icelake march

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 23.10 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 2403278-NE-ICELAKEMA14
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Timed Code Compilation 2 Tests
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Multi-Core 10 Tests
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March 26
  4 Hours, 59 Minutes
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March 27
  4 Hours, 48 Minutes
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icelake march 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 23.10 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 + 2 x 0GB MassStorageClass,Toshiba KBG40ZPZ512G NVMe 512GB + 2 x 0GB MassStorageClass 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 23.10,Ubuntu 23.10 Kernel,,6.7.0-060700rc5-generic (x86_64),6.7.0-060700rc5-generic (x86_64) Desktop,,GNOME Shell 45.1,GNOME Shell 45.1 Display Server,,X Server + Wayland,X Server + Wayland OpenGL,,4.6 Mesa 24.0~git2312230600.551924~oibaf~m (git-551924a 2023-12-23 mantic-oibaf-ppa),4.6 Mesa 24.0~git2312230600.551924~oibaf~m (git-551924a 2023-12-23 mantic-oibaf-ppa) Compiler,,GCC 13.2.0,GCC 13.2.0 File-System,,ext4,ext4 Screen Resolution,,1920x1200,1920x1200 ,,"a","b" "PyTorch - Device: CPU - Batch Size: 1 - Model: ResNet-50 (batches/sec)",HIB,20.99,16.72 "PyTorch - Device: CPU - Batch Size: 1 - Model: ResNet-152 (batches/sec)",HIB,8.45,6.48 "PyTorch - Device: CPU - Batch Size: 16 - Model: ResNet-50 (batches/sec)",HIB,8.92,8.66 "PyTorch - Device: CPU - Batch Size: 32 - Model: ResNet-50 (batches/sec)",HIB,8.69,8.68 "PyTorch - Device: CPU - Batch Size: 64 - Model: ResNet-50 (batches/sec)",HIB,8.54,8.74 "PyTorch - Device: CPU - Batch Size: 16 - Model: ResNet-152 (batches/sec)",HIB,3.45,3.59 "PyTorch - Device: CPU - Batch Size: 32 - Model: ResNet-152 (batches/sec)",HIB,3.37,3.55 "PyTorch - Device: CPU - Batch Size: 64 - Model: ResNet-152 (batches/sec)",HIB,3.39,3.56 "PyTorch - Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l (batches/sec)",HIB,4.43,4.73 "PyTorch - Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l (batches/sec)",HIB,2.46,2.59 "PyTorch - Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l (batches/sec)",HIB,2.47,2.58 "PyTorch - Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l (batches/sec)",HIB,2.48,2.59 "OpenVINO - Model: Face Detection FP16 - Device: CPU (FPS)",HIB,0.63,0.67 "OpenVINO - Model: Person Detection FP16 - Device: CPU (FPS)",HIB,7.26,7.94 "OpenVINO - Model: Person Detection FP32 - Device: CPU (FPS)",HIB,7.1,7.47 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (FPS)",HIB,51.59,54.06 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (FPS)",HIB,2.37,2.47 "OpenVINO - Model: Face Detection Retail FP16 - Device: CPU (FPS)",HIB,167.03,181 "OpenVINO - Model: Road Segmentation ADAS FP16 - Device: CPU (FPS)",HIB,26.34,27.27 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (FPS)",HIB,132.9,138.11 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (FPS)",HIB,63.77,67.76 "OpenVINO - Model: Face Detection Retail FP16-INT8 - Device: CPU (FPS)",HIB,383.05,414.42 "OpenVINO - Model: Road Segmentation ADAS FP16-INT8 - Device: CPU (FPS)",HIB,47.36,54.53 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (FPS)",HIB,8.66,9.36 