Core i3 10100 Xmas Eve Intel Core i3-10100 testing with a Gigabyte B460M DS3H (F2 BIOS) and Gigabyte Intel UHD 630 3GB on Ubuntu 20.04 via the Phoronix Test Suite. 1: Processor: Intel Core i3-10100 @ 4.30GHz (4 Cores / 8 Threads), Motherboard: Gigabyte B460M DS3H (F2 BIOS), Chipset: Intel Device 9b63, Memory: 16GB, Disk: 500GB Western Digital WDS500G3X0C-00SJG0, Graphics: Gigabyte Intel UHD 630 3GB (1100MHz), Audio: Realtek ALC887-VD, Monitor: G237HL, Network: Realtek RTL8111/8168/8411 OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc7daily20201002-generic (x86_64) 20201001, Desktop: GNOME Shell 3.36.3, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 4.6 Mesa 20.0.8, Vulkan: 1.2.131, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 2: Processor: Intel Core i3-10100 @ 4.30GHz (4 Cores / 8 Threads), Motherboard: Gigabyte B460M DS3H (F2 BIOS), Chipset: Intel Device 9b63, Memory: 16GB, Disk: 500GB Western Digital WDS500G3X0C-00SJG0, Graphics: Gigabyte Intel UHD 630 3GB (1100MHz), Audio: Realtek ALC887-VD, Monitor: G237HL, Network: Realtek RTL8111/8168/8411 OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc7daily20201002-generic (x86_64) 20201001, Desktop: GNOME Shell 3.36.3, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 4.6 Mesa 20.0.8, Vulkan: 1.2.131, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 3: Processor: Intel Core i3-10100 @ 4.30GHz (4 Cores / 8 Threads), Motherboard: Gigabyte B460M DS3H (F2 BIOS), Chipset: Intel Device 9b63, Memory: 16GB, Disk: 500GB Western Digital WDS500G3X0C-00SJG0, Graphics: Gigabyte Intel UHD 630 3GB (1100MHz), Audio: Realtek ALC887-VD, Monitor: G237HL, Network: Realtek RTL8111/8168/8411 OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc7daily20201002-generic (x86_64) 20201001, Desktop: GNOME Shell 3.36.3, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 4.6 Mesa 20.0.8, Vulkan: 1.2.131, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 CLOMP 1.2 Static OMP Speedup Speedup > Higher Is Better 1 . 1 |======================================================================== 2 . 1 |======================================================================== 3 . 1 |======================================================================== VKMark 2020-05-21 Resolution: 1920 x 1080 VKMark Score > Higher Is Better 1 . 574 |====================================================================== 2 . 525 |================================================================ 3 . 525 |================================================================ VKMark 2020-05-21 Resolution: 1280 x 1024 VKMark Score > Higher Is Better 1 . 776 |====================================================================== 2 . 740 |=================================================================== 3 . 740 |=================================================================== Monkey Audio Encoding 3.99.6 WAV To APE Seconds < Lower Is Better 1 . 11.80 |==================================================================== 2 . 11.79 |==================================================================== 3 . 11.78 |==================================================================== Opus Codec Encoding 1.3.1 WAV To Opus Encode Seconds < Lower Is Better 1 . 8.681 |==================================================================== 2 . 8.673 |==================================================================== 3 . 8.669 |==================================================================== WavPack Audio Encoding 5.3 WAV To WavPack Seconds < Lower Is Better 1 . 15.21 |==================================================================== 2 . 15.21 |==================================================================== 3 . 15.21 |==================================================================== NCNN 20201218 Target: CPU - Model: mobilenet ms < Lower Is Better 1 . 30.02 |=================================================================== 2 . 30.54 |==================================================================== 3 . 30.52 |==================================================================== NCNN 20201218 Target: CPU-v2-v2 - Model: mobilenet-v2 ms < Lower Is Better 1 . 7.77 |==================================================================== 2 . 7.91 |===================================================================== 3 . 7.91 |===================================================================== NCNN 20201218 Target: CPU-v3-v3 - Model: mobilenet-v3 ms < Lower Is Better 1 . 6.36 |==================================================================== 2 . 6.47 |===================================================================== 3 . 6.46 |===================================================================== NCNN 20201218 Target: CPU - Model: shufflenet-v2 ms < Lower Is Better 1 . 8.27 |===================================================================== 2 . 8.27 |===================================================================== 3 . 8.28 |===================================================================== NCNN 20201218 Target: CPU - Model: mnasnet ms < Lower Is Better 1 . 6.10 |==================================================================== 2 . 6.16 |===================================================================== 3 . 6.18 |===================================================================== NCNN 20201218 Target: CPU - Model: efficientnet-b0 ms < Lower Is Better 1 . 10.42 |=================================================================== 2 . 10.61 |==================================================================== 3 . 10.62 |==================================================================== NCNN 20201218 Target: CPU - Model: blazeface ms < Lower Is Better 1 . 2.21 |===================================================================== 2 . 2.21 |===================================================================== 3 . 2.22 |===================================================================== NCNN 20201218 Target: CPU - Model: googlenet ms < Lower Is Better 1 . 21.11 |================================================================== 2 . 21.53 |==================================================================== 3 . 21.60 |==================================================================== NCNN 20201218 Target: CPU - Model: vgg16 ms < Lower Is Better 1 . 94.55 |=================================================================== 2 . 95.09 |==================================================================== 3 . 95.30 |==================================================================== NCNN 20201218 Target: CPU - Model: resnet18 ms < Lower Is Better 1 . 21.32 |=================================================================== 2 . 21.72 |==================================================================== 3 . 21.73 |==================================================================== NCNN 20201218 Target: CPU - Model: alexnet ms < Lower Is Better 1 . 16.99 |=================================================================== 2 . 