icelake 2023

Tests for a future article. Intel Core i7-1065G7 testing with a Dell 06CDVY (1.0.9 BIOS) and Intel Iris Plus ICL GT2 16GB on Ubuntu 23.04 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 2310252-NE-ICELAKE2057
Jump To Table - Results

View

Do Not Show Noisy Results
Do Not Show Results With Incomplete Data
Do Not Show Results With Little Change/Spread
List Notable Results

Limit displaying results to tests within:

AV1 3 Tests
C/C++ Compiler Tests 2 Tests
CPU Massive 3 Tests
Creator Workloads 6 Tests
Encoding 3 Tests
HPC - High Performance Computing 3 Tests
Machine Learning 2 Tests
Multi-Core 6 Tests
Intel oneAPI 3 Tests
Server CPU Tests 3 Tests
Video Encoding 3 Tests

Statistics

Show Overall Harmonic Mean(s)
Show Overall Geometric Mean
Show Geometric Means Per-Suite/Category
Show Wins / Losses Counts (Pie Chart)
Normalize Results
Remove Outliers Before Calculating Averages

Graph Settings

Force Line Graphs Where Applicable
Convert To Scalar Where Applicable
Prefer Vertical Bar Graphs

Multi-Way Comparison

Condense Multi-Option Tests Into Single Result Graphs

Table

Show Detailed System Result Table

Run Management

Highlight
Result
Hide
Result
Result
Identifier
View Logs
Performance Per
Dollar
Date
Run
  Test
  Duration
a
October 24 2023
  5 Hours, 52 Minutes
b
October 24 2023
  5 Hours, 43 Minutes
Invert Hiding All Results Option
  5 Hours, 48 Minutes
Only show results matching title/arguments (delimit multiple options with a comma):
Do not show results matching title/arguments (delimit multiple options with a comma):

