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 2310250-NE-ICELAKE2082
icelake 2023
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.
,,"a","b"
Processor,,Intel Core i7-1065G7 @ 3.90GHz (4 Cores / 8 Threads),Intel Core i7-1065G7 @ 3.90GHz (4 Cores / 8 Threads)
Motherboard,,Dell 06CDVY (1.0.9 BIOS),Dell 06CDVY (1.0.9 BIOS)
Chipset,,Intel Ice Lake-LP DRAM,Intel Ice Lake-LP DRAM
Memory,,16GB,16GB
Disk,,Toshiba KBG40ZPZ512G NVMe 512GB,Toshiba KBG40ZPZ512G NVMe 512GB
Graphics,,Intel Iris Plus ICL GT2 16GB (1100MHz),Intel Iris Plus ICL GT2 16GB (1100MHz)
Audio,,Realtek ALC289,Realtek ALC289
Network,,Intel Ice Lake-LP PCH CNVi WiFi,Intel Ice Lake-LP PCH CNVi WiFi
OS,,Ubuntu 23.04,Ubuntu 23.04
Kernel,,6.2.0-24-generic (x86_64),6.2.0-24-generic (x86_64)
Desktop,,GNOME Shell 44.0,GNOME Shell 44.0
Display Server,,X Server + Wayland,X Server + Wayland
OpenGL,,4.6 Mesa 23.0.4-0ubuntu1~23.04.1,4.6 Mesa 23.0.4-0ubuntu1~23.04.1
OpenCL,,OpenCL 3.0,OpenCL 3.0
Compiler,,GCC 12.3.0,GCC 12.3.0
File-System,,ext4,ext4
Screen Resolution,,1920x1200,1920x1200
,,"a","b"
"FluidX3D - Test: FP32-FP32 (MLUPs/s)",HIB,257,264
"FluidX3D - Test: FP32-FP16C (MLUPs/s)",HIB,210,209
"FluidX3D - Test: FP32-FP16S (MLUPs/s)",HIB,500,496
"QuantLib - Configuration: Multi-Threaded (MFLOPS)",HIB,7472.8,7336.6
"QuantLib - Configuration: Single-Threaded (MFLOPS)",HIB,2898.3,2894.4
"OpenRadioss - Model: Bumper Beam (sec)",LIB,512.2,516.66
"OpenRadioss - Model: Chrysler Neon 1M (sec)",LIB,2917.08,2947.38
"OpenRadioss - Model: Cell Phone Drop Test (sec)",LIB,337.47,338.36
"OpenRadioss - Model: Bird Strike on Windshield (sec)",LIB,921.19,953.64
"OpenRadioss - Model: Rubber O-Ring Seal Installation (sec)",LIB,693.04,694.01
"OpenRadioss - Model: INIVOL and Fluid Structure Interaction Drop Container (sec)",LIB,1818.21,1824.13
"easyWave - Input: e2Asean Grid + BengkuluSept2007 Source - Time: 240 (sec)",LIB,11.91,11.933
"easyWave - Input: e2Asean Grid + BengkuluSept2007 Source - Time: 1200 (sec)",LIB,225.678,225.755
"easyWave - Input: e2Asean Grid + BengkuluSept2007 Source - Time: 2400 (sec)",LIB,560.794,560.991
"AOM AV1 - Encoder Mode: Speed 9 Realtime - Input: Bosphorus 4K (FPS)",HIB,34.32,34.37
"AOM AV1 - Encoder Mode: Speed 10 Realtime - Input: Bosphorus 4K (FPS)",HIB,34.32,35.13
"AOM AV1 - Encoder Mode: Speed 11 Realtime - Input: Bosphorus 4K (FPS)",HIB,33.21,33.31
"AOM AV1 - Encoder Mode: Speed 9 Realtime - Input: Bosphorus 1080p (FPS)",HIB,146.87,147.01
"AOM AV1 - Encoder Mode: Speed 10 Realtime - Input: Bosphorus 1080p (FPS)",HIB,144.8,143.67
"AOM AV1 - Encoder Mode: Speed 11 Realtime - Input: Bosphorus 1080p (FPS)",HIB,150.75,150.93
"Embree - Binary: Pathtracer - Model: Crown (FPS)",HIB,2.5641,2.6409
"Embree - Binary: Pathtracer ISPC - Model: Crown (FPS)",HIB,2.8093,2.8357
"Embree - Binary: Pathtracer - Model: Asian Dragon (FPS)",HIB,3.2657,3.2676
"Embree - Binary: Pathtracer - Model: Asian Dragon Obj (FPS)",HIB,2.9828,3.035
"Embree - Binary: Pathtracer ISPC - Model: Asian Dragon (FPS)",HIB,3.6724,3.8233
"Embree - Binary: Pathtracer ISPC - Model: Asian Dragon Obj (FPS)",HIB,3.2088,3.3091
"SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 4K (FPS)",HIB,0.727,0.769
"SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 4K (FPS)",HIB,9.095,9.974
"SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 4K (FPS)",HIB,27.939,31.483
"SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 4K (FPS)",HIB,30.093,32.582
"SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 1080p (FPS)",HIB,2.726,2.989
"SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 1080p (FPS)",HIB,25.293,28.357
"SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 1080p (FPS)",HIB,191.279,190.598
"SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 1080p (FPS)",HIB,255.397,252.158
"Intel Open Image Denoise - Run: RT.hdr_alb_nrm.3840x2160 - Device: CPU-Only (Images / Sec)",HIB,0.09,0.10
"Intel Open Image Denoise - Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only (Images / Sec)",HIB,0.09,0.10
"Intel Open Image Denoise - Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only (Images / Sec)",HIB,0.04,0.05
"libavif avifenc - Encoder Speed: 0 (sec)",LIB,581.559,540.786
"libavif avifenc - Encoder Speed: 2 (sec)",LIB,257.08,238.246
