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.
Compare your own system(s) to this result file with the
Phoronix Test Suite by running the command:
phoronix-test-suite benchmark 2203303-NE-10700TCOM78
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