Xeon E3 1245 December v5 Intel Xeon E3-1245 v5 testing with a MSI C236A WORKSTATION (MS-7998) v1.0 (2.90 BIOS) and MSI Intel HD P530 3GB on Ubuntu 20.04 via the Phoronix Test Suite. 1: Processor: Intel Xeon E3-1245 v5 @ 3.90GHz (4 Cores / 8 Threads), Motherboard: MSI C236A WORKSTATION (MS-7998) v1.0 (2.90 BIOS), Chipset: Intel Xeon E3-1200 v5/E3-1500, Memory: 32GB, Disk: 120GB Samsung SSD 850, Graphics: MSI Intel HD P530 3GB (1150MHz), Audio: Realtek ALC1150, Monitor: LG Ultra HD, Network: Intel I219-V OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc7daily20201003-generic (x86_64) 20201002, 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, Vulkan: 1.2.131, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 3840x2160 2: Processor: Intel Xeon E3-1245 v5 @ 3.90GHz (4 Cores / 8 Threads), Motherboard: MSI C236A WORKSTATION (MS-7998) v1.0 (2.90 BIOS), Chipset: Intel Xeon E3-1200 v5/E3-1500, Memory: 32GB, Disk: 120GB Samsung SSD 850, Graphics: MSI Intel HD P530 3GB (1150MHz), Audio: Realtek ALC1150, Monitor: LG Ultra HD, Network: Intel I219-V OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc7daily20201003-generic (x86_64) 20201002, 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, Vulkan: 1.2.131, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 3840x2160 3: Processor: Intel Xeon E3-1245 v5 @ 3.90GHz (4 Cores / 8 Threads), Motherboard: MSI C236A WORKSTATION (MS-7998) v1.0 (2.90 BIOS), Chipset: Intel Xeon E3-1200 v5/E3-1500, Memory: 32GB, Disk: 120GB Samsung SSD 850, Graphics: MSI Intel HD P530 3GB (1150MHz), Audio: Realtek ALC1150, Monitor: LG Ultra HD, Network: Intel I219-V OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc7daily20201003-generic (x86_64) 20201002, 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, Vulkan: 1.2.131, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 3840x2160 VkFFT 1.1.1 Benchmark Score > Higher Is Better 1 . 1403 |===================================================================== 2 . 1404 |===================================================================== 3 . 1405 |===================================================================== Betsy GPU Compressor 1.1 Beta Codec: ETC1 - Quality: Highest Seconds < Lower Is Better 1 . 11.29 |==================================================================== 2 . 11.05 |=================================================================== 3 . 11.00 |================================================================== Betsy GPU Compressor 1.1 Beta Codec: ETC2 RGB - Quality: Highest Seconds < Lower Is Better 1 . 7.187 |==================================================================== VkResample 1.0 Upscale: 2x - Precision: Double ms < Lower Is Better 1 . 998.70 |================================================================== 2 . 1001.14 |================================================================== 3 . 998.82 |================================================================== VkResample 1.0 Upscale: 2x - Precision: Single ms < Lower Is Better 1 . 431.80 |=================================================================== 2 . 430.89 |=================================================================== 3 . 430.67 |=================================================================== Timed HMMer Search 3.3.1 Pfam Database Search Seconds < Lower Is Better 1 . 125.60 |=================================================================== 2 . 125.93 |=================================================================== 3 . 125.73 |=================================================================== Timed MAFFT Alignment 7.471 Multiple Sequence Alignment - LSU RNA Seconds < Lower Is Better 1 . 12.29 |=================================================================== 2 . 12.39 |==================================================================== 3 . 12.32 |==================================================================== simdjson 0.7.1 Throughput Test: Kostya GB/s > Higher Is Better 1 . 0.62 |===================================================================== 2 . 0.61 |==================================================================== 3 . 0.61 |==================================================================== simdjson 0.7.1 Throughput Test: LargeRandom GB/s > Higher Is Better 1 . 0.38 |===================================================================== 2 . 0.38 |===================================================================== 3 . 0.38 |===================================================================== 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 . 8.19338 |================================================================== 2 . 8.19008 |================================================================== 3 . 8.19661 |================================================================== oneDNN 2.0 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 12.05 |================================================================== 2 . 12.40 |==================================================================== 3 . 12.14 |=================================================================== oneDNN 2.0 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3.74565 |================================================================== 2 . 3.74740 |================================================================== 3 . 3.74365 |================================================================== oneDNN 2.0 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3.12413 |================================================================== 2 . 3.13122 |================================================================== 3 . 3.13129 |================================================================== oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 19.93 |=================================================================== 2 . 19.92 |=================================================================== 3 . 20.10 |==================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 10.87 |=================================================================== 2 . 10.96 |==================================================================== 3 . 10.87 |=================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 14.79 |==================================================================== 2 . 14.69 |==================================================================== 3 . 14.70 |==================================================================== oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 19.75 |==================================================================== 2 . 19.72 |==================================================================== 3 . 19.74 |==================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 11.51 |==================================================================== 2 . 