Xeon E3 v3 1231 Intel Xeon E3-1231 v3 testing with a Gigabyte H81M-S1 (FF BIOS) and Sapphire AMD Radeon HD 4550 on Debian 10 via the Phoronix Test Suite. 1: Processor: Intel Xeon E3-1231 v3 @ 3.80GHz (4 Cores / 8 Threads), Motherboard: Gigabyte H81M-S1 (FF BIOS), Chipset: Intel Xeon E3-1200 v3 DRAM, Memory: 16GB, Disk: 120GB Samsung SSD 850, Graphics: Sapphire AMD Radeon HD 4550, Audio: Realtek ALC887-VD, Network: Realtek RTL8111/8168/8411 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: Intel Xeon E3-1231 v3 @ 3.80GHz (4 Cores / 8 Threads), Motherboard: Gigabyte H81M-S1 (FF BIOS), Chipset: Intel Xeon E3-1200 v3 DRAM, Memory: 16GB, Disk: 120GB Samsung SSD 850, Graphics: Sapphire AMD Radeon HD 4550, Audio: Realtek ALC887-VD, Network: Realtek RTL8111/8168/8411 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: Intel Xeon E3-1231 v3 @ 3.80GHz (4 Cores / 8 Threads), Motherboard: Gigabyte H81M-S1 (FF BIOS), Chipset: Intel Xeon E3-1200 v3 DRAM, Memory: 16GB, Disk: 120GB Samsung SSD 850, Graphics: Sapphire AMD Radeon HD 4550, Audio: Realtek ALC887-VD, Network: Realtek RTL8111/8168/8411 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 Timed MAFFT Alignment 7.471 Multiple Sequence Alignment - LSU RNA Seconds < Lower Is Better 1 . 12.85 |==================================================================== 2 . 12.90 |==================================================================== 3 . 12.94 |==================================================================== simdjson 0.7.1 Throughput Test: Kostya Documents/s > Higher Is Better 1 . 4 |======================================================================== simdjson 0.7.1 Throughput Test: Kostya GB/s > Higher Is Better 1 . 0.66 |===================================================================== simdjson 0.7.1 Throughput Test: LargeRandom Documents/s > Higher Is Better 1 . 9 |======================================================================== simdjson 0.7.1 Throughput Test: LargeRandom GB/s > Higher Is Better 1 . 0.42 |===================================================================== simdjson 0.7.1 Throughput Test: PartialTweets Documents/s > Higher Is Better 1 . 1098 |===================================================================== simdjson 0.7.1 Throughput Test: PartialTweets GB/s > Higher Is Better 1 . 0.69 |===================================================================== simdjson 0.7.1 Throughput Test: DistinctUserID Documents/s > Higher Is Better 1 . 1129 |===================================================================== simdjson 0.7.1 Throughput Test: DistinctUserID GB/s > Higher Is Better 1 . 0.71 |===================================================================== Crafty 25.2 Elapsed Time Nodes Per Second > Higher Is Better 1 . 7079403 |================================================================== 2 . 7066898 |================================================================== 3 . 7079168 |================================================================== oneDNN 2.0 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 9.49609 |================================================================== 2 . 9.50836 |================================================================== 3 . 9.50498 |================================================================== oneDNN 2.0 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 13.51 |=================================================================== 2 . 13.53 |=================================================================== 3 . 13.73 |==================================================================== oneDNN 2.0 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 5.78204 |================================================================== 2 . 5.77484 |================================================================== 3 . 5.80286 |================================================================== oneDNN 2.0 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 4.00556 |================================================================== 2 . 3.98145 |================================================================== 3 . 3.98156 |================================================================== oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 25.63 |==================================================================== 2 . 25.73 |==================================================================== 3 . 25.63 |==================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 12.44 |==================================================================== 2 . 12.52 |==================================================================== 3 . 12.44 |==================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 18.85 |==================================================================== 2 . 18.91 |==================================================================== 3 . 18.73 |=================================================================== oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 25.07 |==================================================================== 2 . 25.22 |==================================================================== 3 . 25.25 |==================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 13.15 |================================================================== 2 . 13.50 |==================================================================== 3 . 13.12 |================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 12.28 |==================================================================== 2 . 12.12 |=================================================================== 3 . 12.17 |=================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 9189.79 |============================================================== 2 . 9786.43 |================================================================== 3 . 9461.70 |================================================================ oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 5112.41 |=============================================================== 2 . 5366.10 |================================================================== 3 . 5279.26 |================================================================= oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 9731.61 |============================================================== 2 . 10261.00 |================================================================= 3 . 9833.44 |============================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 5188.53 |=============================================================== 2 . 5471.01 |================================================================== 3 . 5167.34 |============================================================== oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 6.71150 |================================================================== 2 . 6.68687 |================================================================== 3 . 6.66374 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 9899.38 |============================================================== 2 . 10399.40 |================================================================= 3 . 9576.20 |============================================================ oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 5370.81 |================================================================ 2 . 5513.84 |================================================================== 3 . 5243.29 |=============================================================== oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 7.01205 |================================================================ 2 . 7.23953 |================================================================== 3 . 6.93637 |=============================================================== rav1e 0.4 Alpha Speed: 1 Frames Per Second > Higher Is Better 1 . 0.290 |==================================================================== 2 . 0.282 |================================================================== 3 . 0.289 |==================================================================== rav1e 0.4 Alpha Speed: 5 Frames Per Second > Higher Is Better 1 . 0.886 |==================================================================== 2 . 0.890 |==================================================================== 3 . 0.885 |==================================================================== rav1e 0.4 Alpha Speed: 6 Frames Per Second > Higher Is Better 1 . 1.196 |==================================================================== 2 . 1.186 |=================================================================== 3 . 1.196 |==================================================================== rav1e 0.4 Alpha Speed: 10 Frames Per Second > Higher Is Better 1 . 2.670 |=================================================================== 2 . 2.670 |=================================================================== 3 . 2.701 |==================================================================== Coremark 1.0 CoreMark Size 666 - Iterations Per Second Iterations/Sec > Higher Is Better 1 . 141521.96 |================================================================ 2 . 140052.78 |=============================================================== 3 . 141385.89 |================================================================ Stockfish 12 Total Time Nodes Per Second > Higher Is Better 1 . 6604184 |================================================================== 2 . 6224835 |============================================================== 3 . 6421783 |================================================================ asmFish 2018-07-23 1024 Hash Memory, 26 Depth Nodes/second > Higher Is Better 1 . 9945950 |================================================================== 2 . 9854362 |================================================================= Timed FFmpeg Compilation 4.2.2 Time To Compile Seconds < Lower Is Better 1 . 135.61 |=================================================================== 2 . 134.02 |================================================================== SQLite Speedtest 3.30 Timed Time - Size 1,000 Seconds < Lower Is Better 1 . 70.78 |=================================================================== 2 . 71.35 |==================================================================== PHPBench 0.8.1 PHP Benchmark Suite Score > Higher Is Better 1 . 632716 |=================================================================== 2 . 633421 |===================================================================