icelake 2023 Suite 1.0.0 System Test suite extracted from icelake 2023. pts/onednn-3.3.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU pts/ncnn-1.5.0 -1 Target: CPU - Model: shufflenet-v2 pts/ncnn-1.5.0 Target: Vulkan GPU - Model: mnasnet pts/ncnn-1.5.0 -1 Target: CPU - Model: mnasnet pts/onednn-3.3.0 --ip --batch=inputs/ip/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/oidn-2.1.0 -r RTLightmap.hdr.4096x4096 -d cpu Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only pts/onednn-3.3.0 --ip --batch=inputs/ip/shapes_1d --cfg=f32 --engine=cpu Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU pts/onednn-3.3.0 --ip --batch=inputs/ip/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU pts/ncnn-1.5.0 Target: Vulkan GPU - Model: FastestDet pts/onednn-3.3.0 --conv --batch=inputs/conv/shapes_auto --cfg=u8s8f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/svt-av1-2.10.0 --preset 12 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 12 - Input: Bosphorus 4K pts/onednn-3.3.0 --ip --batch=inputs/ip/shapes_3d --cfg=f32 --engine=cpu Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU pts/svt-av1-2.10.0 --preset 8 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 8 - Input: Bosphorus 1080p pts/onednn-3.3.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU pts/oidn-2.1.0 -r RT.ldr_alb_nrm.3840x2160 -d cpu Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only pts/oidn-2.1.0 -r RT.hdr_alb_nrm.3840x2160 -d cpu Run: RT.hdr_alb_nrm.3840x2160 - Device: CPU-Only pts/onednn-3.3.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.3.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.3.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/onednn-3.3.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/ncnn-1.5.0 Target: Vulkan GPU - Model: resnet18 pts/onednn-3.3.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/ncnn-1.5.0 -1 Target: CPU - Model: resnet18 pts/svt-av1-2.10.0 --preset 8 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 8 - Input: Bosphorus 4K pts/svt-av1-2.10.0 --preset 4 -n 160 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 4 - Input: Bosphorus 1080p pts/stress-ng-1.11.0 --vecwide -1 --no-rand-seed Test: Wide Vector Math pts/stress-ng-1.11.0 --clone -1 --no-rand-seed Test: Cloning pts/ncnn-1.5.0 -1 Target: CPU - Model: blazeface pts/avifenc-1.4.0 -s 10 -l Encoder Speed: 10, Lossless pts/avifenc-1.4.0 -s 6 Encoder Speed: 6 pts/ncnn-1.5.0 Target: Vulkan GPU - Model: alexnet pts/ncnn-1.5.0 -1 Target: CPU - Model: resnet50 pts/ncnn-1.5.0 Target: Vulkan GPU - Model: blazeface pts/avifenc-1.4.0 -s 6 -l Encoder Speed: 6, Lossless pts/svt-av1-2.10.0 --preset 13 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 13 - Input: Bosphorus 4K pts/avifenc-1.4.0 -s 2 Encoder Speed: 2 pts/ncnn-1.5.0 Target: Vulkan GPU - Model: vgg16 pts/avifenc-1.4.0 -s 0 Encoder Speed: 0 pts/ncnn-1.5.0 -1 Target: CPU - Model: vgg16 pts/ncnn-1.5.0 -1 Target: CPU - Model: alexnet pts/stress-ng-1.11.0 --vecshuf -1 --no-rand-seed Test: Vector Shuffle pts/ncnn-1.5.0 -1 Target: CPU - Model: googlenet pts/ncnn-1.5.0 Target: Vulkan GPU - Model: vision_transformer pts/ncnn-1.5.0 Target: Vulkan GPU - Model: googlenet pts/ncnn-1.5.0 Target: Vulkan GPU - Model: efficientnet-b0 pts/ncnn-1.5.0 -1 Target: CPU - Model: regnety_400m pts/ncnn-1.5.0 Target: Vulkan GPU - Model: resnet50 pts/ncnn-1.5.0 Target: Vulkan GPU - Model: squeezenet_ssd pts/onednn-3.3.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU pts/ncnn-1.5.0 Target: Vulkan GPU - Model: mobilenet pts/svt-av1-2.10.0 --preset 4 -n 160 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 4 - Input: Bosphorus 4K pts/ncnn-1.5.0 Target: Vulkan GPU - Model: regnety_400m pts/ncnn-1.5.0 -1 Target: CPU - Model: mobilenet pts/ncnn-1.5.0 -1 Target: CPU - Model: yolov4-tiny pts/ncnn-1.5.0 Target: Vulkan GPU - Model: yolov4-tiny pts/ncnn-1.5.0 -1 Target: CPU - Model: squeezenet_ssd pts/ncnn-1.5.0 -1 Target: CPU - Model: vision_transformer pts/ncnn-1.5.0 -1 Target: CPU - Model: efficientnet-b0 pts/embree-1.6.0 pathtracer_ispc -c asian_dragon/asian_dragon.ecs Binary: Pathtracer ISPC - Model: Asian Dragon pts/openradioss-1.1.1 BIRD_WINDSHIELD_v1_0000.rad BIRD_WINDSHIELD_v1_0001.rad Model: Bird Strike on Windshield pts/embree-1.6.0 pathtracer_ispc -c asian_dragon_obj/asian_dragon.ecs Binary: Pathtracer ISPC - Model: Asian Dragon Obj pts/embree-1.6.0 pathtracer -c crown/crown.ecs Binary: Pathtracer - Model: Crown pts/ncnn-1.5.0 Target: Vulkan GPU - Model: shufflenet-v2 pts/ncnn-1.5.0 Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2 pts/fluidx3d-1.2.0 FP32-FP32 Test: FP32-FP32 pts/aom-av1-3.7.0 --cpu-used=10 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 10 Realtime - Input: Bosphorus 4K pts/ncnn-1.5.0 -1 Target: CPU-v2-v2 - Model: mobilenet-v2 pts/ncnn-1.5.0 -1 Target: CPU - Model: FastestDet pts/quantlib-1.2.0 --mp Configuration: Multi-Threaded pts/embree-1.6.0 pathtracer -c asian_dragon_obj/asian_dragon.ecs Binary: Pathtracer - Model: Asian Dragon Obj pts/ncnn-1.5.0 Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3 pts/svt-av1-2.10.0 --preset 13 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 13 - Input: Bosphorus 1080p pts/openradioss-1.1.1 NEON1M11_0000.rad NEON1M11_0001.rad Model: Chrysler Neon 1M pts/embree-1.6.0 pathtracer_ispc -c crown/crown.ecs Binary: Pathtracer ISPC - Model: Crown pts/openradioss-1.1.1 Bumper_Beam_AP_meshed_0000.rad Bumper_Beam_AP_meshed_0001.rad Model: Bumper Beam pts/fluidx3d-1.2.0 FP32-FP16S Test: FP32-FP16S pts/aom-av1-3.7.0 --cpu-used=10 --rt Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 10 Realtime - Input: Bosphorus 1080p pts/onednn-3.3.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU pts/fluidx3d-1.2.0 FP32-FP16C Test: FP32-FP16C pts/svt-av1-2.10.0 --preset 12 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 12 - Input: Bosphorus 1080p pts/openradioss-1.1.1 fsi_drop_container_0000.rad fsi_drop_container_0001.rad Model: INIVOL and Fluid Structure Interaction Drop Container pts/aom-av1-3.7.0 --cpu-used=11 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 11 Realtime - Input: Bosphorus 4K pts/openradioss-1.1.1 Cell_Phone_Drop_0000.rad Cell_Phone_Drop_0001.rad Model: Cell Phone Drop Test pts/easywave-1.0.0 -grid examples/e2Asean.grd -source examples/BengkuluSept2007.flt -time 240 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 240 pts/onednn-3.3.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU pts/aom-av1-3.7.0 --cpu-used=9 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 9 Realtime - Input: Bosphorus 4K pts/openradioss-1.1.1 RUBBER_SEAL_IMPDISP_GEOM_0000.rad RUBBER_SEAL_IMPDISP_GEOM_0001.rad Model: Rubber O-Ring Seal Installation pts/quantlib-1.2.0 Configuration: Single-Threaded pts/aom-av1-3.7.0 --cpu-used=11 --rt Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 11 Realtime - Input: Bosphorus 1080p pts/aom-av1-3.7.0 --cpu-used=9 --rt Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 9 Realtime - Input: Bosphorus 1080p pts/embree-1.6.0 pathtracer -c asian_dragon/asian_dragon.ecs Binary: Pathtracer - Model: Asian Dragon pts/easywave-1.0.0 -grid examples/e2Asean.grd -source examples/BengkuluSept2007.flt -time 2400 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 2400 pts/easywave-1.0.0 -grid examples/e2Asean.grd -source examples/BengkuluSept2007.flt -time 1200 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 1200 pts/onednn-3.3.0 --conv --batch=inputs/conv/shapes_auto --cfg=f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/ncnn-1.5.0 -1 Target: CPU-v3-v3 - Model: mobilenet-v3 pts/stress-ng-1.11.0 --vnni -1 Test: AVX-512 VNNI pts/onednn-3.3.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.3.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.3.0 --conv --batch=inputs/conv/shapes_auto --cfg=bf16bf16bf16 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.3.0 --ip --batch=inputs/ip/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.3.0 --ip --batch=inputs/ip/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU