christmas comet

Intel Core i7-10700T testing with a Logic Supply RXM-181 (Z01-0002A026 BIOS) and Intel UHD 630 CML GT2 30GB on Ubuntu 22.04 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 2212231-NE-CHRISTMAS95
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AV1 2 Tests
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Creator Workloads 7 Tests
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HPC - High Performance Computing 5 Tests
Machine Learning 4 Tests
Multi-Core 7 Tests
Intel oneAPI 3 Tests
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Server CPU Tests 6 Tests
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a
December 22 2022
  6 Hours, 25 Minutes
b
December 22 2022
  6 Hours, 23 Minutes
c
December 23 2022
  6 Hours, 23 Minutes
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  6 Hours, 24 Minutes

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christmas comet Intel Core i7-10700T testing with a Logic Supply RXM-181 (Z01-0002A026 BIOS) and Intel UHD 630 CML GT2 30GB on Ubuntu 22.04 via the Phoronix Test Suite. ,,"a","b","c" Processor,,Intel Core i7-10700T @ 4.50GHz (8 Cores / 16 Threads),Intel Core i7-10700T @ 4.50GHz (8 Cores / 16 Threads),Intel Core i7-10700T @ 4.50GHz (8 Cores / 16 Threads) Motherboard,,Logic Supply RXM-181 (Z01-0002A026 BIOS),Logic Supply RXM-181 (Z01-0002A026 BIOS),Logic Supply RXM-181 (Z01-0002A026 BIOS) Chipset,,Intel Comet Lake PCH,Intel Comet Lake PCH,Intel Comet Lake PCH Memory,,32GB,32GB,32GB Disk,,256GB TS256GMTS800,256GB TS256GMTS800,256GB TS256GMTS800 Graphics,,Intel UHD 630 CML GT2 30GB (1200MHz),Intel UHD 630 CML GT2 30GB (1200MHz),Intel UHD 630 CML GT2 30GB (1200MHz) Audio,,Realtek ALC233,Realtek ALC233,Realtek ALC233 Monitor,,DELL P2415Q,DELL P2415Q,DELL P2415Q Network,,Intel I219-LM + Intel I210,Intel I219-LM + Intel I210,Intel I219-LM + Intel I210 OS,,Ubuntu 22.04,Ubuntu 22.04,Ubuntu 22.04 Kernel,,5.15.0-52-generic (x86_64),5.15.0-52-generic (x86_64),5.15.0-52-generic (x86_64) Desktop,,GNOME Shell 42.2,GNOME Shell 42.2,GNOME Shell 42.2 Display Server,,X Server + Wayland,X Server + Wayland,X Server + Wayland OpenGL,,4.6 Mesa 22.0.1,4.6 Mesa 22.0.1,4.6 Mesa 22.0.1 OpenCL,,OpenCL 3.0,OpenCL 3.0,OpenCL 3.0 Vulkan,,1.3.204,1.3.204,1.3.204 Compiler,,GCC 11.3.0,GCC 11.3.0,GCC 11.3.0 File-System,,ext4,ext4,ext4 Screen Resolution,,1920x1080,1920x1080,1920x1080 ,,"a","b","c" "FluidX3D - Test: FP32-FP32 (MLUPs/s)",HIB,201,201,200 "FluidX3D - Test: FP32-FP16C (MLUPs/s)",HIB,173,175,175 "FluidX3D - Test: FP32-FP16S (MLUPs/s)",HIB,357,377,381 "nekRS - Input: TurboPipe Periodic (FLOP/s)",HIB,25130200000,25286500000,25386800000 "rav1e - Speed: 1 (FPS)",HIB,0.413,0.417,0.416 "rav1e - Speed: 5 (FPS)",HIB,2.238,2.269,2.284 "rav1e - Speed: 6 (FPS)",HIB,3.125,3.163,3.155 "rav1e - Speed: 10 (FPS)",HIB,8.33,8.621,8.654 "SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 4K (FPS)",HIB,1.229,1.247,1.247 "SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 4K (FPS)",HIB,16.499,16.693,16.673 "SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 4K (FPS)",HIB,73.506,75.452,74.668 "SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 4K (FPS)",HIB,79.105,80.31,80.973 "SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 1080p (FPS)",HIB,4.392,4.433,4.446 "SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 1080p (FPS)",HIB,51.631,53.451,53.145 "SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 1080p (FPS)",HIB,323.225,324.322,328.002 "SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 1080p (FPS)",HIB,356.085,356.327,364.038 "OpenVKL - Benchmark: vklBenchmark ISPC (Items / Sec)",HIB,93,94,94 "OpenVKL - Benchmark: vklBenchmark Scalar (Items / Sec)",HIB,46,46,46 "Stargate Digital Audio Workstation - Sample Rate: 44100 - Buffer Size: 512 (Render Ratio)",HIB,1.69939,1.717714,1.704004 "Stargate Digital Audio Workstation - Sample Rate: 96000 - Buffer Size: 512 (Render Ratio)",HIB,1.204979,1.206529,1.206909 "Stargate Digital Audio Workstation - Sample Rate: 192000 - Buffer Size: 512 (Render Ratio)",HIB,0.791489,0.794756,0.792159 "Stargate Digital Audio Workstation - Sample Rate: 44100 - Buffer Size: 1024 (Render Ratio)",HIB,1.798704,1.802469,1.802314 "Stargate Digital Audio Workstation - Sample Rate: 480000 - Buffer Size: 512 (Render Ratio)",HIB,1.638868,1.651464,1.649194 "Stargate Digital Audio Workstation - Sample Rate: 96000 - Buffer Size: 1024 (Render Ratio)",HIB,1.293792,1.29592,1.295966 "Stargate Digital Audio Workstation - Sample Rate: 192000 - Buffer Size: 1024 (Render Ratio)",HIB,0.867297,0.870159,0.870579 "Stargate Digital Audio Workstation - Sample Rate: 480000 - Buffer Size: 1024 (Render Ratio)",HIB,1.731054,1.733011,1.737779 "Timed Linux Kernel Compilation - Build: defconfig (sec)",LIB,190.421,189.627,189.634 "Timed Linux Kernel Compilation - Build: allmodconfig (sec)",LIB,2642.392,2642.66,2688.942 "oneDNN - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU (ms)",LIB,5.11104,5.03425,67.4107 "oneDNN - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU (ms)",LIB,10.8363,10.8813,47.6781 "oneDNN - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,2.37749,2.26968,40.0963 "oneDNN - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,2.48501,2.47157,51.5073 "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,17.1397,17.1202,17.2961 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU (ms)",LIB,11.6929,12.5886,11.7109 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU (ms)",LIB,8.30947,8.38301,8.47927 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,15.5046,15.5676,15.4711 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,3.80985,3.77816,3.78238 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,5.02536,5.05109,5.15364 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU (ms)",LIB,6850.49,6861.12,6832.22 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU (ms)",LIB,3625.06,3632.36,3629.54 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,6878.39,6904.88,6850.47 "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,3622.8,3783.41,3612.19 "oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU (ms)",LIB,3.99763,4.01087,3.99005 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,6869.52,6897.51,6842.96 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,3640.28,3657.32,3636.49 "oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,2.23303,2.2057,2.14039 "oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,,, "CockroachDB - Workload: MoVR - Concurrency: 128 (ops/s)",HIB,174.4,175.2,173.1 "CockroachDB - Workload: MoVR - Concurrency: 256 (ops/s)",HIB,174.8,175.2,167 "CockroachDB - Workload: MoVR - Concurrency: 512 (ops/s)",HIB,173.9,172.1,173.1 "CockroachDB - Workload: MoVR - Concurrency: 1024 (ops/s)",HIB,173.6,171.6,168.9 "CockroachDB - Workload: KV, 10% Reads - Concurrency: 128 (ops/s)",HIB,12318.5,12364.9,12422.9 "CockroachDB - Workload: KV, 10% Reads - Concurrency: 256 (ops/s)",HIB,21256.3,21051.4,21050.4 "CockroachDB - Workload: KV, 10% Reads - Concurrency: 512 (ops/s)",HIB,22167.5,22053.8,22080.3 "CockroachDB - Workload: KV, 50% Reads - Concurrency: 128 (ops/s)",HIB,21728.4,21711.8,21625.8 "CockroachDB - Workload: KV, 50% Reads - Concurrency: 256 (ops/s)",HIB,25273.3,25267.5,25133.1 "CockroachDB - Workload: KV, 50% Reads - Concurrency: 512 (ops/s)",HIB,25227.2,25164.2,25045.3 "CockroachDB - Workload: KV, 60% Reads - Concurrency: 128 (ops/s)",HIB,24369.6,24360.3,24267.8 "CockroachDB - Workload: KV, 60% Reads - Concurrency: 256 (ops/s)",HIB,26268.4,26246.9,26188 "CockroachDB - Workload: KV, 60% Reads - Concurrency: 512 (ops/s)",HIB,25923.7,25936.5,26058.7 "CockroachDB - Workload: KV, 95% Reads - Concurrency: 128 (ops/s)",HIB,31139.2,30808.9,30644.9 "CockroachDB - Workload: KV, 95% Reads - Concurrency: 256 (ops/s)",HIB,30543,30259.7,30253.2 "CockroachDB - Workload: KV, 95% Reads - Concurrency: 512 (ops/s)",HIB,29621.9,29633.5,29572.8 "CockroachDB - Workload: KV, 10% Reads - Concurrency: 1024 (ops/s)",HIB,21256.4,21125.9,21160.8 "CockroachDB - Workload: KV, 50% Reads - Concurrency: 1024 (ops/s)",HIB,23641.5,23487.2,23596 "CockroachDB - Workload: KV, 60% Reads - Concurrency: 1024 (ops/s)",HIB,24536.7,24424.9,24231 "CockroachDB - Workload: KV, 95% Reads - Concurrency: 1024 (ops/s)",HIB,28002.8,27948,27834.5 "Blender - Blend File: BMW27 - Compute: CPU-Only (sec)",LIB,306.09,305.77,307.76 "Blender - Blend File: Classroom - Compute: CPU-Only (sec)",LIB,902.53,904.16,905.71 "Blender - Blend File: Fishy Cat - Compute: CPU-Only (sec)",LIB,417.83,417.45,417.82 "Blender - Blend File: Barbershop - Compute: CPU-Only (sec)",LIB,3260.14,3257.91,3234.94 "Blender - Blend File: Pabellon Barcelona - Compute: CPU-Only (sec)",LIB,1083.6,1086.78,1083.92 "OpenVINO - Model: Face Detection FP16 - Device: CPU (FPS)",HIB,1.37,1.37,1.38 "OpenVINO - Model: Face Detection FP16 - Device: CPU (ms)",LIB,2907.95,2900.79,2889.56 "OpenVINO - Model: Person Detection FP16 - Device: CPU (FPS)",HIB,0.86,0.86,0.86 "OpenVINO - Model: Person Detection FP16 - Device: CPU (ms)",LIB,4564.3,4567.98,4555.58 "OpenVINO - Model: Person Detection FP32 - Device: CPU (FPS)",HIB,0.85,0.85,0.85 "OpenVINO - Model: Person Detection FP32 - Device: CPU (ms)",LIB,4627.4,4705.67,4632.94 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (FPS)",HIB,105.44,104.28,104.85 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (ms)",LIB,37.9,38.33,38.12 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (FPS)",HIB,2.57,2.57,2.58 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (ms)",LIB,1552.62,1555.24,1548.9 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (FPS)",HIB,164.79,164.14,164.12 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (ms)",LIB,24.25,24.35,24.35 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (FPS)",HIB,124.59,124.97,125.45 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (ms)",LIB,32.08,31.98,31.85 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (FPS)",HIB,14.9,14.92,14.93 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (ms)",LIB,268.31,267.76,267.65 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (FPS)",HIB,260.1,258.24,257.58 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (ms)",LIB,30.73,30.96,31.03 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (FPS)",HIB,181.05,177.44,174.87 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (ms)",LIB,22.07,22.52,22.85 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (FPS)",HIB,3609.48,3595.44,3620.42 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (ms)",LIB,2.2,2.21,2.2 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (FPS)",HIB,3914.1,3925.52,3970.02 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (ms)",LIB,2.03,2.03,2 "Numenta Anomaly Benchmark - Detector: KNN CAD (sec)",LIB,364.397,364.876,360.585 "Numenta Anomaly Benchmark - Detector: Relative Entropy (sec)",LIB,32.183,32.448,34.335 "Numenta Anomaly Benchmark - Detector: Windowed Gaussian (sec)",LIB,18.272,18.344,18.648 "Numenta Anomaly Benchmark - Detector: Earthgecko Skyline (sec)",LIB,200.783,193.302,201.311 "Numenta Anomaly Benchmark - Detector: Bayesian Changepoint (sec)",LIB,56.706,60.654,60.22 "Numenta Anomaly Benchmark - Detector: Contextual Anomaly Detector OSE (sec)",LIB,69.885,70.238,69.625 "Scikit-Learn - Benchmark: MNIST Dataset (sec)",LIB,251.412,252.299,251.815 "Scikit-Learn - Benchmark: TSNE MNIST Dataset (sec)",LIB,109.345,108.375,110.002 "Scikit-Learn - Benchmark: Sparse Random Projections, 100 Iterations (sec)",LIB,3294.519,3294.87,3302.545