Xeon Plat 8280 March 2 x Intel Xeon Platinum 8280 testing with a GIGABYTE MD61-SC2-00 v01000100 (T15 BIOS) and llvmpipe on Ubuntu 21.04 via the Phoronix Test Suite. 1: Processor: 2 x Intel Xeon Platinum 8280 @ 4.00GHz (56 Cores / 112 Threads), Motherboard: GIGABYTE MD61-SC2-00 v01000100 (T15 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 378GB, Disk: 280GB INTEL SSDPED1D280GA, Graphics: llvmpipe, Monitor: VE228, Network: 2 x Intel X722 for 1GbE + 2 x QLogic FastLinQ QL41000 10/25/40/50GbE OS: Ubuntu 21.04, Kernel: 5.11.0-051100rc7daily20210209-generic (x86_64) 20210208, Desktop: GNOME Shell 3.38.2, Display Server: X Server 1.20.9, OpenGL: 4.5 Mesa 20.3.4 (LLVM 11.0.1 256 bits), Vulkan: 1.0.2, Compiler: GCC 10.2.1 20210130, File-System: ext4, Screen Resolution: 1920x1080 2: Processor: 2 x Intel Xeon Platinum 8280 @ 4.00GHz (56 Cores / 112 Threads), Motherboard: GIGABYTE MD61-SC2-00 v01000100 (T15 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 378GB, Disk: 280GB INTEL SSDPED1D280GA, Graphics: llvmpipe, Monitor: VE228, Network: 2 x Intel X722 for 1GbE + 2 x QLogic FastLinQ QL41000 10/25/40/50GbE OS: Ubuntu 21.04, Kernel: 5.11.0-051100rc7daily20210209-generic (x86_64) 20210208, Desktop: GNOME Shell 3.38.2, Display Server: X Server 1.20.9, OpenGL: 4.5 Mesa 20.3.4 (LLVM 11.0.1 256 bits), Vulkan: 1.0.2, Compiler: GCC 10.2.1 20210130, File-System: ext4, Screen Resolution: 1920x1080 Xcompact3d Incompact3d 2021-03-11 Input: X3D-benchmarking input.i3d Seconds < Lower Is Better 1 . 476.53 |=================================================================== 2 . 474.51 |=================================================================== Xcompact3d Incompact3d 2021-03-11 Input: input.i3d 129 Cells Per Direction Seconds < Lower Is Better 1 . 4.54274321 |=============================================================== 2 . 4.39153099 |============================================================= Xcompact3d Incompact3d 2021-03-11 Input: input.i3d 193 Cells Per Direction Seconds < Lower Is Better 1 . 18.85 |=================================================================== 2 . 18.99 |==================================================================== simdjson 0.8.2 Throughput Test: Kostya GB/s > Higher Is Better 1 . 2.54 |===================================================================== 2 . 2.52 |==================================================================== simdjson 0.8.2 Throughput Test: LargeRandom GB/s > Higher Is Better 1 . 0.83 |===================================================================== 2 . 0.83 |===================================================================== simdjson 0.8.2 Throughput Test: PartialTweets GB/s > Higher Is Better 1 . 3.48 |===================================================================== 2 . 3.46 |===================================================================== simdjson 0.8.2 Throughput Test: DistinctUserID GB/s > Higher Is Better 1 . 3.6 |====================================================================== 2 . 3.6 |====================================================================== JPEG XL 0.3.3 Input: PNG - Encode Speed: 5 MP/s > Higher Is Better 1 . 55.55 |==================================================================== 2 . 53.80 |================================================================== JPEG XL 0.3.3 Input: PNG - Encode Speed: 7 MP/s > Higher Is Better 1 . 7.34 |===================================================================== 2 . 7.33 |===================================================================== JPEG XL 0.3.3 Input: PNG - Encode Speed: 8 MP/s > Higher Is Better 1 . 0.62 |===================================================================== 2 . 0.62 |===================================================================== JPEG XL 0.3.3 Input: JPEG - Encode Speed: 5 MP/s > Higher Is Better 1 . 45.08 |==================================================================== 2 . 45.11 |==================================================================== JPEG XL 0.3.3 Input: JPEG - Encode Speed: 7 MP/s > Higher Is Better 1 . 45.45 |==================================================================== 2 . 45.03 |=================================================================== JPEG XL 0.3.3 Input: JPEG - Encode Speed: 8 MP/s > Higher Is Better 1 . 21.37 |=================================================================== 2 . 21.70 |==================================================================== JPEG XL Decoding 0.3.3 CPU Threads: 1 MP/s > Higher Is Better 1 . 28.37 |=================================================================== 2 . 28.65 |==================================================================== JPEG XL Decoding 0.3.3 CPU Threads: All MP/s > Higher Is Better 1 . 131.58 |=================================================================== 2 . 129.64 |================================================================== AOM AV1 3.0 Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 4K Frames Per Second > Higher Is Better 1 . 0.16 |===================================================================== 2 . 0.16 |===================================================================== AOM AV1 3.0 Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 4K Frames Per Second > Higher Is Better 1 . 2.99 |===================================================================== 2 . 2.91 |=================================================================== AOM AV1 3.0 Encoder Mode: Speed 6 Realtime - Input: Bosphorus 4K Frames Per Second > Higher Is Better 1 . 8.07 |===================================================================== 2 . 7.88 |=================================================================== AOM AV1 3.0 Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 4K Frames Per Second > Higher Is Better 1 . 4.86 |==================================================================== 2 . 4.90 |===================================================================== AOM AV1 3.0 Encoder Mode: Speed 8 Realtime - Input: Bosphorus 4K Frames Per Second > Higher Is Better 1 . 14.28 |=================================================================== 2 . 14.44 |==================================================================== AOM AV1 3.0 Encoder Mode: Speed 9 Realtime - Input: Bosphorus 4K Frames Per Second > Higher Is Better 1 . 19.63 |==================================================================== 2 . 19.03 |================================================================== AOM AV1 3.0 Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 0.41 |=================================================================== 2 . 0.42 |===================================================================== AOM AV1 3.0 Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 4.64 |===================================================================== 2 . 