3700X More march AMD Ryzen 7 3700X 8-Core testing with a Gigabyte A320M-S2H-CF (F52a BIOS) and HIS AMD Radeon HD 7750/8740 / R7 250E 1GB on Ubuntu 20.04 via the Phoronix Test Suite. 1: Processor: AMD Ryzen 7 3700X 8-Core @ 3.60GHz (8 Cores / 16 Threads), Motherboard: Gigabyte A320M-S2H-CF (F52a BIOS), Chipset: AMD Starship/Matisse, Memory: 8GB, Disk: 240GB TOSHIBA RC100, Graphics: HIS AMD Radeon HD 7750/8740 / R7 250E 1GB, Audio: AMD Oland/Hainan/Cape, Monitor: DELL S2409W, Network: Realtek RTL8111/8168/8411 OS: Ubuntu 20.04, Kernel: 5.8.1-050801-generic (x86_64), Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.9, OpenGL: 4.5 Mesa 20.0.8 (LLVM 10.0.0), Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 2: Processor: AMD Ryzen 7 3700X 8-Core @ 3.60GHz (8 Cores / 16 Threads), Motherboard: Gigabyte A320M-S2H-CF (F52a BIOS), Chipset: AMD Starship/Matisse, Memory: 8GB, Disk: 240GB TOSHIBA RC100, Graphics: HIS AMD Radeon HD 7750/8740 / R7 250E 1GB, Audio: AMD Oland/Hainan/Cape, Monitor: DELL S2409W, Network: Realtek RTL8111/8168/8411 OS: Ubuntu 20.04, Kernel: 5.8.1-050801-generic (x86_64), Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.9, OpenGL: 4.5 Mesa 20.0.8 (LLVM 10.0.0), Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 3: Processor: AMD Ryzen 7 3700X 8-Core @ 3.60GHz (8 Cores / 16 Threads), Motherboard: Gigabyte A320M-S2H-CF (F52a BIOS), Chipset: AMD Starship/Matisse, Memory: 8GB, Disk: 240GB TOSHIBA RC100, Graphics: HIS AMD Radeon HD 7750/8740 / R7 250E 1GB, Audio: AMD Oland/Hainan/Cape, Monitor: DELL S2409W, Network: Realtek RTL8111/8168/8411 OS: Ubuntu 20.04, Kernel: 5.8.1-050801-generic (x86_64), Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.9, OpenGL: 4.5 Mesa 20.0.8 (LLVM 10.0.0), Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 oneDNN 2.1.2 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 5.56715 |================================================================== 2 . 5.53204 |================================================================== 3 . 5.53438 |================================================================== oneDNN 2.1.2 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 10.23 |================================================================ 2 . 10.74 |=================================================================== 3 . 10.95 |==================================================================== oneDNN 2.1.2 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 2.63018 |=============== 2 . 2.62524 |=============== 3 . 11.59989 |================================================================= oneDNN 2.1.2 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 2.38623 |=========================================================== 2 . 2.68827 |================================================================== 3 . 2.46096 |============================================================ oneDNN 2.1.2 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 23.04 |==================================================================== 2 . 23.09 |==================================================================== 3 . 22.95 |==================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 8.99245 |================================================================== 2 . 8.70508 |================================================================ 3 . 8.71411 |================================================================ oneDNN 2.1.2 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 6.69724 |================================================================= 2 . 6.74862 |================================================================== 3 . 6.73558 |================================================================== oneDNN 2.1.2 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 21.06 |=================================================================== 2 . 21.52 |==================================================================== 3 . 21.24 |=================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3.63976 |================================================================== 2 . 3.64478 |================================================================== 3 . 3.64264 |================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 4.62287 |================================================================== 2 . 4.64339 |================================================================== 3 . 4.63664 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 3899.22 |================================================================= 2 . 3936.82 |================================================================== 3 . 3811.13 |================================================================ oneDNN 2.1.2 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 2758.75 |================================================================ 2 . 2830.69 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3873.42 |================================================================= 2 . 3938.80 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 2746.85 |================================================================ 2 . 2819.51 |================================================================== oneDNN 2.1.2 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 4.82779 |================================================================ 2 . 4.94477 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 3858.11 |================================================================= 2 . 3919.20 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 2743.14 |================================================================= 2 . 2801.03 |================================================================== oneDNN 2.1.2 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3.05660 |================================================================== 2 . 3.06767 |================================================================== SVT-HEVC 1.5.0 Tuning: 1 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 7.63 |===================================================================== 2 . 7.59 |===================================================================== 3 . 7.61 |===================================================================== SVT-HEVC 1.5.0 Tuning: 7 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 106.19 |=================================================================== 2 . 105.83 |=================================================================== 3 . 105.92 |=================================================================== SVT-HEVC 1.5.0 Tuning: 10 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 209.25 |=================================================================== 2 . 209.21 |=================================================================== 3 . 209.45 |=================================================================== SVT-VP9 0.3 Tuning: VMAF Optimized - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 136.60 |=================================================================== 2 . 134.18 |================================================================== 3 . 135.52 |================================================================== SVT-VP9 0.3 Tuning: PSNR/SSIM Optimized - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 143.24 |=================================================================== 2 . 143.51 |=================================================================== 3 . 143.34 |=================================================================== SVT-VP9 0.3 Tuning: Visual Quality Optimized - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 114.39 |=================================================================== 2 . 114.45 |=================================================================== 3 . 114.17 |=================================================================== Sysbench 1.0.20 Test: RAM / Memory MiB/sec > Higher Is Better 1 . 10289.34 |================================================================= 2 . 10276.90 |================================================================= Sysbench 1.0.20 Test: CPU Events Per Second > Higher Is Better 1 . 17383.33 |================================================================= 2 . 17341.94 |================================================================= Timed Mesa Compilation 21.0 Time To Compile Seconds < Lower Is Better 1 . 55.88 |==================================================================== 2 . 55.56 |==================================================================== 3 . 55.76 |==================================================================== Xcompact3d Incompact3d 2021-03-11 Input: input.i3d 129 Cells Per Direction Seconds < Lower Is Better 1 . 40.35 |=================================================================== 2 . 40.48 |==================================================================== 3 . 40.72 |==================================================================== Xcompact3d Incompact3d 2021-03-11 Input: input.i3d 192 Cells Per Direction Seconds < Lower Is Better 1 . 318.21 |=================================================================== 2 . 316.53 |=================================================================== 3 . 317.01 |===================================================================