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
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:

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

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
1
March 18 2021
  2 Hours, 28 Minutes
2
March 18 2021
  2 Hours, 28 Minutes
3
March 18 2021
  2 Hours, 28 Minutes
Invert Hiding All Results Option
  2 Hours, 28 Minutes

Only show results where is faster than
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):

10600K pre RKL Suite 1.0.0 System Test suite extracted from 10600K pre RKL. pts/build-nodejs-1.0.0 Time To Compile pts/astcenc-1.1.0 -exhaustive Preset: Exhaustive pts/incompact3d-2.0.2 input_193_nodes.i3d Input: input.i3d 193 Cells Per Direction 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/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/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/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/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/mnn-1.2.0 Model: inception-v3 pts/mnn-1.2.0 Model: mobilenet-v1-1.0 pts/mnn-1.2.0 Model: MobileNetV2_224 pts/mnn-1.2.0 Model: resnet-v2-50 pts/mnn-1.2.0 Model: SqueezeNetV1.0 pts/basis-1.1.0 -uastc -uastc_level 3 Settings: UASTC Level 3 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=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - 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/build-mesa-1.0.0 Time To Compile pts/simdjson-1.2.0 partial_tweets Throughput Test: PartialTweets pts/simdjson-1.2.0 distinct_user_id Throughput Test: DistinctUserID pts/simdjson-1.2.0 kostya Throughput Test: Kostya pts/simdjson-1.2.0 large_random Throughput Test: LargeRandom pts/aom-av1-2.2.0 --cpu-used=6 Encoder Mode: Speed 6 Two-Pass pts/basis-1.1.0 -uastc -uastc_level 2 Settings: UASTC Level 2 pts/incompact3d-2.0.2 input_129_nodes.i3d Input: input.i3d 129 Cells Per Direction pts/aom-av1-2.2.0 --cpu-used=6 --rt Encoder Mode: Speed 6 Realtime pts/basis-1.1.0 Settings: ETC1S pts/astcenc-1.1.0 -thorough Preset: Thorough 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 --deconv --batch=inputs/deconv/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - 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 --ip --batch=inputs/ip/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU 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/onednn-1.7.0 --ip --batch=inputs/ip/shapes_3d --cfg=f32 --engine=cpu Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU 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/basis-1.1.0 -uastc -uastc_level 0 Settings: UASTC Level 0 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/aom-av1-2.2.0 --cpu-used=8 --rt Encoder Mode: Speed 8 Realtime 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/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/astcenc-1.1.0 -medium Preset: Medium 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/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/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/sysbench-1.1.0 memory run Test: RAM / Memory 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 --deconv --batch=inputs/deconv/shapes_3d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - 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