Core i7 4770K Xmas

Intel Core i7-4770K testing with a Gigabyte Z97-HD3 (F10c BIOS) and Gigabyte Intel HD 4600 2GB on Ubuntu 20.10 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 2012256-HA-COREI747702
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Audio Encoding 3 Tests
Timed Code Compilation 3 Tests
C/C++ Compiler Tests 4 Tests
CPU Massive 4 Tests
Creator Workloads 5 Tests
Encoding 3 Tests
HPC - High Performance Computing 3 Tests
Machine Learning 2 Tests
Multi-Core 5 Tests
Programmer / Developer System Benchmarks 6 Tests
Server 3 Tests

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December 25 2020
  5 Hours, 48 Minutes
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December 25 2020
  5 Hours, 39 Minutes
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December 25 2020
  5 Hours, 54 Minutes
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  5 Hours, 47 Minutes

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Core i7 4770K Xmas, "BRL-CAD 7.30.8 - VGR Performance Metric", Higher Results Are Better "1", "2", "3", "Build2 0.13 - Time To Compile", Lower Results Are Better "1",393.514,385.781,385.885 "2",386.51,382.162,388.079 "3",389.832,390.152,386.874 "CLOMP 1.2 - Static OMP Speedup", Higher Results Are Better "1",0.5,1,0.9,0.9,1,0.9,1.1,0.9,0.9,0.9,0.9 "2",1,1,1.1,1.1,1.3,1.3,1.1,1.2,1.1 "3",1.2,1.1,1.2,1.2,1.2,1,1.2,1.1,1.2,1.1,1.2,1 "Coremark 1.0 - CoreMark Size 666 - Iterations Per Second", Higher Results Are Better "1",144066.270484,144763.628138,144463.003928 "2",143587.902719,143897.832539,144678.542364 "3",143652.361286,145368.6458,144124.667838 "Monkey Audio Encoding 3.99.6 - WAV To APE", Lower Results Are Better "1",14.199,14.189,13.834,14.211,13.864 "2",13.858,13.911,13.867,13.836,13.891 "3",14.087,14,13.922,13.862,13.895 "NCNN 20201218 - Target: CPU - Model: mobilenet", Lower Results Are Better "1",39.34,38.99,39.56 "2",39.1,39.63,39.13 "3",39.87,39.69,39.74 "NCNN 20201218 - Target: CPU-v2-v2 - Model: mobilenet-v2", Lower Results Are Better "1",10.42,10.41,10.44 "2",10.61,10.48,10.89 "3",10.94,11.25,10.92 "NCNN 20201218 - Target: CPU-v3-v3 - Model: mobilenet-v3", Lower Results Are Better "1",8.74,8.83,8.65 "2",8.72,8.62,8.65 "3",9.07,8.83,8.84 "NCNN 20201218 - Target: CPU - Model: shufflenet-v2", Lower Results Are Better "1",11.32,11.42,11.48 "2",11.24,11.46,11.55 "3",11.27,11.41,11.63 "NCNN 20201218 - Target: CPU - Model: mnasnet", Lower Results Are Better "1",8.32,8.41,8.39 "2",8.36,8.54,8.36 "3",8.42,8.63,8.9 "NCNN 20201218 - Target: CPU - Model: efficientnet-b0", Lower Results Are Better "1",14.21,14.37,14.2 "2",14.12,14.19,14.65 "3",14.38,14.35,14.56 "NCNN 20201218 - Target: CPU - Model: blazeface", Lower Results Are Better "1",3.31,3.37,3.19 "2",3.14,3.21,3.17 "3",3.14,3.36,3.34 "NCNN 20201218 - Target: CPU - Model: googlenet", Lower Results Are Better "1",29.45,29.86,29.65 "2",30.43,30.34,30.53 "3",29.69,29.96,30.93 "NCNN 20201218 - Target: CPU - Model: vgg16", Lower Results Are Better "1",126.62,127.51,128.26 "2",127.68,127.3,127.66 "3",128.21,128.38,127.47 "NCNN 20201218 - Target: CPU - Model: resnet18", Lower Results Are Better "1",29.53,30.92,30.99 "2",30.74,31.6,31.93 "3",29.76,31.25,31.23 "NCNN 20201218 - Target: CPU - Model: alexnet", Lower Results Are Better "1",25.11,25.04,25.15 "2",25.19,25.1,25.19 "3",25.05,25.17,25.3 "NCNN 20201218 - Target: CPU - Model: resnet50", Lower Results Are Better "1",61.47,61.59,62.48 "2",63.1,62.74,63.96 "3",63.35,65.35,63.61 "NCNN 20201218 - Target: CPU - Model: yolov4-tiny", Lower Results Are Better "1",51.16,52.77,50.82 "2",52.02,52.82,54.11 "3",54.2,53.39,55.09 "NCNN 20201218 - Target: CPU - Model: squeezenet_ssd", Lower Results Are Better "1",40.66,39.94,41.24 "2",39.88,40.91,40.11 "3",41.7,41.57,41.43 "NCNN 20201218 - Target: CPU - Model: regnety_400m", Lower Results Are Better "1",20.5,20.7,20.86 "2",20.4,20.48,20.76 "3",20.47,20.86,21.36 "NCNN 20201218 - Target: Vulkan GPU - Model: mobilenet", Lower Results Are Better "1",39.39,39.14,39.19 "2",39.44,39.71,39.49 "3",39.56,39.68,39.68 "NCNN 20201218 - Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2", Lower Results Are Better "1",10.7,10.5,10.47 "2",10.28,10.72,10.51 "3",10.73,10.9,11.04 "NCNN 20201218 - Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3", Lower Results Are Better "1",8.6,9.02,8.68 "2",8.71,8.61,8.83 "3",8.86,8.77,8.89 "NCNN 20201218 - Target: Vulkan GPU - Model: shufflenet-v2", Lower Results Are Better "1",11.49,11.44,11.59 "2",11.24,11.52,11.5 "3",11.36,11.51,11.61 "NCNN 20201218 - Target: Vulkan GPU - Model: mnasnet", Lower Results Are Better "1",8.45,8.54,8.59 "2",8.36,8.57,8.31 "3",8.55,8.4,8.35 "NCNN 20201218 - Target: Vulkan GPU - Model: efficientnet-b0", Lower Results Are Better "1",14.31,14.37,14.51 "2",14.25,14.22,14.37 "3",14.09,14.27,14.41 "NCNN 20201218 - Target: Vulkan GPU - Model: blazeface", Lower Results Are Better "1",3.19,3.33,3.24 "2",3.14,3.21,3.2 "3",3.2,3.19,3.33 "NCNN 20201218 - Target: Vulkan GPU - Model: googlenet", Lower Results Are Better "1",29.66,29.91,29.87 "2",30.57,30.24,30.77 "3",30.08,30.07,30.96 "NCNN 20201218 - Target: Vulkan GPU - Model: vgg16", Lower Results Are Better "1",126.73,126.36,126.36 "2",128.27,128.69,128.06 "3",128.22,128.16,127.53 "NCNN 20201218 - Target: Vulkan GPU - Model: resnet18", Lower Results Are Better "1",29.81,30.66,30.93 "2",30.72,30.18,31.03 "3",30.51,31.31,30.25 "NCNN 20201218 - Target: Vulkan GPU - Model: alexnet", Lower Results Are Better "1",25.03,25.08,25.15 "2",25.06,25.72,26.07 "3",25.08,25.08,25.16 "NCNN 20201218 - Target: Vulkan GPU - Model: resnet50", Lower Results Are Better "1",61.92,61.7,66 "2",64.21,63.88,62.56 "3",64.57,63.32,62.14 "NCNN 20201218 - Target: Vulkan GPU - Model: yolov4-tiny", Lower Results Are Better "1",52.01,51.18,52.31 "2",53.94,53.03,52.18 "3",53.58,54.02,52.75 "NCNN 20201218 - Target: Vulkan GPU - Model: squeezenet_ssd", Lower Results Are Better "1",41,40.05,39.75 "2",39.98,42.42,41.41 "3",42.94,41.8,41.57 "NCNN 20201218 - Target: Vulkan GPU - Model: regnety_400m", Lower Results Are Better "1",20.7,20.84,20.56 "2",20.93,20.59,20.72 "3",20.67,21.03,20.95 "Node.js V8 Web Tooling Benchmark - ", Higher Results Are Better "1",9.39,9.14,9.38 "2",9.3,9.28,9.29 "3",9.14,9.18,9.4 "oneDNN 2.0 - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU", Lower Results Are Better "1",11.1443,11.1131,11.2628 "2",11.5718,11.3476,11.1658 "3",11.6258,11.5685,11.5855 "oneDNN 2.0 - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU", Lower Results Are Better "1",16.2264,15.5807,15.9355 "2",15.7974,15.953,15.2035 "3",18.9625,18.4192,19.8447,18.1514,19.5193,18.8939,18.4559,18.9602,18.8281 "oneDNN 2.0 - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "1",5.98587,5.96681,5.98111 "2",5.91618,5.93573,5.97247 "3",5.99584,5.9645,6.03583 "oneDNN 2.0 - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "1",4.7367,4.71723,4.72447 "2",4.70826,4.7038,4.6686 "3",4.99142,4.99881,5.00334 "oneDNN 2.0 - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU", Lower Results Are Better "1",32.1558,32.1525,32.0511 "2",32.0967,32.108,32.1737 "3",32.5051,32.6108,32.6181 "oneDNN 2.0 - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU", Lower Results Are Better "1",13.6403,13.5101,13.4987 "2",13.3402,13.4002,13.3268 "3",13.2526,13.3728,13.2511 "oneDNN 2.0 - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU", Lower Results Are Better "1",18.9455,18.9216,18.9125 "2",18.9212,18.9611,18.9781 "3",19.0376,19.0262,19.1572 "oneDNN 2.0 - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "1",30.9508,31.0473,31.0815 "2",30.8708,30.9597,30.9446 "3",30.4449,31.1614,31.2358 "oneDNN 2.0 - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "1",14.456,14.2702,14.5312 "2",14.44,15.1679,15.4244,14.3375,15.1717,14.6648,14.6085 "3",14.9539,14.3957,14.3519 "oneDNN 2.0 - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "1",12.9757,12.9358,12.9569 "2",12.9833,12.9638,12.9155 "3",12.9803,12.9106,12.8149 "oneDNN 2.0 - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU", Lower Results Are Better "1",10206.1,10555.6,10496 "2",10233.3,10453.6,10730.3 "3",10477.4,10774.9,10798.3 "oneDNN 2.0 - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU", Lower Results Are Better "1",5812.93,5655.33,5883.87 "2",5955.19,5968.63,5845.15 "3",5889.27,5899.81,6125.23 "oneDNN 2.0 - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "1",10892.7,10517.2,10508.2 "2",10351.1,10381.2,10530.2 "3",10847,10701.9,11011.6 "oneDNN 2.0 - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "1",5669.98,5791.09,5731.08 "2",5920.58,6011.87,5931.11 "3",5982.37,6242.5,5900.82 "oneDNN 2.0 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU", Lower Results Are Better "1",8.09121,8.1542,8.06765 "2",8.08866,8.09616,8.10815 "3",8.14463,8.90279,8.87446,8.95221,8.93,8.96102,8.98246,8.98723,8.96352,8.84597 "oneDNN 2.0 - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU", Lower Results Are Better "1",10662.7,10486,10775.8 "2",10763.2,10683.7,10494.1 "3",10658.7,11091.7,11025 "oneDNN 2.0 - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU", Lower Results Are Better "1",5838.18,5921.47,5962.26 "2",5895.31,5787.25,5783.82 "3",5925.2,5949.24,6070.97 "oneDNN 2.0 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "1",7.31001,7.35252,7.32599 "2",7.38918,7.29553,7.32229 "3",7.25086,7.26767,7.1904 "Opus Codec Encoding 1.3.1 - WAV To Opus Encode", Lower Results Are Better "1",9.202,9.127,9.133,9.095,9.103 "2",9.136,9.134,9.159,9.106,9.131 "3",9.11,9.124,9.136,9.093,9.101 "simdjson 0.7.1 - Throughput Test: Kostya", Higher Results Are Better "1",0.6,0.59,0.6 "2",0.6,0.59,0.6 "3",0.6,0.59,0.6 "simdjson 0.7.1 - Throughput Test: LargeRandom", Higher Results Are Better "1",0.4,0.4,0.4 "2",0.4,0.4,0.4 "3",0.4,0.4,0.4 "simdjson 0.7.1 - Throughput Test: PartialTweets", Higher Results Are Better "1",0.66,0.66,0.66 "2",0.66,0.66,0.66 "3",0.66,0.66,0.66 "simdjson 0.7.1 - Throughput Test: DistinctUserID", Higher Results Are Better "1",0.68,0.68,0.68 "2",0.68,0.68,0.68 "3",0.68,0.68,0.68 "SQLite Speedtest 3.30 - Timed Time - Size 1,000", Lower Results Are Better "1",80.174,81.338,79.045 "2",82.29,80.861,81.421 "3",79.165,79.729,81.407 "Timed Eigen Compilation 3.3.9 - Time To Compile", Lower Results Are Better "1",96.932,96.635,96.4 "2",97.947,97.028,96.893 "3",97.835,96.913,97.148 "Timed FFmpeg Compilation 4.2.2 - Time To Compile", Lower Results Are Better "1",141.659,147.176,148.99 "2",141.808,148.762,148.314 "3",143.433,149.31,148.191 "Timed HMMer Search 3.3.1 - Pfam Database Search", Lower Results Are Better "1",137.845,137.706,137.77 "2",138.111,137.93,137.777 "3",137.965,137.879,137.865 "VKMark 2020-05-21 - Resolution: 1920 x 1080", Higher Results Are Better "1",291,292,292 "2",293,291,291 "3", "WavPack Audio Encoding 5.3 - WAV To WavPack", Lower Results Are Better "1",15.547,15.491,15.632,15.498,15.572 "2",15.488,15.549,15.515,15.653,15.513 "3",15.508,15.595,15.698,15.502,15.508