3800XT Xmas AMD Ryzen 7 3800XT 8-Core testing with a MSI X370 XPOWER GAMING TITANIUM (MS-7A31) v1.0 (1.MS BIOS) and Sapphire AMD Radeon HD 4650 on Debian 10 via the Phoronix Test Suite. 1: Processor: AMD Ryzen 7 3800XT 8-Core @ 3.90GHz (8 Cores / 16 Threads), Motherboard: MSI X370 XPOWER GAMING TITANIUM (MS-7A31) v1.0 (1.MS BIOS), Chipset: AMD Starship/Matisse, Memory: 16GB, Disk: 128GB INTEL SSDPEKKW128G7, Graphics: Sapphire AMD Radeon HD 4650, Audio: AMD RV710/730, Network: Intel I211 OS: Debian 10, Kernel: 4.19.0-13-amd64 (x86_64), Display Server: X Server 1.20.4, Display Driver: modesetting 1.20.4, Compiler: GCC 8.3.0, File-System: ext4, Screen Resolution: 1024x768 2: Processor: AMD Ryzen 7 3800XT 8-Core @ 3.90GHz (8 Cores / 16 Threads), Motherboard: MSI X370 XPOWER GAMING TITANIUM (MS-7A31) v1.0 (1.MS BIOS), Chipset: AMD Starship/Matisse, Memory: 16GB, Disk: 128GB INTEL SSDPEKKW128G7, Graphics: Sapphire AMD Radeon HD 4650, Audio: AMD RV710/730, Network: Intel I211 OS: Debian 10, Kernel: 4.19.0-13-amd64 (x86_64), Display Server: X Server 1.20.4, Display Driver: modesetting 1.20.4, Compiler: GCC 8.3.0, File-System: ext4, Screen Resolution: 1024x768 3: Processor: AMD Ryzen 7 3800XT 8-Core @ 3.90GHz (8 Cores / 16 Threads), Motherboard: MSI X370 XPOWER GAMING TITANIUM (MS-7A31) v1.0 (1.MS BIOS), Chipset: AMD Starship/Matisse, Memory: 16GB, Disk: 128GB INTEL SSDPEKKW128G7, Graphics: Sapphire AMD Radeon HD 4650, Audio: AMD RV710/730, Network: Intel I211 OS: Debian 10, Kernel: 4.19.0-13-amd64 (x86_64), Display Server: X Server 1.20.4, Display Driver: modesetting 1.20.4, Compiler: GCC 8.3.0, File-System: ext4, Screen Resolution: 1024x768 CLOMP 1.2 Static OMP Speedup Speedup > Higher Is Better 1 . 24.6 |===================================================================== 2 . 24.3 |==================================================================== 3 . 24.1 |==================================================================== simdjson 0.7.1 Throughput Test: Kostya GB/s > Higher Is Better 1 . 0.79 |===================================================================== 2 . 0.77 |=================================================================== 3 . 0.77 |=================================================================== simdjson 0.7.1 Throughput Test: LargeRandom GB/s > Higher Is Better 1 . 0.52 |===================================================================== 2 . 0.52 |===================================================================== 3 . 0.50 |================================================================== simdjson 0.7.1 Throughput Test: PartialTweets GB/s > Higher Is Better 1 . 0.87 |===================================================================== 2 . 0.87 |===================================================================== 3 . 0.87 |===================================================================== simdjson 0.7.1 Throughput Test: DistinctUserID GB/s > Higher Is Better 1 . 0.91 |=================================================================== 2 . 0.93 |==================================================================== 3 . 0.94 |===================================================================== oneDNN 2.0 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 4.48738 |================================================================== 2 . 4.48906 |================================================================== 3 . 4.48307 |================================================================== oneDNN 2.0 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 8.51187 |================================================================= 2 . 8.62804 |================================================================== 3 . 8.52628 |================================================================= oneDNN 2.0 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 2.54343 |================================================================== 2 . 2.51638 |================================================================= 3 . 2.53349 |================================================================== oneDNN 2.0 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 2.24794 |================================================================== 2 . 2.24690 |================================================================== 3 . 2.21311 |================================================================= oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 22.49 |==================================================================== 2 . 22.36 |==================================================================== 3 . 22.44 |==================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 4.89338 |================================================================== 2 . 4.92244 |================================================================== 3 . 4.92300 |================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 6.46084 |================================================================= 2 . 6.52103 |================================================================== 3 . 6.54317 |================================================================== oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 19.94 |==================================================================== 2 . 20.01 |==================================================================== 3 . 20.00 |==================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 6.08209 |================================================================= 2 . 6.20820 |================================================================== 3 . 6.11717 |================================================================= oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 5.04369 |================================================================== 2 . 5.05767 |================================================================== 3 . 5.03873 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 3738.79 |================================================================== 2 . 3744.57 |================================================================== 3 . 3746.85 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 2559.41 |================================================================= 2 . 2599.00 |================================================================== 3 . 2562.58 |================================================================= oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3737.84 |================================================================== 2 . 3729.25 |================================================================== 3 . 3754.88 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 2539.97 |================================================================== 2 . 2548.96 |================================================================== 3 . 2549.09 |================================================================== oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 4.88553 |================================================================= 2 . 4.92836 |================================================================= 3 . 4.98457 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 3748.94 |================================================================== 2 . 