5220R 2P Ubuntu EO 2020 2 x Intel Xeon Gold 5220R testing with a TYAN S7106 (V2.01.B40 BIOS) and llvmpipe on Ubuntu 20.04 via the Phoronix Test Suite. 1: Processor: 2 x Intel Xeon Gold 5220R @ 3.90GHz (36 Cores / 72 Threads), Motherboard: TYAN S7106 (V2.01.B40 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 94GB, Disk: 500GB Samsung SSD 860, Graphics: llvmpipe, Monitor: VE228, Network: 2 x Intel I210 + 2 x QLogic cLOM8214 1/10GbE OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc6-generic (x86_64) 20200920, Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 3.3 Mesa 20.0.4 (LLVM 9.0.1 256 bits), Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 2: Processor: 2 x Intel Xeon Gold 5220R @ 3.90GHz (36 Cores / 72 Threads), Motherboard: TYAN S7106 (V2.01.B40 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 94GB, Disk: 500GB Samsung SSD 860, Graphics: llvmpipe, Monitor: VE228, Network: 2 x Intel I210 + 2 x QLogic cLOM8214 1/10GbE OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc6-generic (x86_64) 20200920, Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 3.3 Mesa 20.0.4 (LLVM 9.0.1 256 bits), Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 3: Processor: 2 x Intel Xeon Gold 5220R @ 3.90GHz (36 Cores / 72 Threads), Motherboard: TYAN S7106 (V2.01.B40 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 94GB, Disk: 500GB Samsung SSD 860, Graphics: llvmpipe, Monitor: VE228, Network: 2 x Intel I210 + 2 x QLogic cLOM8214 1/10GbE OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc6-generic (x86_64) 20200920, Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 3.3 Mesa 20.0.4 (LLVM 9.0.1 256 bits), Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 CLOMP 1.2 Static OMP Speedup Speedup > Higher Is Better 1 . 32.0 |===================================================================== 2 . 31.8 |===================================================================== 3 . 31.5 |==================================================================== Timed HMMer Search 3.3.1 Pfam Database Search Seconds < Lower Is Better 1 . 224.33 |=================================================================== 2 . 223.76 |=================================================================== 3 . 225.09 |=================================================================== Timed MAFFT Alignment 7.471 Multiple Sequence Alignment - LSU RNA Seconds < Lower Is Better 1 . 11.84 |==================================================================== 2 . 11.73 |=================================================================== 3 . 11.86 |==================================================================== simdjson 0.7.1 Throughput Test: Kostya GB/s > Higher Is Better 1 . 0.56 |===================================================================== 2 . 0.56 |===================================================================== 3 . 0.56 |===================================================================== simdjson 0.7.1 Throughput Test: LargeRandom GB/s > Higher Is Better 1 . 0.39 |===================================================================== 2 . 0.39 |===================================================================== 3 . 0.39 |===================================================================== simdjson 0.7.1 Throughput Test: PartialTweets GB/s > Higher Is Better 1 . 0.56 |==================================================================== 2 . 0.57 |===================================================================== 3 . 0.56 |==================================================================== simdjson 0.7.1 Throughput Test: DistinctUserID GB/s > Higher Is Better 1 . 0.58 |===================================================================== 2 . 0.58 |===================================================================== 3 . 0.58 |===================================================================== oneDNN 2.0 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 1.81362 |================================================================== 2 . 1.81094 |================================================================== 3 . 1.81333 |================================================================== oneDNN 2.0 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 3.92479 |================================================================== 2 . 3.85328 |================================================================= 3 . 3.89296 |================================================================= oneDNN 2.0 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 1.34503 |================================================================= 2 . 1.36278 |================================================================== 3 . 1.34385 |================================================================= oneDNN 2.0 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 1.25537 |================================================================= 2 . 1.27284 |================================================================== 3 . 1.24818 |================================================================= oneDNN 2.0 Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 5.69233 |================================================================== 2 . 5.68773 |================================================================== 3 . 5.68500 |================================================================== oneDNN 2.0 Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 14.35869 |================================================================= 2 . 3.69864 |================= 3 . 3.66837 |================= oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 7.47037 |================================================================== 2 . 7.47063 |================================================================== 3 . 7.47617 |================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 2.28487 |================================================================== 2 . 2.28141 |================================================================== 3 . 2.27524 |================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 2.73594 |================================================================== 2 . 2.73944 |================================================================== 3 . 2.73941 |================================================================== oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 7.05918 |================================================================== 2 . 7.06278 |================================================================== 3 . 6.97601 |================================================================= oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 0.562519 |================================================================= 2 . 