3900X New Tests

AMD Ryzen 9 3900X 12-Core testing with a ASUS TUF GAMING X570-PLUS (WI-FI) (2203 BIOS) and MSI AMD Radeon RX 470/480/570/570X/580/580X/590 8GB 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 2009250-PTS-3900XNEW05
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Creator Workloads 4 Tests
HPC - High Performance Computing 3 Tests
Imaging 3 Tests
Machine Learning 3 Tests
NVIDIA GPU Compute 2 Tests
Single-Threaded 2 Tests
Vulkan Compute 2 Tests

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September 25 2020
  2 Hours, 40 Minutes
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September 25 2020
  2 Hours, 7 Minutes
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September 25 2020
  2 Hours, 22 Minutes
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3900X New Tests, "RealSR-NCNN 20200818 - Scale: 4x - TAA: No", Lower Results Are Better "1",15.554,15.517,15.669 "2",15.62,15.514,15.68 "3",15.607,15.609,15.631 "RealSR-NCNN 20200818 - Scale: 4x - TAA: Yes", Lower Results Are Better "1",110.462,112.01,112.454 "2",110.451,112.104,113.22 "3",110.509,111.912,112.668 "OSBench - Test: Create Files", Lower Results Are Better "1",11.834637,12.488875,12.177036 "2",12.18508,12.554768,12.522232 "3",11.935332,11.967424,12.250143 "OSBench - Test: Create Threads", Lower Results Are Better "1",13.899803,14.228821,14.059544 "2",11.749268,11.229515,13.990402,12.090206,11.1413,14.04047,13.608932,14.541149,11.138916,13.670921,13.859272,14.081001,11.210442,11.131763,10.819435 "3",10.631084,13.990402,11.510849,12.040138,13.949871,11.100769,13.918877,11.379719,13.899803,13.878345,11.191368,13.921261,10.871887,14.31942,11.000633 "OSBench - Test: Launch Programs", Lower Results Are Better "1",40.87925,42.319298,42.01889 "2",41.601658,40.62891,40.509701 "3",41.749477,40.850639,40.380955 "OSBench - Test: Create Processes", Lower Results Are Better "1",32.408237,32.980442,34.089088 "2",33.152103,32.479763,32.730103 "3",32.78017,32.479763,31.859875 "OSBench - Test: Memory Allocations", Lower Results Are Better "1",65.074921,65.601826,65.618992 "2",68.356037,68.711042,68.563938 "3",68.045855,68.511009,67.760944 "WebP Image Encode 1.1 - Encode Settings: Default", Lower Results Are Better "1",1.485,1.407,1.482,1.447 "2",1.452,1.461,1.387 "3",1.476,1.402,1.458 "WebP Image Encode 1.1 - Encode Settings: Quality 100", Lower Results Are Better "1",2.236,2.276,2.209 "2",2.158,2.21,2.158 "3",2.198,2.24,2.232 "WebP Image Encode 1.1 - Encode Settings: Quality 100, Lossless", Lower Results Are Better "1",15.198,15.225,15.746 "2",15.499,15.918,15.211 "3",15.34,15.069,15.202 "WebP Image Encode 1.1 - Encode Settings: Quality 100, Highest Compression", Lower Results Are Better "1",6.87,6.867,7.096 "2",6.887,7.141,6.693,7.031 "3",6.87,6.719,6.75 "WebP Image Encode 1.1 - Encode Settings: Quality 100, Lossless, Highest Compression", Lower