3300X oneDNN SVT Stuff

AMD Ryzen 3 3300X 4-Core testing with a MSI B350M GAMING PRO (MS-7A39) v1.0 (2.NR BIOS) and AMD FirePro V3800 512MB on Ubuntu 20.04 via the Phoronix Test Suite.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2103158-HA-3300XONED31
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March 15 2021
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3300X oneDNN SVT Stuff AMD Ryzen 3 3300X 4-Core testing with a MSI B350M GAMING PRO (MS-7A39) v1.0 (2.NR BIOS) and AMD FirePro V3800 512MB on Ubuntu 20.04 via the Phoronix Test Suite. 1: Processor: AMD Ryzen 3 3300X 4-Core @ 3.80GHz (4 Cores / 8 Threads), Motherboard: MSI B350M GAMING PRO (MS-7A39) v1.0 (2.NR BIOS), Chipset: AMD Starship/Matisse, Memory: 8GB, Disk: 256GB INTEL SSDPEKKW256G7, Graphics: AMD FirePro V3800 512MB, Audio: AMD Redwood HDMI Audio, Monitor: DELL S2409W, Network: Realtek RTL8111/8168/8411 OS: Ubuntu 20.04, Kernel: 5.9.0-rc5-14sep-patch (x86_64) 20200914, Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.9, OpenGL: 3.3 Mesa 20.0.8 (LLVM 10.0.0), Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 2: Processor: AMD Ryzen 3 3300X 4-Core @ 3.80GHz (4 Cores / 8 Threads), Motherboard: MSI B350M GAMING PRO (MS-7A39) v1.0 (2.NR BIOS), Chipset: AMD Starship/Matisse, Memory: 8GB, Disk: 256GB INTEL SSDPEKKW256G7, Graphics: AMD FirePro V3800 512MB, Audio: AMD Redwood HDMI Audio, Monitor: DELL S2409W, Network: Realtek RTL8111/8168/8411 OS: Ubuntu 20.04, Kernel: 5.9.0-rc5-14sep-patch (x86_64) 20200914, Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.9, OpenGL: 3.3 Mesa 20.0.8 (LLVM 10.0.0), Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 3: Processor: AMD Ryzen 3 3300X 4-Core @ 3.80GHz (4 Cores / 8 Threads), Motherboard: MSI B350M GAMING PRO (MS-7A39) v1.0 (2.NR BIOS), Chipset: AMD Starship/Matisse, Memory: 8GB, Disk: 256GB INTEL SSDPEKKW256G7, Graphics: AMD FirePro V3800 512MB, Audio: AMD Redwood HDMI Audio, Monitor: DELL S2409W, Network: Realtek RTL8111/8168/8411 OS: Ubuntu 20.04, Kernel: 5.9.0-rc5-14sep-patch (x86_64) 20200914, Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.9, OpenGL: 3.3 Mesa 20.0.8 (LLVM 10.0.0), Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 Sysbench 1.0.20 Test: CPU Events Per Second > Higher Is Better 1 . 8918.82 |================================================================== 2 . 8918.55 |================================================================== 3 . 8921.34 |================================================================== SVT-HEVC 1.5.0 Tuning: 1 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 4.03 |===================================================================== 2 . 4.04 |===================================================================== 3 . 4.04 |===================================================================== SVT-HEVC 1.5.0 Tuning: 7 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 62.02 |==================================================================== 2 . 61.91 |==================================================================== 3 . 61.92 |==================================================================== SVT-HEVC 1.5.0 Tuning: 10 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 133.19 |=================================================================== 2 . 133.04 |=================================================================== 3 . 133.20 |=================================================================== SVT-VP9 0.3 Tuning: VMAF Optimized - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 108.00 |================================================================== 2 . 109.62 |=================================================================== 3 . 109.10 |=================================================================== SVT-VP9 0.3 Tuning: PSNR/SSIM Optimized - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 110.67 |=================================================================== 2 . 110.57 |=================================================================== 3 . 111.25 |=================================================================== SVT-VP9 0.3 Tuning: Visual Quality Optimized - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 87.76 |==================================================================== 2 . 88.03 |==================================================================== 3 . 87.67 |==================================================================== Sysbench 1.0.20 Test: RAM / Memory MiB/sec > Higher Is Better 1 . 16393.95 |================================================================= 2 . 16345.46 |================================================================= 3 . 16159.08 |================================================================ oneDNN 2.1.2 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 6.78051 |================================================================== 2 . 6.75096 |================================================================== 3 . 6.78600 |================================================================== oneDNN 2.1.2 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 10.40 |==================================================================== 2 . 10.47 |==================================================================== 3 . 10.24 |=================================================================== oneDNN 2.1.2 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 4.91753 |================================================================== 2 . 4.90848 |================================================================== 3 . 4.88115 |================================================================== oneDNN 2.1.2 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 2.73839 |================================================================== 2 . 2.71709 |================================================================= 3 . 2.68835 |================================================================= oneDNN 2.1.2 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 21.73 |==================================================================== 2 . 21.88 |==================================================================== 3 . 21.75 |==================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 11.04 |========================================================= 2 . 13.01 |==================================================================== 3 . 13.09 |==================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 11.33 |==================================================================== 2 . 11.35 |==================================================================== 3 . 11.30 |==================================================================== oneDNN 2.1.2 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 22.66 |==================================================================== 2 . 22.53 |==================================================================== 3 . 22.62 |==================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 6.79725 |================================================================== 2 . 6.78850 |================================================================== 3 . 6.77109 |================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 9.15795 |================================================================== 2 . 9.17290 |================================================================== 3 . 9.15620 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 5993.82 |================================================================== 2 . 5983.99 |================================================================== 3 . 5981.79 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 3109.22 |================================================================== 2 . 3118.20 |================================================================== 3 . 3119.40 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 6032.29 |================================================================== 2 . 6025.13 |================================================================== 3 . 6008.78 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3101.94 |================================================================== 2 . 3107.96 |================================================================== 3 . 3113.52 |================================================================== oneDNN 2.1.2 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 5.02827 |================================================================== 2 . 5.03340 |================================================================== 3 . 5.01653 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 6030.31 |================================================================== 2 . 6010.23 |================================================================== 3 . 6015.97 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 3113.93 |================================================================== 2 . 3110.22 |================================================================== 3 . 3102.45 |================================================================== oneDNN 2.1.2 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 6.09889 |================================================================== 2 . 6.11621 |================================================================== 3 . 6.12141 |==================================================================