11.49 |==================================================================== 3 . 11.51 |==================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 7.55654 |================================================================== 2 . 7.56196 |================================================================== 3 . 7.56918 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 7475.48 |================================================================== 2 . 7493.45 |================================================================== 3 . 7484.24 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 3935.48 |================================================================= 2 . 3968.98 |================================================================== 3 . 3951.34 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 7465.28 |================================================================== 2 . 7469.16 |================================================================== 3 . 7470.61 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3936.93 |================================================================= 2 . 3957.02 |================================================================= 3 . 3997.52 |================================================================== oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 5.16070 |================================================================== 2 . 5.14219 |================================================================== 3 . 5.15452 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 8018.55 |================================================================== 2 . 7485.17 |============================================================== 3 . 7471.28 |============================================================= oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 3936.66 |================================================================== 2 . 3939.66 |================================================================== 3 . 3950.78 |================================================================== oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 7.06309 |================================================================== 2 . 7.05721 |================================================================== 3 . 7.06753 |================================================================== Coremark 1.0 CoreMark Size 666 - Iterations Per Second Iterations/Sec > Higher Is Better 1 . 139999.67 |=============================================================== 2 . 139563.39 |=============================================================== 3 . 141836.10 |================================================================ Timed Clash Compilation Time To Compile Seconds < Lower Is Better 1 . 503.14 |=================================================================== 2 . 502.74 |=================================================================== 3 . 504.48 |=================================================================== Timed FFmpeg Compilation 4.2.2 Time To Compile Seconds < Lower Is Better 1 . 128.18 |=================================================================== 2 . 128.29 |=================================================================== 3 . 128.21 |=================================================================== Build2 0.13 Time To Compile Seconds < Lower Is Better 1 . 278.21 |================================================================== 2 . 277.89 |================================================================= 3 . 284.27 |=================================================================== Node.js V8 Web Tooling Benchmark runs/s > Higher Is Better 1 . 10.43 |=================================================================== 2 . 10.52 |==================================================================== 3 . 10.44 |=================================================================== SQLite Speedtest 3.30 Timed Time - Size 1,000 Seconds < Lower Is Better 1 . 73.62 |==================================================================== 2 . 73.73 |==================================================================== 3 . 73.81 |==================================================================== Apache Siege 2.4.29 Concurrent Users: 10 Transactions Per Second > Higher Is Better 1 . 34682.17 |================================================================= 2 . 34482.76 |================================================================= 3 . 34134.69 |================================================================ Apache Siege 2.4.29 Concurrent Users: 50 Transactions Per Second > Higher Is Better 1 . 37184.02 |================================================================= 2 . 36795.60 |================================================================ 3 . 37055.53 |================================================================= Apache Siege 2.4.29 Concurrent Users: 100 Transactions Per Second > Higher Is Better 1 . 36590.03 |================================================================= 2 . 36203.37 |================================================================ 3 . 35988.65 |================================================================ Apache Siege 2.4.29 Concurrent Users: 200 Transactions Per Second > Higher Is Better 1 . 35475.13 |================================================================= 2 . 34911.49 |================================================================ 3 . 34952.97 |================================================================ Apache Siege 2.4.29 Concurrent Users: 250 Transactions Per Second > Higher Is Better 1 . 35071.79 |================================================================= 2 . 34308.01 |================================================================ 3 . 34647.10 |================================================================ Apache Siege 2.4.29 Concurrent Users: 500 Transactions Per Second > Higher Is Better 1 . 35425.87 |================================================================= 2 . 34441.37 |=============================================================== 3 . 34499.88 |=============================================================== PHPBench 0.8.1 PHP Benchmark Suite Score > Higher Is Better 1 . 651452 |=================================================================== 2 . 648537 |=================================================================== 3 . 651248 |=================================================================== BRL-CAD 7.30.8 VGR Performance Metric VGR Performance Metric > Higher Is Better 1 . 48474 |==================================================================== 2 . 48425 |==================================================================== 3 . 48489 |====================================================================