0.562248 |================================================================= 3 . 0.561408 |================================================================= oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 0.694878 |================================================================= 2 . 0.695254 |================================================================= 3 . 0.694724 |================================================================= oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 1443.59 |================================================= 2 . 1951.91 |================================================================== 3 . 1434.19 |================================================ oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 819.46 |================================================================== 2 . 828.06 |=================================================================== 3 . 822.74 |=================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 1457.16 |================================================================== 2 . 1430.86 |================================================================= 3 . 1446.43 |================================================================== oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 6.41138 |================================================================== 2 . 6.41636 |================================================================== 3 . 6.42989 |================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 7.65860 |================================================================== 2 . 7.66616 |================================================================== 3 . 7.65364 |================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 9.50819 |================================================================== 2 . 9.53901 |================================================================== 3 . 9.53508 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 818.43 |================================================================== 2 . 826.79 |=================================================================== 3 . 821.64 |=================================================================== oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 0.552851 |================================================================ 2 . 0.553813 |================================================================= 3 . 0.557242 |================================================================= oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 1941.01 |================================================================== 2 . 1431.41 |================================================= 3 . 1458.06 |================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 818.41 |================================================================== 2 . 817.92 |================================================================== 3 . 832.76 |=================================================================== oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 0.348159 |================================================================= 2 . 0.341476 |================================================================ 3 . 0.347151 |================================================================= oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 1.46645 |================================================================== 2 . 1.45854 |================================================================== 3 . 1.45596 |================================================================== Coremark 1.0 CoreMark Size 666 - Iterations Per Second Iterations/Sec > Higher Is Better 1 . 1096383.89 |=============================================================== 2 . 1088921.70 |=============================================================== 3 . 1093129.51 |=============================================================== Timed Clash Compilation Time To Compile Seconds < Lower Is Better 1 . 482.03 |=================================================================== 2 . 485.10 |=================================================================== 3 . 484.60 |=================================================================== Timed FFmpeg Compilation 4.2.2 Time To Compile Seconds < Lower Is Better 1 . 30.59 |==================================================================== 2 . 30.60 |==================================================================== 3 . 30.51 |==================================================================== Build2 0.13 Time To Compile Seconds < Lower Is Better 1 . 79.00 |==================================================================== 2 . 78.86 |==================================================================== 3 . 78.80 |==================================================================== Timed Eigen Compilation 3.3.9 Time To Compile Seconds < Lower Is Better 1 . 85.76 |==================================================================== 2 . 86.02 |==================================================================== 3 . 85.78 |==================================================================== Monkey Audio Encoding 3.99.6 WAV To APE Seconds < Lower Is Better 1 . 13.08 |==================================================================== 2 . 13.01 |=================================================================== 3 . 13.11 |==================================================================== Ogg Audio Encoding 1.3.4 WAV To Ogg Seconds < Lower Is Better 1 . 23.22 |==================================================================== 2 . 23.20 |==================================================================== 3 . 23.17 |==================================================================== Opus Codec Encoding 1.3.1 WAV To Opus Encode Seconds < Lower Is Better 1 . 10.18 |==================================================================== 2 . 10.18 |==================================================================== 3 . 10.20 |==================================================================== Node.js V8 Web Tooling Benchmark runs/s > Higher Is Better 1 . 10.25 |=================================================================== 2 . 10.28 |=================================================================== 3 . 10.37 |==================================================================== SQLite Speedtest 3.30 Timed Time - Size 1,000 Seconds < Lower Is Better 1 . 66.08 |==================================================================== 2 . 65.30 |=================================================================== 3 . 64.79 |=================================================================== NCNN 20201218 Target: CPU - Model: mobilenet ms < Lower Is Better 1 . 22.91 |=================================================================== 2 . 