63.20 |==================================================================== 3 . 63.33 |==================================================================== SVT-HEVC 1.5.0 Tuning: 10 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 132.86 |=================================================================== 2 . 132.44 |=================================================================== 3 . 133.08 |=================================================================== SVT-VP9 0.3 Tuning: VMAF Optimized - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 116.51 |=================================================================== 2 . 116.36 |=================================================================== 3 . 116.64 |=================================================================== SVT-VP9 0.3 Tuning: PSNR/SSIM Optimized - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 117.74 |=================================================================== 2 . 117.96 |=================================================================== 3 . 117.88 |=================================================================== SVT-VP9 0.3 Tuning: Visual Quality Optimized - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 94.87 |==================================================================== 2 . 94.91 |==================================================================== 3 . 94.55 |==================================================================== Stockfish 13 Total Time Nodes Per Second > Higher Is Better 1 . 12423645 |================================================================ 2 . 12462066 |================================================================ 3 . 12596102 |================================================================= Timed Mesa Compilation 21.0 Time To Compile Seconds < Lower Is Better 1 . 96.02 |==================================================================== 2 . 95.82 |==================================================================== 3 . 95.94 |==================================================================== Timed Node.js Compilation 15.11 Time To Compile Seconds < Lower Is Better 1 . 868.88 |=================================================================== 2 . 869.02 |=================================================================== 3 . 869.26 |=================================================================== oneDNN 2.1.2 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 5.38261 |================================================================== 2 . 5.41408 |================================================================== 3 . 5.35508 |================================================================= oneDNN 2.1.2 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 10.52 |=================================================================== 2 . 10.34 |================================================================== 3 . 10.60 |==================================================================== oneDNN 2.1.2 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3.32381 |================================================================== 2 . 3.32302 |================================================================== 3 . 3.32685 |================================================================== oneDNN 2.1.2 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 2.14099 |================================================================== 2 . 2.14355 |================================================================== 3 . 2.13409 |================================================================== oneDNN 2.1.2 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 20.53 |==================================================================== 2 . 20.52 |==================================================================== 3 . 20.53 |==================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 7.38112 |================================================================== 2 . 7.36038 |================================================================== 3 . 7.35392 |================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 9.41175 |================================================================== 2 . 9.43695 |================================================================== 3 . 9.42173 |================================================================== oneDNN 2.1.2 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 15.88 |==================================================================== 2 . 15.84 |==================================================================== 3 . 15.88 |==================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3.16664 |================================================================== 2 . 3.17003 |================================================================== 3 . 3.17132 |================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 6.34104 |================================================================== 2 . 6.34022 |================================================================== 3 . 6.36246 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 4629.05 |================================================================== 2 . 4526.88 |================================================================= 3 . 4560.44 |================================================================= oneDNN 2.1.2 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 2569.74 |================================================================== 2 . 2556.44 |================================================================== 3 . 2560.05 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 4618.45 |================================================================== 2 . 4653.17 |================================================================== 3 . 4629.21 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 2557.47 |================================================================= 2 . 2601.07 |================================================================== 3 . 2568.63 |================================================================= oneDNN 2.1.2 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 3.75357 |================================================================== 2 . 3.75446 |================================================================== 3 . 3.75387 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 4548.60 |================================================================= 2 . 4647.00 |================================================================== 3 . 4588.83 |================================================================= oneDNN 2.1.2 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 2615.31 |================================================================== 2 . 2598.32 |================================================================= 3 . 2621.26 |================================================================== oneDNN 2.1.2 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 4.19566 |================================================================== 2 . 4.18604 |================================================================== 3 . 4.18462 |================================================================== Sysbench 1.0.20 Test: RAM / Memory MiB/sec > Higher Is Better 1 . 12559.86 |================================================================= 2 . 12654.75 |================================================================= 3 . 12635.66 |================================================================= Sysbench 1.0.20 Test: CPU Events Per Second > Higher Is Better 1 . 8027.05 |================================================================== 2 . 8031.24 |================================================================== 3 . 8033.66 |==================================================================