4.61 |===================================================================== AOM AV1 3.0 Encoder Mode: Speed 6 Realtime - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 12.29 |================================================================ 2 . 12.97 |==================================================================== AOM AV1 3.0 Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 10.16 |==================================================================== 2 . 9.67 |================================================================= AOM AV1 3.0 Encoder Mode: Speed 8 Realtime - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 42.22 |==================================================================== 2 . 42.18 |==================================================================== AOM AV1 3.0 Encoder Mode: Speed 9 Realtime - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 55.86 |==================================================================== 2 . 51.85 |=============================================================== SVT-HEVC 1.5.0 Tuning: 1 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 28.42 |=================================================================== 2 . 28.69 |==================================================================== SVT-HEVC 1.5.0 Tuning: 7 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 221.73 |================================================================== 2 . 223.88 |=================================================================== SVT-HEVC 1.5.0 Tuning: 10 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 338.98 |=================================================================== 2 . 327.69 |================================================================= SVT-VP9 0.3 Tuning: VMAF Optimized - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 267.01 |=================================================================== 2 . 265.39 |=================================================================== SVT-VP9 0.3 Tuning: PSNR/SSIM Optimized - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 258.96 |================================================================ 2 . 270.70 |=================================================================== SVT-VP9 0.3 Tuning: Visual Quality Optimized - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 211.07 |================================================================ 2 . 220.95 |=================================================================== Stockfish 13 Total Time Nodes Per Second > Higher Is Better 1 . 126833009 |=============================================================== 2 . 128023879 |================================================================ Timed Node.js Compilation 15.11 Time To Compile Seconds < Lower Is Better 1 . 122.65 |=================================================================== 2 . 123.55 |=================================================================== oneDNN 2.1.2 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 1.47881 |================================================================== 2 . 1.45298 |================================================================= oneDNN 2.1.2 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 3.37073 |================================================================== 2 . 3.26772 |================================================================ oneDNN 2.1.2 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 1.55537 |================================================================== 2 . 1.55501 |================================================================== oneDNN 2.1.2 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 0.985454 |========================================================= 2 . 1.125920 |================================================================= oneDNN 2.1.2 Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 3.90428 |================================================================== 2 . 3.90592 |================================================================== oneDNN 2.1.2 Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 2.40558 |================================================== 2 . 3.17529 |================================================================== oneDNN 2.1.2 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 4.60206 |================================================================== 2 . 4.39572 |=============================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 18.68 |==================================================================== 2 . 17.57 |================================================================ oneDNN 2.1.2 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 1.29331 |================================================================== 2 . 1.25134 |================================================================ oneDNN 2.1.2 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3.93935 |============================================================== 2 . 4.18477 |================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 0.416015 |================================================================= 2 . 0.415621 |================================================================= oneDNN 2.1.2 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 0.366415 |======================================================= 2 . 0.436156 |================================================================= oneDNN 2.1.2 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 1020.53 |============================================================= 2 . 1110.42 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 565.31 |=================================================================== 2 . 568.68 |=================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 1172.30 |================================================================== 2 . 1103.13 |============================================================== oneDNN 2.1.2 Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 3.38749 |================================================================= 2 . 3.42681 |================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 4.66824 |================================================================== 2 . 4.62775 |================================================================= oneDNN 2.1.2 Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 4.50971 |================================================================== 2 . 4.53078 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 562.81 |================================================================= 2 . 575.80 |=================================================================== oneDNN 2.1.2 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 0.378284 |================================================================= 2 . 0.361022 |============================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 1092.64 |================================================================== 2 . 