23.15 |==================================================================== 3 . 23.16 |==================================================================== NCNN 20201218 Target: CPU-v2-v2 - Model: mobilenet-v2 ms < Lower Is Better 1 . 10.12 |=================================================================== 2 . 10.19 |=================================================================== 3 . 10.29 |==================================================================== NCNN 20201218 Target: CPU-v3-v3 - Model: mobilenet-v3 ms < Lower Is Better 1 . 9.26 |===================================================================== 2 . 9.08 |==================================================================== 3 . 9.26 |===================================================================== NCNN 20201218 Target: CPU - Model: shufflenet-v2 ms < Lower Is Better 1 . 9.20 |===================================================================== 2 . 8.82 |================================================================== 3 . 9.08 |==================================================================== NCNN 20201218 Target: CPU - Model: mnasnet ms < Lower Is Better 1 . 9.30 |=================================================================== 2 . 9.46 |==================================================================== 3 . 9.61 |===================================================================== NCNN 20201218 Target: CPU - Model: efficientnet-b0 ms < Lower Is Better 1 . 12.14 |================================================================= 2 . 12.56 |=================================================================== 3 . 12.68 |==================================================================== NCNN 20201218 Target: CPU - Model: blazeface ms < Lower Is Better 1 . 4.80 |==================================================================== 2 . 4.85 |===================================================================== 3 . 4.83 |===================================================================== NCNN 20201218 Target: CPU - Model: googlenet ms < Lower Is Better 1 . 20.58 |================================================================== 2 . 20.48 |================================================================== 3 . 21.10 |==================================================================== NCNN 20201218 Target: CPU - Model: vgg16 ms < Lower Is Better 1 . 40.28 |=================================================================== 2 . 39.97 |=================================================================== 3 . 40.81 |==================================================================== NCNN 20201218 Target: CPU - Model: resnet18 ms < Lower Is Better 1 . 13.86 |=================================================================== 2 . 13.67 |================================================================== 3 . 14.12 |==================================================================== NCNN 20201218 Target: CPU - Model: alexnet ms < Lower Is Better 1 . 10.04 |=============================================================== 2 . 9.80 |============================================================== 3 . 10.83 |==================================================================== NCNN 20201218 Target: CPU - Model: resnet50 ms < Lower Is Better 1 . 26.96 |================================================================= 2 . 28.40 |==================================================================== 3 . 26.77 |================================================================ NCNN 20201218 Target: CPU - Model: yolov4-tiny ms < Lower Is Better 1 . 32.26 |================================================================ 2 . 34.06 |==================================================================== 3 . 32.43 |================================================================= NCNN 20201218 Target: CPU - Model: squeezenet_ssd ms < Lower Is Better 1 . 23.43 |================================================================== 2 . 24.25 |==================================================================== 3 . 23.94 |=================================================================== NCNN 20201218 Target: CPU - Model: regnety_400m ms < Lower Is Better 1 . 66.18 |================================================================== 2 . 68.47 |==================================================================== 3 . 67.53 |=================================================================== Apache Siege 2.4.29 Concurrent Users: 10 Transactions Per Second > Higher Is Better 1 . 21723.41 |================================================================= 2 . 21708.10 |================================================================= 3 . 21771.53 |================================================================= Apache Siege 2.4.29 Concurrent Users: 50 Transactions Per Second > Higher Is Better 1 . 33348.71 |================================================================ 2 . 33392.84 |================================================================ 3 . 33656.66 |================================================================= Apache Siege 2.4.29 Concurrent Users: 100 Transactions Per Second > Higher Is Better 1 . 35458.43 |=============================================================== 2 . 36786.98 |================================================================= 3 . 35464.35 |=============================================================== Apache Siege 2.4.29 Concurrent Users: 200 Transactions Per Second > Higher Is Better 1 . 51905.59 |================================================================= 2 . 44343.84 |======================================================== 3 . 41854.18 |==================================================== Apache Siege 2.4.29 Concurrent Users: 250 Transactions Per Second > Higher Is Better 1 . 45610.34 |================================================================ 2 . 46010.13 |================================================================= 3 . 44492.56 |=============================================================== Apache Siege 2.4.29 Concurrent Users: 500 Transactions Per Second > Higher Is Better 1 . 51574.65 |================================================================= 2 . 48209.46 |============================================================= 3 . 46008.38 |========================================================== WavPack Audio Encoding 5.3 WAV To WavPack Seconds < Lower Is Better 1 . 16.79 |==================================================================== 2 . 16.78 |==================================================================== 3 . 16.81 |==================================================================== BRL-CAD 7.30.8 VGR Performance Metric VGR Performance Metric > Higher Is Better 1 . 253104 |===================================================================