Xeon Gold 6226R December Intel Xeon Gold 6226R testing with a Supermicro X11SPL-F v1.02 (3.1 BIOS) and llvmpipe on Ubuntu 20.04 via the Phoronix Test Suite. 1: Processor: Intel Xeon Gold 6226R @ 3.90GHz (16 Cores / 32 Threads), Motherboard: Supermicro X11SPL-F v1.02 (3.1 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 188GB, Disk: 3841GB Micron_9300_MTFDHAL3T8TDP, Graphics: llvmpipe, Monitor: VE228, Network: 2 x Intel I210 OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc6daily20200921-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 20.0.8 (LLVM 10.0.0 256 bits), Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 2: Processor: Intel Xeon Gold 6226R @ 3.90GHz (16 Cores / 32 Threads), Motherboard: Supermicro X11SPL-F v1.02 (3.1 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 188GB, Disk: 3841GB Micron_9300_MTFDHAL3T8TDP, Graphics: llvmpipe, Monitor: VE228, Network: 2 x Intel I210 OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc6daily20200921-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 20.0.8 (LLVM 10.0.0 256 bits), Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 3: Processor: Intel Xeon Gold 6226R @ 3.90GHz (16 Cores / 32 Threads), Motherboard: Supermicro X11SPL-F v1.02 (3.1 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 188GB, Disk: 3841GB Micron_9300_MTFDHAL3T8TDP, Graphics: llvmpipe, Monitor: VE228, Network: 2 x Intel I210 OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc6daily20200921-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 20.0.8 (LLVM 10.0.0 256 bits), Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 CLOMP 1.2 Static OMP Speedup Speedup > Higher Is Better 1 . 26.8 |===================================================================== 2 . 26.2 |=================================================================== 3 . 26.5 |==================================================================== Timed HMMer Search 3.3.1 Pfam Database Search Seconds < Lower Is Better 1 . 174.23 |=================================================================== 2 . 174.17 |=================================================================== 3 . 174.45 |=================================================================== Timed MAFFT Alignment 7.471 Multiple Sequence Alignment - LSU RNA Seconds < Lower Is Better 1 . 10.64 |==================================================================== 2 . 10.59 |==================================================================== 3 . 10.55 |=================================================================== 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 . 0.57 |===================================================================== 2 . 0.57 |===================================================================== 3 . 0.57 |===================================================================== 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 . 2.35358 |================================================================== 2 . 2.35038 |================================================================== 3 . 2.34990 |================================================================== oneDNN 2.0 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 3.14816 |================================================================== 2 . 3.13707 |================================================================== 3 . 3.15835 |================================================================== oneDNN 2.0 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 0.496245 |================================================================= 2 . 0.491459 |================================================================ 3 . 0.495733 |================================================================= oneDNN 2.0 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 1.24491 |================================================================== 2 . 1.24781 |================================================================== 3 . 1.24380 |================================================================== oneDNN 2.0 Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 5.61346 |================================================================== 2 . 5.60704 |================================================================== 3 . 5.60477 |================================================================== oneDNN 2.0 Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 2.59610 |================================================================== 2 . 2.58806 |================================================================= 3 . 2.61350 |================================================================== oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 4.34336 |================================================================== 2 . 4.34082 |================================================================== 3 . 4.35080 |================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 2.75497 |================================================================== 2 . 2.76210 |================================================================== 3 . 2.75619 |================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 3.21126 |================================================================== 2 . 3.20825 |================================================================== 3 . 3.21278 |================================================================== oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 4.16656 |================================================================== 2 . 4.16237 |================================================================== 3 . 4.17445 |================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 0.526692 |================================================================= 2 . 0.527252 |================================================================= 3 . 0.526437 |================================================================= oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 0.867040 |================================================================= 2 . 0.860663 |================================================================= 3 . 0.862856 |================================================================= oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 1643.22 |================================================================== 2 . 1645.08 |================================================================== 3 . 1647.12 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 922.99 |=================================================================== 2 . 923.54 |=================================================================== 3 . 922.27 |=================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 1643.59 |================================================================== 2 . 1645.64 |================================================================== 3 . 1645.91 |================================================================== oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 9.42174 |================================================================== 2 . 9.42301 |================================================================== 3 . 9.42277 |================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 11.29 |==================================================================== 2 . 11.29 |==================================================================== 3 . 11.32 |==================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 12.53 |==================================================================== 2 . 12.54 |==================================================================== 3 . 12.53 |==================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 922.87 |=================================================================== 2 . 923.37 |=================================================================== 3 . 923.34 |=================================================================== oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 0.974624 |================================================================= 2 . 0.973874 |================================================================= 3 . 0.977073 |================================================================= oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 1645.61 |================================================================== 2 . 1643.67 |================================================================== 3 . 1644.83 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 921.67 |=================================================================== 2 . 923.44 |=================================================================== 3 . 920.86 |=================================================================== oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 0.486440 |================================================================= 2 . 0.477951 |================================================================ 3 . 0.479216 |================================================================ oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 2.05419 |================================================================== 2 . 2.05772 |================================================================== 3 . 2.05675 |================================================================== Coremark 1.0 CoreMark Size 666 - Iterations Per Second Iterations/Sec > Higher Is Better 1 . 537952.81 |================================================================ 2 . 535255.63 |================================================================ 3 . 534883.16 |================================================================ Timed FFmpeg Compilation 4.2.2 Time To Compile Seconds < Lower Is Better 1 . 43.50 |==================================================================== 2 . 43.24 |==================================================================== 3 . 43.45 |==================================================================== Build2 0.13 Time To Compile Seconds < Lower Is Better 1 . 95.58 |==================================================================== 2 . 95.69 |==================================================================== 3 . 95.53 |==================================================================== Timed Eigen Compilation 3.3.9 Time To Compile Seconds < Lower Is Better 1 . 85.53 |==================================================================== 2 . 85.55 |==================================================================== 3 . 85.78 |==================================================================== Monkey Audio Encoding 3.99.6 WAV To APE Seconds < Lower Is Better 1 . 17.53 |==================================================================== 2 . 17.54 |==================================================================== 3 . 17.54 |==================================================================== Node.js V8 Web Tooling Benchmark runs/s > Higher Is Better 1 . 10.78 |==================================================================== 2 . 10.69 |=================================================================== 3 . 10.68 |=================================================================== SQLite Speedtest 3.30 Timed Time - Size 1,000 Seconds < Lower Is Better 1 . 65.36 |=================================================================== 2 . 65.85 |==================================================================== 3 . 65.59 |==================================================================== NCNN 20201218 Target: CPU - Model: mobilenet ms < Lower Is Better 1 . 17.77 |==================================================================== 2 . 16.96 |================================================================= 3 . 17.73 |==================================================================== NCNN 20201218 Target: CPU-v2-v2 - Model: mobilenet-v2 ms < Lower Is Better 1 . 6.07 |===================================================================== 2 . 5.96 |==================================================================== 3 . 5.89 |=================================================================== NCNN 20201218 Target: CPU-v3-v3 - Model: mobilenet-v3 ms < Lower Is Better 1 . 5.21 |===================================================================== 2 . 5.14 |==================================================================== 3 . 5.07 |=================================================================== NCNN 20201218 Target: CPU - Model: shufflenet-v2 ms < Lower Is Better 1 . 5.94 |==================================================================== 2 . 5.97 |===================================================================== 3 . 6.01 |===================================================================== NCNN 20201218 Target: CPU - Model: mnasnet ms < Lower Is Better 1 . 5.56 |===================================================================== 2 . 5.37 |=================================================================== 3 . 5.34 |================================================================== NCNN 20201218 Target: CPU - Model: efficientnet-b0 ms < Lower Is Better 1 . 7.70 |===================================================================== 2 . 7.26 |================================================================= 3 . 7.20 |================================================================= NCNN 20201218 Target: CPU - Model: blazeface ms < Lower Is Better 1 . 2.93 |===================================================================== 2 . 2.88 |==================================================================== 3 . 2.90 |==================================================================== NCNN 20201218 Target: CPU - Model: googlenet ms < Lower Is Better 1 . 14.56 |==================================================================== 2 . 12.99 |============================================================= 3 . 13.09 |============================================================= NCNN 20201218 Target: CPU - Model: vgg16 ms < Lower Is Better 1 . 29.65 |==================================================================== 2 . 28.04 |================================================================ 3 . 29.24 |=================================================================== NCNN 20201218 Target: CPU - Model: resnet18 ms < Lower Is Better 1 . 10.58 |==================================================================== 2 . 9.41 |============================================================ 3 . 9.49 |============================================================= NCNN 20201218 Target: CPU - Model: alexnet ms < Lower Is Better 1 . 8.07 |===================================================================== 2 . 6.73 |========================================================== 3 . 6.99 |============================================================ NCNN 20201218 Target: CPU - Model: resnet50 ms < Lower Is Better 1 . 20.27 |==================================================================== 2 . 18.93 |=============================================================== 3 . 20.38 |==================================================================== NCNN 20201218 Target: CPU - Model: yolov4-tiny ms < Lower Is Better 1 . 24.72 |=================================================================== 2 . 24.40 |================================================================== 3 . 25.22 |==================================================================== NCNN 20201218 Target: CPU - Model: squeezenet_ssd ms < Lower Is Better 1 . 16.73 |=================================================================== 2 . 16.45 |================================================================== 3 . 16.94 |==================================================================== NCNN 20201218 Target: CPU - Model: regnety_400m ms < Lower Is Better 1 . 27.19 |=================================================================== 2 . 27.13 |=================================================================== 3 . 27.41 |==================================================================== WavPack Audio Encoding 5.3 WAV To WavPack Seconds < Lower Is Better 1 . 16.73 |==================================================================== 2 . 16.75 |==================================================================== 3 . 16.78 |==================================================================== BRL-CAD 7.30.8 VGR Performance Metric VGR Performance Metric > Higher Is Better 1 . 164354 |=================================================================== 2 . 163713 |===================================================================