10700t comet weds

Intel Core i7-10700T testing with a Logic Supply RXM-181 (Z01-0002A026 BIOS) and Intel UHD 630 CML GT2 3GB on Ubuntu 21.10 via the Phoronix Test Suite.

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March 30 2022
  1 Hour, 37 Minutes
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March 30 2022
  26 Minutes
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March 30 2022
  18 Minutes
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10700t comet weds Intel Core i7-10700T testing with a Logic Supply RXM-181 (Z01-0002A026 BIOS) and Intel UHD 630 CML GT2 3GB on Ubuntu 21.10 via the Phoronix Test Suite. A: Processor: Intel Core i7-10700T @ 4.50GHz (8 Cores / 16 Threads), Motherboard: Logic Supply RXM-181 (Z01-0002A026 BIOS), Chipset: Intel Comet Lake PCH, Memory: 32GB, Disk: 256GB TS256GMTS800, Graphics: Intel UHD 630 CML GT2 3GB (1200MHz), Audio: Realtek ALC233, Monitor: DELL P2415Q, Network: Intel I219-LM + Intel I210 OS: Ubuntu 21.10, Kernel: 5.13.0-35-generic (x86_64), Desktop: GNOME Shell 40.5, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 21.2.2, Vulkan: 1.2.182, Compiler: GCC 11.2.0, File-System: ext4, Screen Resolution: 1920x1080 B: Processor: Intel Core i7-10700T @ 4.50GHz (8 Cores / 16 Threads), Motherboard: Logic Supply RXM-181 (Z01-0002A026 BIOS), Chipset: Intel Comet Lake PCH, Memory: 32GB, Disk: 256GB TS256GMTS800, Graphics: Intel UHD 630 CML GT2 3GB (1200MHz), Audio: Realtek ALC233, Monitor: DELL P2415Q, Network: Intel I219-LM + Intel I210 OS: Ubuntu 21.10, Kernel: 5.13.0-35-generic (x86_64), Desktop: GNOME Shell 40.5, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 21.2.2, Vulkan: 1.2.182, Compiler: GCC 11.2.0, File-System: ext4, Screen Resolution: 1920x1080 C: Processor: Intel Core i7-10700T @ 4.50GHz (8 Cores / 16 Threads), Motherboard: Logic Supply RXM-181 (Z01-0002A026 BIOS), Chipset: Intel Comet Lake PCH, Memory: 32GB, Disk: 256GB TS256GMTS800, Graphics: Intel UHD 630 CML GT2 3GB (1200MHz), Audio: Realtek ALC233, Monitor: DELL P2415Q, Network: Intel I219-LM + Intel I210 OS: Ubuntu 21.10, Kernel: 5.13.0-35-generic (x86_64), Desktop: GNOME Shell 40.5, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 21.2.2, Vulkan: 1.2.182, Compiler: GCC 11.2.0, File-System: ext4, Screen Resolution: 1920x1080 ONNX Runtime 1.11 Model: fcn-resnet101-11 - Device: CPU Inferences Per Minute > Higher Is Better A . 39 |======================================================================= B . 39 |======================================================================= ONNX Runtime 1.11 Model: bertsquad-12 - Device: CPU Inferences Per Minute > Higher Is Better A . 303 |===================================================================== B . 306 |====================================================================== ONNX Runtime 1.11 Model: ArcFace ResNet-100 - Device: CPU Inferences Per Minute > Higher Is Better A . 694 |===================================================================== B . 703 |====================================================================== ONNX Runtime 1.11 Model: super-resolution-10 - Device: CPU Inferences Per Minute > Higher Is Better A . 2167 |==================================================================== B . 2186 |===================================================================== ONNX Runtime 1.11 Model: GPT-2 - Device: CPU Inferences Per Minute > Higher Is Better A . 3644 |==================================================================== B . 3713 |===================================================================== C . 3711 |===================================================================== ONNX Runtime 1.11 Model: yolov4 - Device: CPU Inferences Per Minute > Higher Is Better A . 203 |====================================================================== B . 203 |====================================================================== C . 204 |====================================================================== perf-bench Benchmark: Epoll Wait ops/sec > Higher Is Better A . 109366 |================================================================ B . 115261 |=================================================================== C . 112104 |================================================================= oneDNN 2.6 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better A . 6801.99 |================================================================== B . 6804.00 |================================================================== C . 6790.86 |================================================================== oneDNN 2.6 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 6792.71 |================================================================== B . 6780.39 |================================================================== C . 6786.74 |================================================================== oneDNN 2.6 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 6791.09 |================================================================== B . 6778.99 |================================================================== C . 6787.52 |================================================================== oneDNN 2.6 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better A . 3612.75 |================================================================== B . 3619.68 |================================================================== C . 3600.38 |================================================================== oneDNN 2.6 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 3603.05 |================================================================== B . 3588.14 |================================================================== C . 3594.68 |================================================================== oneDNN 2.6 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 3597.62 |================================================================== B . 3575.32 |================================================================== C . 3590.54 |================================================================== oneDNN 2.6 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 14.09 |==================================================================== B . 11.04 |===================================================== C . 12.90 |============================================================== oneDNN 2.6 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 6.31917 |================================================================== B . 4.19472 |============================================ C . 4.18294 |============================================ oneDNN 2.6 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 2.80373 |================================================================== B . 2.20050 |==================================================== C . 2.26074 |===================================================== perf-bench Benchmark: Futex Hash ops/sec > Higher Is Better A . 3559455 |================================================================ B . 3644833 |================================================================= C . 3674250 |================================================================== perf-bench Benchmark: Sched Pipe ops/sec > Higher Is Better A . 163024 |=================================================================== B . 161604 |================================================================== C . 161030 |================================================================== perf-bench Benchmark: Futex Lock-Pi ops/sec > Higher Is Better A . 843 |====================================================================== B . 845 |====================================================================== C . 848 |====================================================================== oneDNN 2.6 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 2.62391 |================================================================== B . 2.48963 |=============================================================== C . 2.43947 |============================================================= oneDNN 2.6 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 3.87824 |================================================================== B . 3.76516 |================================================================ C . 3.76013 |================================================================ perf-bench Benchmark: Memcpy 1MB GB/sec > Higher Is Better A . 26.84 |==================================================================== B . 26.80 |==================================================================== C . 25.62 |================================================================= oneDNN 2.6 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 3.98170 |================================================================== B . 3.96065 |================================================================== C . 3.94494 |================================================================= oneDNN 2.6 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 2.26038 |================================================================= B . 2.30454 |================================================================== C . 2.24351 |================================================================ perf-bench Benchmark: Memset 1MB GB/sec > Higher Is Better A . 43.09 |==================================================================== B . 43.23 |==================================================================== C . 41.20 |================================================================= oneDNN 2.6 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 9.22489 |================================================================== B . 8.17558 |========================================================== C . 8.25954 |=========================================================== oneDNN 2.6 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 5.46541 |================================================================== B . 4.65868 |======================================================== C . 4.60998 |======================================================== oneDNN 2.6 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 9.74806 |================================================================== B . 9.64795 |================================================================= C . 9.36624 |=============================================================== perf-bench Benchmark: Syscall Basic ops/sec > Higher Is Better A . 14180008 |================================================================= B . 14188609 |================================================================= C . 14102641 |================================================================= oneDNN 2.6 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 15.53 |==================================================================== B . 15.58 |==================================================================== C . 15.45 |=================================================================== oneDNN 2.6 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 17.02 |==================================================================== B . 17.07 |==================================================================== C . 17.02 |==================================================================== oneDNN 2.6 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.6 Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.6 Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.6 Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.6 Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.6 Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better