"libavif avifenc - Encoder Speed: 6 (sec)",LIB,28.757,26.309
"libavif avifenc - Encoder Speed: 6, Lossless (sec)",LIB,39.912,36.845
"libavif avifenc - Encoder Speed: 10, Lossless (sec)",LIB,13.14,12.02
"oneDNN - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU (ms)",LIB,13.3903,10.7302
"oneDNN - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU (ms)",LIB,9.05715,8.04914
"oneDNN - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,6.00177,4.91146
"oneDNN - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,3.81175,3.0439
"oneDNN - Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,,
"oneDNN - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,,
"oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU (ms)",LIB,13.1328,13.1309
"oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU (ms)",LIB,21.4405,12.9325
"oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU (ms)",LIB,15.1772,15.0825
"oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,17.715,15.4635
"oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,9.48511,8.47414
"oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,8.16222,8.149
"oneDNN - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU (ms)",LIB,14396.7,12976.6
"oneDNN - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU (ms)",LIB,7400.64,6701.8
"oneDNN - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,14373.9,13583.8
"oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,,
"oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,,
"oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,,
"oneDNN - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,7378.4,6648.27
"oneDNN - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,14376.4,12945.5
"oneDNN - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,7398.63,6677.03
"Stress-NG - Test: Cloning (Bogo Ops/s)",HIB,641.54,702.93
"Stress-NG - Test: AVX-512 VNNI ()",,,
"Stress-NG - Test: Vector Shuffle (Bogo Ops/s)",HIB,2156.51,2306.11
"Stress-NG - Test: Wide Vector Math (Bogo Ops/s)",HIB,88229.8,96693.23
"NCNN - Target: CPU - Model: mobilenet (ms)",LIB,24.14,22.94
"NCNN - Target: CPU-v2-v2 - Model: mobilenet-v2 (ms)",LIB,4.37,4.46
"NCNN - Target: CPU-v3-v3 - Model: mobilenet-v3 (ms)",LIB,3.54,3.54
"NCNN - Target: CPU - Model: shufflenet-v2 (ms)",LIB,4.33,2.73
"NCNN - Target: CPU - Model: mnasnet (ms)",LIB,5.83,4.02
"NCNN - Target: CPU - Model: efficientnet-b0 (ms)",LIB,10.72,10.23
"NCNN - Target: CPU - Model: blazeface (ms)",LIB,1.15,1.05
"NCNN - Target: CPU - Model: googlenet (ms)",LIB,17.18,16.11
"NCNN - Target: CPU - Model: vgg16 (ms)",LIB,70.08,65.36
"NCNN - Target: CPU - Model: resnet18 (ms)",LIB,12.98,11.81
"NCNN - Target: CPU - Model: alexnet (ms)",LIB,10.3,9.61
"NCNN - Target: CPU - Model: resnet50 (ms)",LIB,34.19,31.55
"NCNN - Target: CPU - Model: yolov4-tiny (ms)",LIB,32.22,30.62
"NCNN - Target: CPU - Model: squeezenet_ssd (ms)",LIB,13.81,13.15
"NCNN - Target: CPU - Model: regnety_400m (ms)",LIB,11.97,11.26
"NCNN - Target: CPU - Model: vision_transformer (ms)",LIB,267.05,254.37
"NCNN - Target: CPU - Model: FastestDet (ms)",LIB,5.15,5.25
"NCNN - Target: Vulkan GPU - Model: mobilenet (ms)",LIB,24.15,22.83
"NCNN - Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2 (ms)",LIB,4.35,4.47
"NCNN - Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3 (ms)",LIB,3.54,3.48
"NCNN - Target: Vulkan GPU - Model: shufflenet-v2 (ms)",LIB,2.71,2.79
"NCNN - Target: Vulkan GPU - Model: mnasnet (ms)",LIB,5.24,3.6
"NCNN - Target: Vulkan GPU - Model: efficientnet-b0 (ms)",LIB,10.78,10.14
"NCNN - Target: Vulkan GPU - Model: blazeface (ms)",LIB,1.17,1.08
"NCNN - Target: Vulkan GPU - Model: googlenet (ms)",LIB,17.19,16.13
"NCNN - Target: Vulkan GPU - Model: vgg16 (ms)",LIB,70.21,65.22
"NCNN - Target: Vulkan GPU - Model: resnet18 (ms)",LIB,13.06,11.79
"NCNN - Target: Vulkan GPU - Model: alexnet (ms)",LIB,10.38,9.57
"NCNN - Target: Vulkan GPU - Model: resnet50 (ms)",LIB,34.28,32.25
"NCNN - Target: Vulkan GPU - Model: yolov4-tiny (ms)",LIB,32.2,30.66
"NCNN - Target: Vulkan GPU - Model: squeezenet_ssd (ms)",LIB,13.89,13.12
"NCNN - Target: Vulkan GPU - Model: regnety_400m (ms)",LIB,11.95,11.33
"NCNN - Target: Vulkan GPU - Model: vision_transformer (ms)",LIB,268.47,251.88
"NCNN - Target: Vulkan GPU - Model: FastestDet (ms)",LIB,5.64,4.84