1100.32 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 581.88 |=================================================================== 2 . 571.13 |================================================================== oneDNN 2.1.2 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 0.403840 |================================================================= 2 . 0.380466 |============================================================= oneDNN 2.1.2 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 0.893551 |================================================================= 2 . 0.891122 |================================================================= Liquid-DSP 2021.01.31 Threads: 1 - Buffer Length: 256 - Filter Length: 57 samples/s > Higher Is Better 1 . 51265000 |================================================================= 2 . 50187000 |================================================================ Liquid-DSP 2021.01.31 Threads: 2 - Buffer Length: 256 - Filter Length: 57 samples/s > Higher Is Better 1 . 95314000 |================================================================= 2 . 91223000 |============================================================== Liquid-DSP 2021.01.31 Threads: 4 - Buffer Length: 256 - Filter Length: 57 samples/s > Higher Is Better 1 . 178690000 |================================================================ 2 . 178830000 |================================================================ Liquid-DSP 2021.01.31 Threads: 8 - Buffer Length: 256 - Filter Length: 57 samples/s > Higher Is Better 1 . 350750000 |=============================================================== 2 . 354370000 |================================================================ Liquid-DSP 2021.01.31 Threads: 16 - Buffer Length: 256 - Filter Length: 57 samples/s > Higher Is Better 1 . 691900000 |=============================================================== 2 . 699050000 |================================================================ Liquid-DSP 2021.01.31 Threads: 32 - Buffer Length: 256 - Filter Length: 57 samples/s > Higher Is Better 1 . 1332900000 |=============================================================== 2 . 1316100000 |============================================================== Liquid-DSP 2021.01.31 Threads: 64 - Buffer Length: 256 - Filter Length: 57 samples/s > Higher Is Better 1 . 2133000000 |=============================================================== 2 . 2133000000 |=============================================================== Liquid-DSP 2021.01.31 Threads: 112 - Buffer Length: 256 - Filter Length: 57 samples/s > Higher Is Better 1 . 2370800000 |=============================================================== 2 . 2350300000 |============================================================== ASTC Encoder 2.4 Preset: Medium Seconds < Lower Is Better 1 . 5.8877 |=================================================================== 2 . 5.8448 |=================================================================== ASTC Encoder 2.4 Preset: Thorough Seconds < Lower Is Better 1 . 9.6515 |================================================================== 2 . 9.8305 |=================================================================== ASTC Encoder 2.4 Preset: Exhaustive Seconds < Lower Is Better 1 . 24.62 |==================================================================== 2 . 24.66 |==================================================================== Basis Universal 1.13 Settings: ETC1S Seconds < Lower Is Better 1 . 29.15 |==================================================================== 2 . 29.21 |==================================================================== Basis Universal 1.13 Settings: UASTC Level 0 Seconds < Lower Is Better 1 . 8.444 |==================================================================== 2 . 8.211 |================================================================== Basis Universal 1.13 Settings: UASTC Level 2 Seconds < Lower Is Better 1 . 13.74 |==================================================================== 2 . 13.72 |==================================================================== Basis Universal 1.13 Settings: UASTC Level 3 Seconds < Lower Is Better 1 . 19.85 |==================================================================== 2 . 19.96 |==================================================================== OpenSCAD Render: Pistol Seconds < Lower Is Better 1 . 120.94 |=================================================================== 2 . 118.16 |================================================================= OpenSCAD Render: Retro Car Seconds < Lower Is Better 1 . 5.705 |==================================================================== 2 . 5.587 |=================================================================== OpenSCAD Render: Mini-ITX Case Seconds < Lower Is Better 1 . 54.43 |==================================================================== 2 . 53.44 |=================================================================== OpenSCAD Render: Projector Mount Swivel Seconds < Lower Is Better 1 . 10.81 |==================================================================== 2 . 10.77 |==================================================================== OpenSCAD Render: Leonardo Phone Case Slim Seconds < Lower Is Better 1 . 21.77 |==================================================================== 2 . 21.51 |=================================================================== Mobile Neural Network 1.1.3 Model: SqueezeNetV1.0 ms < Lower Is Better 1 . 8.318 |==================================================================== 2 . 8.008 |================================================================= Mobile Neural Network 1.1.3 Model: resnet-v2-50 ms < Lower Is Better 1 . 27.93 |==================================================================== 2 . 27.61 |=================================================================== Mobile Neural Network 1.1.3 Model: MobileNetV2_224 ms < Lower Is Better 1 . 5.154 |==================================================================== 2 . 4.388 |========================================================== Mobile Neural Network 1.1.3 Model: mobilenet-v1-1.0 ms < Lower Is Better 1 . 3.291 |==================================================================== 2 . 3.222 |=================================================================== Mobile Neural Network 1.1.3 Model: inception-v3 ms < Lower Is Better 1 . 33.92 |==================================================================== 2 . 33.27 |=================================================================== Sysbench 1.0.20 Test: RAM / Memory MiB/sec > Higher Is Better 1 . 11036.69 |========================================================== 2 . 12276.48 |================================================================= Sysbench 1.0.20 Test: CPU Events Per Second > Higher Is Better 1 . 97455.53 |================================================================= 2 . 97505.52 |=================================================================