3760.66 |================================================================== 3 . 3729.32 |================================================================= oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 2545.13 |================================================================= 2 . 2589.72 |================================================================== 3 . 2535.92 |================================================================= oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 2.77016 |================================================================= 2 . 2.80307 |================================================================== 3 . 2.79235 |================================================================== Build2 0.13 Time To Compile Seconds < Lower Is Better 1 . 91.95 |==================================================================== 2 . 92.31 |==================================================================== 3 . 91.56 |=================================================================== Timed Eigen Compilation 3.3.9 Time To Compile Seconds < Lower Is Better 1 . 53.64 |=================================================================== 2 . 53.61 |=================================================================== 3 . 54.14 |==================================================================== Monkey Audio Encoding 3.99.6 WAV To APE Seconds < Lower Is Better 1 . 10.88 |==================================================================== 2 . 10.90 |==================================================================== 3 . 10.94 |==================================================================== Opus Codec Encoding 1.3.1 WAV To Opus Encode Seconds < Lower Is Better 1 . 6.672 |================================================================== 2 . 6.843 |==================================================================== 3 . 6.868 |==================================================================== Node.js V8 Web Tooling Benchmark runs/s > Higher Is Better 1 . 11.67 |=================================================================== 2 . 11.85 |==================================================================== 3 . 11.77 |==================================================================== NCNN 20201218 Target: CPU - Model: mobilenet ms < Lower Is Better 1 . 17.71 |=================================================================== 2 . 17.96 |==================================================================== 3 . 17.75 |=================================================================== NCNN 20201218 Target: CPU-v2-v2 - Model: mobilenet-v2 ms < Lower Is Better 1 . 5.65 |==================================================================== 2 . 5.75 |===================================================================== 3 . 5.74 |===================================================================== NCNN 20201218 Target: CPU-v3-v3 - Model: mobilenet-v3 ms < Lower Is Better 1 . 4.75 |=================================================================== 2 . 4.89 |===================================================================== 3 . 4.82 |==================================================================== NCNN 20201218 Target: CPU - Model: shufflenet-v2 ms < Lower Is Better 1 . 6.53 |=================================================================== 2 . 6.75 |===================================================================== 3 . 6.56 |=================================================================== NCNN 20201218 Target: CPU - Model: mnasnet ms < Lower Is Better 1 . 4.55 |==================================================================== 2 . 4.59 |===================================================================== 3 . 4.59 |===================================================================== NCNN 20201218 Target: CPU - Model: efficientnet-b0 ms < Lower Is Better 1 . 7.71 |===================================================================== 2 . 7.60 |==================================================================== 3 . 7.59 |==================================================================== NCNN 20201218 Target: CPU - Model: blazeface ms < Lower Is Better 1 . 2.20 |===================================================================== 2 . 2.20 |===================================================================== 3 . 2.21 |===================================================================== NCNN 20201218 Target: CPU - Model: googlenet ms < Lower Is Better 1 . 15.80 |==================================================================== 2 . 15.90 |==================================================================== 3 . 15.88 |==================================================================== NCNN 20201218 Target: CPU - Model: vgg16 ms < Lower Is Better 1 . 65.01 |=================================================================== 2 . 64.95 |=================================================================== 3 . 65.73 |==================================================================== NCNN 20201218 Target: CPU - Model: resnet18 ms < Lower Is Better 1 . 17.06 |=================================================================== 2 . 17.35 |==================================================================== 3 . 17.15 |=================================================================== NCNN 20201218 Target: CPU - Model: alexnet ms < Lower Is Better 1 . 13.35 |================================================================== 2 . 13.59 |=================================================================== 3 . 13.72 |==================================================================== NCNN 20201218 Target: CPU - Model: resnet50 ms < Lower Is Better 1 . 29.89 |==================================================================== 2 . 29.40 |=================================================================== 3 . 29.56 |=================================================================== NCNN 20201218 Target: CPU - Model: yolov4-tiny ms < Lower Is Better 1 . 27.55 |==================================================================== 2 . 27.02 |=================================================================== 3 . 27.50 |==================================================================== NCNN 20201218 Target: CPU - Model: squeezenet_ssd ms < Lower Is Better 1 . 22.34 |=================================================================== 2 . 22.47 |=================================================================== 3 . 22.70 |==================================================================== NCNN 20201218 Target: CPU - Model: regnety_400m ms < Lower Is Better 1 . 16.91 |=================================================================== 2 . 17.11 |==================================================================== 3 . 17.15 |==================================================================== WavPack Audio Encoding 5.3 WAV To WavPack Seconds < Lower Is Better 1 . 11.61 |=================================================================== 2 . 11.86 |==================================================================== 3 . 11.86 |====================================================================