17.22 |==================================================================== 3 . 17.25 |==================================================================== NCNN 20201218 Target: CPU - Model: resnet50 ms < Lower Is Better 1 . 44.96 |=================================================================== 2 . 45.52 |==================================================================== 3 . 45.53 |==================================================================== NCNN 20201218 Target: CPU - Model: yolov4-tiny ms < Lower Is Better 1 . 41.04 |=================================================================== 2 . 41.86 |==================================================================== 3 . 41.87 |==================================================================== NCNN 20201218 Target: CPU - Model: squeezenet_ssd ms < Lower Is Better 1 . 30.56 |=================================================================== 2 . 31.10 |==================================================================== 3 . 31.14 |==================================================================== NCNN 20201218 Target: CPU - Model: regnety_400m ms < Lower Is Better 1 . 13.67 |===================================== 2 . 25.27 |==================================================================== 3 . 13.71 |===================================== NCNN 20201218 Target: Vulkan GPU - Model: mobilenet ms < Lower Is Better 1 . 30.06 |=================================================================== 2 . 30.51 |==================================================================== 3 . 30.52 |==================================================================== NCNN 20201218 Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2 ms < Lower Is Better 1 . 7.78 |==================================================================== 2 . 7.92 |===================================================================== 3 . 7.94 |===================================================================== NCNN 20201218 Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3 ms < Lower Is Better 1 . 6.36 |==================================================================== 2 . 6.47 |===================================================================== 3 . 6.48 |===================================================================== NCNN 20201218 Target: Vulkan GPU - Model: shufflenet-v2 ms < Lower Is Better 1 . 8.27 |===================================================================== 2 . 8.29 |===================================================================== 3 . 8.28 |===================================================================== NCNN 20201218 Target: Vulkan GPU - Model: mnasnet ms < Lower Is Better 1 . 6.12 |==================================================================== 2 . 6.17 |===================================================================== 3 . 6.18 |===================================================================== NCNN 20201218 Target: Vulkan GPU - Model: efficientnet-b0 ms < Lower Is Better 1 . 10.42 |=================================================================== 2 . 10.61 |==================================================================== 3 . 10.61 |==================================================================== NCNN 20201218 Target: Vulkan GPU - Model: blazeface ms < Lower Is Better 1 . 2.22 |===================================================================== 2 . 2.21 |===================================================================== 3 . 2.21 |===================================================================== NCNN 20201218 Target: Vulkan GPU - Model: googlenet ms < Lower Is Better 1 . 21.07 |=================================================================== 2 . 21.52 |==================================================================== 3 . 21.52 |==================================================================== NCNN 20201218 Target: Vulkan GPU - Model: vgg16 ms < Lower Is Better 1 . 94.48 |=================================================================== 2 . 95.16 |==================================================================== 3 . 95.21 |==================================================================== NCNN 20201218 Target: Vulkan GPU - Model: resnet18 ms < Lower Is Better 1 . 21.32 |=================================================================== 2 . 21.69 |==================================================================== 3 . 21.71 |==================================================================== NCNN 20201218 Target: Vulkan GPU - Model: alexnet ms < Lower Is Better 1 . 17.02 |==================================================================== 2 . 17.12 |==================================================================== 3 . 17.12 |==================================================================== NCNN 20201218 Target: Vulkan GPU - Model: resnet50 ms < Lower Is Better 1 . 44.92 |=================================================================== 2 . 45.52 |==================================================================== 3 . 45.52 |==================================================================== NCNN 20201218 Target: Vulkan GPU - Model: yolov4-tiny ms < Lower Is Better 1 . 41.00 |=================================================================== 2 . 41.86 |==================================================================== 3 . 41.87 |==================================================================== NCNN 20201218 Target: Vulkan GPU - Model: squeezenet_ssd ms < Lower Is Better 1 . 30.54 |=================================================================== 2 . 31.06 |==================================================================== 3 . 31.11 |==================================================================== NCNN 20201218 Target: Vulkan GPU - Model: regnety_400m ms < Lower Is Better 1 . 13.66 |==================================================================== 2 . 13.75 |==================================================================== 3 . 13.72 |==================================================================== oneDNN 2.0 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 8.38005 |================================================================== 2 . 8.42143 |================================================================== 3 . 8.42214 |================================================================== oneDNN 2.0 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 19.08 |=================================================================== 2 . 19.48 |==================================================================== 3 . 19.45 |==================================================================== oneDNN 2.0 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3.20623 |================================================================== 2 . 3.21059 |================================================================== 3 . 3.20943 |================================================================== oneDNN 2.0 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3.13581 |================================================================= 2 . 