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (FPS)",HIB,236.71,253.97 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (FPS)",HIB,90.92,99.85 "OpenVINO - Model: Noise Suppression Poconet-Like FP16 - Device: CPU (FPS)",HIB,116.52,123.67 "OpenVINO - Model: Handwritten English Recognition FP16 - Device: CPU (FPS)",HIB,32.47,34.59 "OpenVINO - Model: Person Re-Identification Retail FP16 - Device: CPU (FPS)",HIB,98.25,105.03 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (FPS)",HIB,1779.08,1894.37 "OpenVINO - Model: Handwritten English Recognition FP16-INT8 - Device: CPU (FPS)",HIB,36.88,40.23 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (FPS)",HIB,4463.62,4765.04 "SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 4K (FPS)",HIB,0.928,0.952 "SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 4K (FPS)",HIB,7.551,7.569 "SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 4K (FPS)",HIB,24.902,24.624 "SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 4K (FPS)",HIB,25.118,26.134 "SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 1080p (FPS)",HIB,3.643,3.806 "SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 1080p (FPS)",HIB,28.85,31.373 "SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 1080p (FPS)",HIB,184.734,184.085 "SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 1080p (FPS)",HIB,240.46,238.626 "TensorFlow - Device: CPU - Batch Size: 1 - Model: AlexNet (images/sec)",HIB,11.23,11.56 "TensorFlow - Device: CPU - Batch Size: 16 - Model: AlexNet (images/sec)",HIB,39.04,41.38 "TensorFlow - Device: CPU - Batch Size: 32 - Model: AlexNet (images/sec)",HIB,42.93,46.1 "TensorFlow - Device: CPU - Batch Size: 64 - Model: AlexNet (images/sec)",HIB,45.59,48.86 "TensorFlow - Device: CPU - Batch Size: 1 - Model: GoogLeNet (images/sec)",HIB,22.17,23.26 "TensorFlow - Device: CPU - Batch Size: 1 - Model: ResNet-50 (images/sec)",HIB,5.35,6.03 "TensorFlow - Device: CPU - Batch Size: 16 - Model: GoogLeNet (images/sec)",HIB,19.69,21.44 "TensorFlow - Device: CPU - Batch Size: 16 - Model: ResNet-50 (images/sec)",HIB,7.28,7.79 "TensorFlow - Device: CPU - Batch Size: 32 - Model: GoogLeNet (images/sec)",HIB,20.09,21.52 "TensorFlow - Device: CPU - Batch Size: 32 - Model: ResNet-50 (images/sec)",HIB,7.44,7.87 "TensorFlow - Device: CPU - Batch Size: 64 - Model: GoogLeNet (images/sec)",HIB,20.37,21.63 "TensorFlow - Device: CPU - Batch Size: 64 - Model: ResNet-50 (images/sec)",HIB,7.5,7.96 "JPEG-XL libjxl - Input: PNG - Quality: 80 (MP/s)",HIB,8.278,11.053 "JPEG-XL libjxl - Input: PNG - Quality: 90 (MP/s)",HIB,7.543,8.222 "JPEG-XL libjxl - Input: JPEG - Quality: 80 (MP/s)",HIB,8.048,8.662 "JPEG-XL libjxl - Input: JPEG - Quality: 90 (MP/s)",HIB,7.662,8.256 "JPEG-XL libjxl - Input: PNG - Quality: 100 (MP/s)",HIB,2.997,3.169 "JPEG-XL libjxl - Input: JPEG - Quality: 100 (MP/s)",HIB,2.981,3.13 "JPEG-XL Decoding libjxl - CPU Threads: 1 (MP/s)",HIB,57.584,58.041 "JPEG-XL Decoding libjxl - CPU Threads: All (MP/s)",HIB,123.512,130.443 "Stockfish - Chess Benchmark (Nodes/s)",HIB,2762544,2945032 "RocksDB - Test: Overwrite (Op/s)",HIB,487116,541161 "RocksDB - Test: Random Fill (Op/s)",HIB,505433,532197 "RocksDB - Test: Random Read (Op/s)",HIB,9192128,9961527 "RocksDB - Test: Update Random (Op/s)",HIB,186334,199263 "RocksDB - Test: Sequential Fill (Op/s)",HIB,813335,940711 "RocksDB - Test: Random Fill Sync (Op/s)",HIB,1719,1683 "RocksDB - Test: Read While Writing (Op/s)",HIB,500210,542153 "RocksDB - Test: Read Random Write Random (Op/s)",HIB,504091,522145 "Chaos Group V-RAY - Mode: CPU (vsamples)",HIB,3535,3899 "oneDNN - Harness: IP Shapes 1D - Engine: CPU (ms)",LIB,7.08037,6.82639 "oneDNN - Harness: IP Shapes 3D - Engine: CPU (ms)",LIB,5.80745,5.83135 "oneDNN - Harness: Convolution Batch Shapes Auto - Engine: CPU (ms)",LIB,13.7106,12.755 "oneDNN - Harness: Deconvolution Batch shapes_1d - Engine: CPU (ms)",LIB,18.832,16.9829 "oneDNN - Harness: Deconvolution Batch shapes_3d - Engine: CPU (ms)",LIB,13.4544,13.4885 "oneDNN - Harness: Recurrent Neural Network Training - Engine: CPU (ms)",LIB,12553.3,11783.6 "oneDNN - Harness: Recurrent Neural Network Inference - Engine: CPU (ms)",LIB,6399.73,6341.9 "Google Draco - Model: Lion (ms)",LIB,5559,5491 "Google Draco - Model: Church Facade (ms)",LIB,8421,8361 "OpenVINO - Model: Face Detection FP16 - Device: CPU (ms)",LIB,6365.09,5987.53 "OpenVINO - Model: Person Detection FP16 - Device: CPU (ms)",LIB,548.85,502.55 "OpenVINO - Model: Person Detection FP32 - Device: CPU (ms)",LIB,562.29,535.1 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (ms)",LIB,77.47,73.91 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (ms)",LIB,1675.08,1614.49 "OpenVINO - Model: Face Detection Retail FP16 - Device: CPU (ms)",LIB,23.86,22.01 "OpenVINO - Model: Road Segmentation ADAS FP16 - Device: CPU (ms)",LIB,151.61,146.48 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (ms)",LIB,30.02,28.89 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (ms)",LIB,62.62,58.98 "OpenVINO - Model: Face Detection Retail FP16-INT8 - Device: CPU (ms)",LIB,10.38,9.59 "OpenVINO - Model: Road Segmentation ADAS FP16-INT8 - Device: CPU (ms)",LIB,84.34,73.26 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (ms)",LIB,460.93,426.3 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (ms)",LIB,16.82,15.67 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (ms)",LIB,43.9,39.97 "OpenVINO - Model: Noise Suppression Poconet-Like FP16 - Device: CPU (ms)",LIB,34.26,32.26 "OpenVINO - Model: Handwritten English Recognition FP16 - Device: CPU (ms)",LIB,123.07,115.5 "OpenVINO - Model: Person Re-Identification Retail FP16 - Device: CPU (ms)",LIB,40.64,38.02 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (ms)",LIB,2.2,2.07 "OpenVINO - Model: Handwritten English Recognition FP16-INT8 - Device: CPU (ms)",LIB,108.37,99.37 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (ms)",LIB,0.86,0.81 "Timed Linux Kernel Compilation - Build: defconfig (sec)",LIB,496.16,497.181 "Timed Mesa Compilation - Time To Compile (sec)",LIB,143.674,143.249 "Parallel BZIP2 Compression - FreeBSD-13.0-RELEASE-amd64-memstick.img Compression (sec)",LIB,28.581235,28.096278 "Primesieve - Length: 1e12 (sec)",LIB,88.592,84.583 "Blender - Blend File: BMW27 - Compute: CPU-Only (sec)",LIB,671.9,633.72 "Blender - Blend File: Junkshop - Compute: CPU-Only (sec)",LIB,921.09,892.47 "Blender - Blend File: Fishy Cat - Compute: CPU-Only (sec)",LIB,848.45,806.43 "Blender - Blend File: Pabellon Barcelona - Compute: CPU-Only (sec)",LIB,2207.35,2092.75 "WavPack Audio Encoding - WAV To WavPack (sec)",LIB,17.657,14.432