10600K pre RKL

Intel Core i5-10600K testing with a ASUS PRIME Z490M-PLUS (1001 BIOS) and ASUS Intel UHD 630 3GB on Ubuntu 20.04 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 2103183-IB-10600KPRE35
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Timed Code Compilation 2 Tests
C/C++ Compiler Tests 3 Tests
CPU Massive 4 Tests
Creator Workloads 6 Tests
Encoding 3 Tests
Game Development 2 Tests
HPC - High Performance Computing 3 Tests
Machine Learning 2 Tests
Multi-Core 7 Tests
Programmer / Developer System Benchmarks 3 Tests
Python Tests 2 Tests
Server CPU Tests 4 Tests
Texture Compression 2 Tests
Video Encoding 3 Tests

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March 18 2021
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March 18 2021
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March 18 2021
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10600K pre RKL Suite 1.0.0 System Test suite extracted from 10600K pre RKL. pts/onednn-1.7.0 --ip --batch=inputs/ip/shapes_3d --cfg=f32 --engine=cpu Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU pts/incompact3d-2.0.2 input_129_nodes.i3d Input: input.i3d 129 Cells Per Direction pts/mnn-1.2.0 Model: SqueezeNetV1.0 pts/onednn-1.7.0 --ip --batch=inputs/ip/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/onednn-1.7.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.7.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU pts/onednn-1.7.0 --ip --batch=inputs/ip/shapes_1d --cfg=f32 --engine=cpu Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU pts/onednn-1.7.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU pts/mnn-1.2.0 Model: mobilenet-v1-1.0 pts/incompact3d-2.0.2 input_193_nodes.i3d Input: input.i3d 193 Cells Per Direction pts/sysbench-1.1.0 memory run Test: RAM / Memory pts/onednn-1.7.0 --conv --batch=inputs/conv/shapes_auto --cfg=u8s8f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/aom-av1-2.2.0 --cpu-used=6 --rt Encoder Mode: Speed 6 Realtime pts/astcenc-1.1.0 -exhaustive Preset: Exhaustive pts/svt-hevc-1.2.0 -encMode 7 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Tuning: 7 - Input: Bosphorus 1080p pts/mnn-1.2.0 Model: inception-v3 pts/basis-1.1.0 Settings: ETC1S pts/mnn-1.2.0 Model: MobileNetV2_224 pts/onednn-1.7.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU pts/onednn-1.7.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=u8s8f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU pts/aom-av1-2.2.0 --cpu-used=8 --rt Encoder Mode: Speed 8 Realtime pts/onednn-1.7.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/mnn-1.2.0 Model: resnet-v2-50 pts/onednn-1.7.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU pts/simdjson-1.2.0 kostya Throughput Test: Kostya pts/onednn-1.7.0 --ip --batch=inputs/ip/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/svt-hevc-1.2.0 -encMode 10 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Tuning: 10 - Input: Bosphorus 1080p pts/onednn-1.7.0 --conv --batch=inputs/conv/shapes_auto --cfg=f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/astcenc-1.1.0 -thorough Preset: Thorough pts/svt-vp9-1.3.0 -tune 1 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Tuning: PSNR/SSIM Optimized - Input: Bosphorus 1080p pts/aom-av1-2.2.0 --cpu-used=6 Encoder Mode: Speed 6 Two-Pass pts/simdjson-1.2.0 partial_tweets Throughput Test: PartialTweets pts/onednn-1.7.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU pts/build-mesa-1.0.0 Time To Compile pts/svt-vp9-1.3.0 -tune 2 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Tuning: VMAF Optimized - Input: Bosphorus 1080p pts/astcenc-1.1.0 -medium Preset: Medium pts/svt-hevc-1.2.0 -encMode 1 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Tuning: 1 - Input: Bosphorus 1080p pts/aom-av1-2.2.0 --cpu-used=4 Encoder Mode: Speed 4 Two-Pass pts/onednn-1.7.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU pts/build-nodejs-1.0.0 Time To Compile pts/svt-vp9-1.3.0 -tune 0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Tuning: Visual Quality Optimized - Input: Bosphorus 1080p pts/basis-1.1.0 -uastc -uastc_level 0 Settings: UASTC Level 0 pts/basis-1.1.0 -uastc -uastc_level 2 Settings: UASTC Level 2 pts/basis-1.1.0 -uastc -uastc_level 3 Settings: UASTC Level 3 pts/sysbench-1.1.0 cpu run Test: CPU pts/aom-av1-2.2.0 --cpu-used=0 --limit=20 Encoder Mode: Speed 0 Two-Pass pts/simdjson-1.2.0 distinct_user_id Throughput Test: DistinctUserID pts/simdjson-1.2.0 large_random Throughput Test: LargeRandom