Core i9 9900K Xmas Intel Core i9-9900K testing with a ASRock Z390M Pro4 (P4.20 BIOS) and Intel UHD 630 3GB on Ubuntu 20.04 via the Phoronix Test Suite. 1: Processor: Intel Core i9-9900K @ 5.00GHz (8 Cores / 16 Threads), Motherboard: ASRock Z390M Pro4 (P4.20 BIOS), Chipset: Intel Cannon Lake PCH, Memory: 16GB, Disk: 240GB Corsair Force MP510, Graphics: Intel UHD 630 3GB (1200MHz), Audio: Realtek ALC892, Monitor: G237HL, Network: Intel I219-V OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc1daily20200819-generic (x86_64) 20200818, Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 4.6 Mesa 20.0.4, OpenCL: OpenCL 2.1, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 2: Processor: Intel Core i9-9900K @ 5.00GHz (8 Cores / 16 Threads), Motherboard: ASRock Z390M Pro4 (P4.20 BIOS), Chipset: Intel Cannon Lake PCH, Memory: 16GB, Disk: 240GB Corsair Force MP510, Graphics: Intel UHD 630 3GB (1200MHz), Audio: Realtek ALC892, Monitor: G237HL, Network: Intel I219-V OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc1daily20200819-generic (x86_64) 20200818, Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 4.6 Mesa 20.0.4, OpenCL: OpenCL 2.1, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 3: Processor: Intel Core i9-9900K @ 5.00GHz (8 Cores / 16 Threads), Motherboard: ASRock Z390M Pro4 (P4.20 BIOS), Chipset: Intel Cannon Lake PCH, Memory: 16GB, Disk: 240GB Corsair Force MP510, Graphics: Intel UHD 630 3GB (1200MHz), Audio: Realtek ALC892, Monitor: G237HL, Network: Intel I219-V OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc1daily20200819-generic (x86_64) 20200818, Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 4.6 Mesa 20.0.4, OpenCL: OpenCL 2.1, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 VkFFT 1.1.1 Benchmark Score > Higher Is Better 1 . 1501 |===================================================================== 2 . 1504 |===================================================================== 3 . 1500 |===================================================================== VkResample 1.0 Upscale: 2x - Precision: Double ms < Lower Is Better 1 . 903.65 |================================================================== 2 . 918.53 |=================================================================== 3 . 903.69 |================================================================== VkResample 1.0 Upscale: 2x - Precision: Single ms < Lower Is Better 1 . 388.58 |=================================================================== 2 . 387.32 |=================================================================== 3 . 388.43 |=================================================================== CLOMP 1.2 Static OMP Speedup Speedup > Higher Is Better 1 . 3.6 |====================================================================== 2 . 3.6 |====================================================================== 3 . 3.3 |================================================================ simdjson 0.7.1 Throughput Test: Kostya GB/s > Higher Is Better 1 . 0.8 |====================================================================== 2 . 0.8 |====================================================================== 3 . 0.8 |====================================================================== simdjson 0.7.1 Throughput Test: LargeRandom GB/s > Higher Is Better 1 . 0.49 |===================================================================== 2 . 0.49 |===================================================================== 3 . 0.49 |===================================================================== simdjson 0.7.1 Throughput Test: PartialTweets GB/s > Higher Is Better 1 . 0.73 |===================================================================== 2 . 0.73 |===================================================================== 3 . 0.73 |===================================================================== simdjson 0.7.1 Throughput Test: DistinctUserID GB/s > Higher Is Better 1 . 0.75 |===================================================================== 2 . 0.75 |===================================================================== 3 . 0.75 |===================================================================== oneDNN 2.0 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 3.84297 |================================================================== 2 . 3.86124 |================================================================== 3 . 3.83181 |================================================================= oneDNN 2.0 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 10.91 |================================================================== 2 . 11.10 |=================================================================== 3 . 11.21 |==================================================================== oneDNN 2.0 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 1.75677 |================================================================== 2 . 1.74322 |================================================================= 3 . 1.72885 |================================================================= oneDNN 2.0 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 2.02879 |================================================================== 2 . 2.00419 |================================================================= 3 . 2.02940 |================================================================== oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 19.50 |==================================================================== 2 . 19.58 |==================================================================== 3 . 19.62 |==================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 5.23865 |================================================================== 2 . 5.26020 |================================================================== 3 . 5.23902 |================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 6.24783 |================================================================== 2 . 6.25847 |================================================================== 3 . 6.23265 |================================================================== oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 17.26 |==================================================================== 2 . 17.15 |==================================================================== 3 . 17.26 |==================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 5.76729 |================================================================== 2 . 5.73649 |================================================================== 3 . 5.71931 |================================================================= oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3.11316 |================================================================== 2 . 3.12973 |================================================================== 3 . 3.11831 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 3758.92 |================================================================== 2 . 3760.83 |================================================================== 3 . 3765.10 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 2073.48 |================================================================== 2 . 2070.81 |================================================================== 3 . 2077.32 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3765.37 |================================================================== 2 . 3767.16 |================================================================== 3 . 3767.18 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 2066.09 |================================================================== 2 . 2074.97 |================================================================== 3 . 2081.02 |================================================================== oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 3.87754 |================================================================== 2 . 3.84547 |================================================================= 3 . 3.85413 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 3763.98 |================================================================== 2 . 3763.03 |================================================================== 3 . 3771.30 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 2072.87 |================================================================== 2 . 2074.81 |================================================================== 3 . 2077.34 |================================================================== oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3.19467 |================================================================== 2 . 3.19403 |================================================================== 3 . 3.19453 |================================================================== Build2 0.13 Time To Compile Seconds < Lower Is Better 1 . 130.42 |=================================================================== 2 . 130.21 |================================================================== 3 . 131.30 |=================================================================== Timed Eigen Compilation 3.3.9 Time To Compile Seconds < Lower Is Better 1 . 66.68 |=================================================================== 2 . 66.84 |==================================================================== 3 . 67.28 |==================================================================== Monkey Audio Encoding 3.99.6 WAV To APE Seconds < Lower Is Better 1 . 9.869 |==================================================================== 2 . 9.867 |==================================================================== 3 . 9.903 |==================================================================== Ogg Audio Encoding 1.3.4 WAV To Ogg Seconds < Lower Is Better 1 . 18.11 |==================================================================== 2 . 18.11 |==================================================================== 3 . 18.16 |==================================================================== Opus Codec Encoding 1.3.1 WAV To Opus Encode Seconds < Lower Is Better 1 . 7.709 |==================================================================== 2 . 7.734 |==================================================================== 3 . 7.699 |==================================================================== Node.js V8 Web Tooling Benchmark runs/s > Higher Is Better 1 . 14.20 |==================================================================== 2 . 13.72 |================================================================== 3 . 14.05 |=================================================================== NCNN 20201218 Target: CPU - Model: mobilenet ms < Lower Is Better 1 . 17.84 |==================================================================== 2 . 17.82 |==================================================================== 3 . 17.82 |==================================================================== NCNN 20201218 Target: CPU-v2-v2 - Model: mobilenet-v2 ms < Lower Is Better 1 . 4.90 |===================================================================== 2 . 4.91 |===================================================================== 3 . 4.90 |===================================================================== NCNN 20201218 Target: CPU-v3-v3 - Model: mobilenet-v3 ms < Lower Is Better 1 . 3.79 |==================================================================== 2 . 3.84 |===================================================================== 3 . 3.77 |==================================================================== NCNN 20201218 Target: CPU - Model: shufflenet-v2 ms < Lower Is Better 1 . 4.75 |===================================================================== 2 . 4.77 |===================================================================== 3 . 4.76 |===================================================================== NCNN 20201218 Target: CPU - Model: mnasnet ms < Lower Is Better 1 . 3.70 |===================================================================== 2 . 3.72 |===================================================================== 3 . 3.69 |==================================================================== NCNN 20201218 Target: CPU - Model: efficientnet-b0 ms < Lower Is Better 1 . 6.15 |===================================================================== 2 . 6.13 |===================================================================== 3 . 6.14 |===================================================================== NCNN 20201218 Target: CPU - Model: blazeface ms < Lower Is Better 1 . 1.78 |===================================================================== 2 . 1.76 |==================================================================== 3 . 1.76 |==================================================================== NCNN 20201218 Target: CPU - Model: googlenet ms < Lower Is Better 1 . 13.51 |==================================================================== 2 . 13.45 |==================================================================== 3 . 13.53 |==================================================================== NCNN 20201218 Target: CPU - Model: vgg16 ms < Lower Is Better 1 . 59.10 |==================================================================== 2 . 59.01 |==================================================================== 3 . 59.01 |==================================================================== NCNN 20201218 Target: CPU - Model: resnet18 ms < Lower Is Better 1 . 13.17 |==================================================================== 2 . 13.17 |==================================================================== 3 . 13.19 |==================================================================== NCNN 20201218 Target: CPU - Model: alexnet ms < Lower Is Better 1 . 11.27 |==================================================================== 2 . 11.25 |==================================================================== 3 . 11.30 |==================================================================== NCNN 20201218 Target: CPU - Model: resnet50 ms < Lower Is Better 1 . 25.08 |==================================================================== 2 . 25.05 |==================================================================== 3 . 25.17 |==================================================================== NCNN 20201218 Target: CPU - Model: yolov4-tiny ms < Lower Is Better 1 . 25.56 |==================================================================== 2 . 25.57 |==================================================================== 3 . 25.62 |==================================================================== NCNN 20201218 Target: CPU - Model: squeezenet_ssd ms < Lower Is Better 1 . 18.88 |==================================================================== 2 . 18.93 |==================================================================== 3 . 18.88 |==================================================================== NCNN 20201218 Target: CPU - Model: regnety_400m ms < Lower Is Better 1 . 12.78 |==================================================================== 2 . 12.49 |================================================================== 3 . 12.33 |================================================================== NCNN 20201218 Target: Vulkan GPU - Model: mobilenet ms < Lower Is Better 1 . 17.90 |==================================================================== 2 . 17.82 |==================================================================== 3 . 17.87 |==================================================================== NCNN 20201218 Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2 ms < Lower Is Better 1 . 4.89 |===================================================================== 2 . 4.92 |===================================================================== 3 . 4.92 |===================================================================== NCNN 20201218 Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3 ms < Lower Is Better 1 . 3.74 |==================================================================== 2 . 3.81 |===================================================================== 3 . 3.79 |===================================================================== NCNN 20201218 Target: Vulkan GPU - Model: shufflenet-v2 ms < Lower Is Better 1 . 4.76 |==================================================================== 2 . 4.84 |===================================================================== 3 . 4.80 |==================================================================== NCNN 20201218 Target: Vulkan GPU - Model: mnasnet ms < Lower Is Better 1 . 3.72 |=================================================================== 2 . 3.84 |===================================================================== 3 . 3.72 |=================================================================== NCNN 20201218 Target: Vulkan GPU - Model: efficientnet-b0 ms < Lower Is Better 1 . 6.21 |===================================================================== 2 . 6.24 |===================================================================== 3 . 6.17 |==================================================================== NCNN 20201218 Target: Vulkan GPU - Model: blazeface ms < Lower Is Better 1 . 1.75 |=================================================================== 2 . 1.80 |===================================================================== 3 . 1.76 |=================================================================== NCNN 20201218 Target: Vulkan GPU - Model: googlenet ms < Lower Is Better 1 . 13.47 |==================================================================== 2 . 13.52 |==================================================================== 3 . 13.48 |==================================================================== NCNN 20201218 Target: Vulkan GPU - Model: vgg16 ms < Lower Is Better 1 . 59.16 |==================================================================== 2 . 58.95 |==================================================================== 3 . 58.95 |==================================================================== NCNN 20201218 Target: Vulkan GPU - Model: resnet18 ms < Lower Is Better 1 . 13.26 |==================================================================== 2 . 13.16 |=================================================================== 3 . 13.33 |==================================================================== NCNN 20201218 Target: Vulkan GPU - Model: alexnet ms < Lower Is Better 1 . 11.29 |==================================================================== 2 . 11.25 |==================================================================== 3 . 11.27 |==================================================================== NCNN 20201218 Target: Vulkan GPU - Model: resnet50 ms < Lower Is Better 1 . 25.46 |==================================================================== 2 . 25.04 |=================================================================== 3 . 25.19 |=================================================================== NCNN 20201218 Target: Vulkan GPU - Model: yolov4-tiny ms < Lower Is Better 1 . 25.63 |================================================================= 2 . 25.59 |================================================================= 3 . 26.69 |==================================================================== NCNN 20201218 Target: Vulkan GPU - Model: squeezenet_ssd ms < Lower Is Better 1 . 18.93 |==================================================================== 2 . 18.62 |=================================================================== 3 . 18.72 |=================================================================== NCNN 20201218 Target: Vulkan GPU - Model: regnety_400m ms < Lower Is Better 1 . 12.69 |================================================================ 2 . 13.40 |==================================================================== 3 . 12.41 |=============================================================== WavPack Audio Encoding 5.3 WAV To WavPack Seconds < Lower Is Better 1 . 13.05 |==================================================================== 2 . 13.03 |==================================================================== 3 . 13.05 |====================================================================