3.20505 |================================================================== 3 . 3.18814 |================================================================== oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 32.51 |==================================================================== 2 . 32.70 |==================================================================== 3 . 32.66 |==================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 9.61175 |================================================================== 2 . 9.59852 |================================================================== 3 . 9.62788 |================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 13.21 |=================================================================== 2 . 13.37 |==================================================================== 3 . 13.19 |=================================================================== oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 25.44 |=================================================================== 2 . 25.87 |==================================================================== 3 . 25.81 |==================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 9.13529 |================================================================ 2 . 9.17848 |================================================================ 3 . 9.44103 |================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 7.40439 |================================================================== 2 . 7.38660 |================================================================== 3 . 7.40681 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 6880.64 |================================================================== 2 . 6892.35 |================================================================== 3 . 6897.36 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 3856.65 |================================================================= 2 . 3919.52 |================================================================== 3 . 3890.67 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 6878.81 |================================================================== 2 . 6894.20 |================================================================== 3 . 6891.19 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3869.18 |================================================================= 2 . 3918.96 |================================================================== 3 . 3892.06 |================================================================== oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 6.25800 |================================================================== 2 . 6.26061 |================================================================== 3 . 6.24930 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 6878.53 |================================================================== 2 . 6895.47 |================================================================== 3 . 6893.03 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 3861.49 |================================================================= 2 . 3919.44 |================================================================== 3 . 3912.80 |================================================================== oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 5.21634 |================================================================== 2 . 5.22700 |================================================================== 3 . 5.22065 |================================================================== Build2 0.13 Time To Compile Seconds < Lower Is Better 1 . 256.45 |=================================================================== 2 . 255.15 |=================================================================== 3 . 253.97 |================================================================== Unpacking Firefox 84.0 Extracting: firefox-84.0.source.tar.xz Seconds < Lower Is Better 1 . 18.39 |==================================================================== 2 . 18.42 |==================================================================== 3 . 18.42 |==================================================================== Timed Eigen Compilation 3.3.9 Time To Compile Seconds < Lower Is Better 1 . 78.29 |==================================================================== 2 . 78.49 |==================================================================== 3 . 78.72 |==================================================================== VkFFT 1.1.1 Benchmark Score > Higher Is Better 1 . 1314 |===================================================================== 2 . 1305 |===================================================================== 3 . 1304 |==================================================================== VkResample 1.0 Upscale: 2x - Precision: Double ms < Lower Is Better 1 . 1008.65 |================================================================= 2 . 1018.32 |================================================================== 3 . 1008.14 |================================================================= VkResample 1.0 Upscale: 2x - Precision: Single ms < Lower Is Better 1 . 458.51 |=================================================================== 2 . 456.83 |=================================================================== 3 . 457.04 |=================================================================== Node.js V8 Web Tooling Benchmark runs/s > Higher Is Better 1 . 12.09 |==================================================================== 2 . 11.70 |================================================================== 3 . 11.96 |=================================================================== simdjson 0.7.1 Throughput Test: Kostya GB/s > Higher Is Better 1 . 0.66 |===================================================================== 2 . 0.66 |===================================================================== 3 . 0.66 |===================================================================== simdjson 0.7.1 Throughput Test: LargeRandom GB/s > Higher Is Better 1 . 0.44 |===================================================================== 2 . 0.44 |===================================================================== 3 . 0.44 |===================================================================== simdjson 0.7.1 Throughput Test: PartialTweets GB/s > Higher Is Better 1 . 0.76 |===================================================================== 2 . 0.76 |===================================================================== 3 . 0.76 |===================================================================== simdjson 0.7.1 Throughput Test: DistinctUserID GB/s > Higher Is Better 1 . 0.77 |===================================================================== 2 . 0.77 |===================================================================== 3 . 0.77 |=====================================================================