Results Are Better "1",31.758,31.992,32.301 "2",32.296,31.949,33.353 "3",32.513,33.456,33.309 "LibRaw 0.20 - Post-Processing Benchmark", Higher Results Are Better "1",42.33,42.4,42.54 "2",42.41,42.02,42.52 "3",42.72,42.85,42.66 "dcraw - RAW To PPM Image Conversion", Lower Results Are Better "1",38.603,40.835,39.077 "2",38.583,38.503,40.17 "3",38.493,38.556,38.452 "eSpeak-NG Speech Engine 20200907 - Text-To-Speech Synthesis", Lower Results Are Better "1",26.566,27.033,28.573,27.159,27.157 "2",27.29,27.806,27.086,27.083 "3",27.541,26.979,27.392,28.14 "MPV - Video Input: Big Buck Bunny Sunflower 4K - Decode: Software Only", Higher Results Are Better "1",702.7489706666,698.36740347957,693.13563736955 "2",700.16067356298,701.71814530389,700.13377985131 "3",697.10875549406,698.9574137369,697.66084542797 "MPV - Video Input: Big Buck Bunny Sunflower 1080p - Decode: Software Only", Higher Results Are Better "1",2357.626322819,2335.8059271372,2342.3416215651 "2",2311.9259425058,2344.6592270335,2346.493225995 "3",2321.4859697109,2347.1654865325,2313.8882668138 "NCNN 20200916 - Target: CPU - Model: squeezenet", Lower Results Are Better "1",16.26,16.34,16.36 "2",15.99,15.66,16.14 "3",16.34,15.89,16.07 "NCNN 20200916 - Target: CPU - Model: mobilenet", Lower Results Are Better "1",16.87,16.56,16.62 "2",16.35,16.35,16.58 "3",16.62,16.31,16.68 "NCNN 20200916 - Target: CPU-v2-v2 - Model: mobilenet-v2", Lower Results Are Better "1",5.38,5.49,5.47 "2",5.42,5.4,5.42 "3",5.38,5.46,5.45 "NCNN 20200916 - Target: CPU-v3-v3 - Model: mobilenet-v3", Lower Results Are Better "1",4.76,4.79,4.78 "2",4.78,4.79,4.81 "3",4.78,4.78,4.78 "NCNN 20200916 - Target: CPU - Model: shufflenet-v2", Lower Results Are Better "1",4.87,4.93,4.89 "2",4.92,4.87,4.92 "3",4.87,4.88,4.9 "NCNN 20200916 - Target: CPU - Model: mnasnet", Lower Results Are Better "1",4.86,4.85,4.85 "2",4.82,4.84 "3",4.82,4.85,4.84 "NCNN 20200916 - Target: CPU - Model: efficientnet-b0", Lower Results Are Better "1",6.62,6.67,6.66 "2",6.57,6.72,6.59 "3",6.55,6.7,6.65 "NCNN 20200916 - Target: CPU - Model: blazeface", Lower Results Are Better "1",1.94,1.95,1.97 "2",1.96,1.95,1.97 "3",1.94,1.98,1.97 "NCNN 20200916 - Target: CPU - Model: googlenet", Lower Results Are Better "1",17.02,17.29,17.18 "2",16.91,17.25,17.24 "3",17.19,17.54,17.01 "NCNN 20200916 - Target: CPU - Model: vgg16", Lower Results Are Better "1",66.91,67.31,67.3 "2",67.34,66.27,67.34 "3",67.42,67.82,66.87 "NCNN 20200916 - Target: CPU - Model: resnet18", Lower Results Are Better "1",16.19,16.7,16.31 "2",16.18,16.52,16.22 "3",16.43,16.59,16.29 "NCNN 20200916 - Target: CPU - Model: alexnet", Lower Results Are Better "1",16.23,16.24,16.25 "2",16.22,16.48,16.23 "3",16.12,16.29,16.22 "NCNN 20200916 - Target: CPU - Model: resnet50", Lower Results Are Better "1",188.66,27.85,27.47 "2",27.26,27.09,27.37 "3",27.85,27.95,27.26 "NCNN 20200916 - Target: CPU - Model: yolov4-tiny", Lower Results Are Better "1",28.85,28.68,28.58 "2",28.35,28.08,28.58 "3",28.48,28.58,28.58 "NCNN 20200916 - Target: Vulkan GPU - Model: squeezenet", Lower Results Are Better "1",5.61,5.73,6.07,5.63,5.63,5.65,5.62 "2",5.65,5.64,5.66 "3",5.61,5.6,5.63 "NCNN 20200916 - Target: Vulkan GPU - Model: mobilenet", Lower Results Are Better "1",6.1,6.1,6.05,6.29,6.08,6.1,6.08 "2",6,6.07,6.08 "3",6.03,6.05,6.1 "NCNN 20200916 - Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2", Lower Results Are Better "1",2.95,2.87,2.86,2.93,2.92,2.92,2.88 "2",2.93,2.93,2.92 "3",2.94,2.79,2.92 "NCNN 20200916 - Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3", Lower Results Are Better "1",4.06,4.08,4.08,4.07,4.05,4.07,4.07 "2",4.09,4.06,4.08 "3",4.11,4.09,4.08 "NCNN 20200916 - Target: Vulkan GPU - Model: shufflenet-v2", Lower Results Are Better "1",2.52,2.48,2.47,2.49,2.47,2.49,2.49 "2",2.48,2.48,2.48 "3",2.49,2.47,2.5 "NCNN 20200916 - Target: Vulkan GPU - Model: mnasnet", Lower Results Are Better "1",3.05,3.08,3.08,3.06,3.03,3.05,3.05 "2",3.04,3.04,3.05 "3",3.09,3.03,3.07 "NCNN 20200916 - Target: Vulkan GPU - Model: efficientnet-b0", Lower Results Are Better "1",10.9,11.01,10.88,11.1,11.05,11.06,11.21 "2",10.95,10.85,10.91 "3",10.89,11.15,11.13 "NCNN 20200916 - Target: Vulkan GPU - Model: blazeface", Lower Results Are Better "1",0.94,0.92,0.94,0.95,0.91,0.9,0.95 "2",0.94,0.94,0.91 "3",0.94,0.94,0.93 "NCNN 20200916 - Target: Vulkan GPU - Model: googlenet", Lower Results Are Better "1",6.23,6.22,6.25,6.36,6.41,6.43 "2",6.23,6.37,6.38 "3",6.24,6.42,6.3 "NCNN 20200916 - Target: Vulkan GPU - Model: vgg16", Lower Results Are Better "1",14.95,15.15,15.01,15.35,15.01,15.05,15.53 "2",15.09,15.11,15.12 "3",14.89,15.33,14.95 "NCNN 20200916 - Target: Vulkan GPU - Model: resnet18", Lower Results Are Better "1",2.75,2.73,2.71,2.75,2.73,2.73,2.74 "2",2.73,2.84,2.72 "3",2.73,2.73,2.73 "NCNN 20200916 - Target: Vulkan GPU - Model: alexnet", Lower Results Are Better "1",5.56,5.59,5.55,5.58,5.67,5.56,5.57 "2",5.58,5.58,5.59 "3",5.59,5.57,5.57 "NCNN 20200916 - Target: Vulkan GPU - Model: resnet50", Lower Results Are Better "1",7.65,7.85,7.78,7.58,7.7,7.66,7.73 "2",7.93,7.77,7.53 "3",7.85,7.87,7.83 "NCNN 20200916 - Target: Vulkan GPU - Model: yolov4-tiny", Lower Results Are Better "1",8.63,8.64,8.58,8.57,8.63,8.6,8.59 "2",8.53,8.6,8.59 "3",8.56,8.56,8.63 "TNN 0.2.3 - Target: CPU - Model: MobileNet v2", Lower Results Are Better "1",254.513,253.772,253.614 "2",247.267,250.318,252.645 "3",250.171,250.977,255.314 "TNN 0.2.3 - Target: CPU - Model: SqueezeNet v1.1", Lower Results Are Better "1",232.908,234.335,233.198 "2",233.698,233.71,233.066 "3",230.493,229.295,232.132 "OpenCV 4.4 - Test: Features 2D", Lower Results Are Better "1",131387,154205,144575,141485,140722,157926,155108,137927,146590,134232,145002,129318 "2",137246,139307,147839,141106,132356,130419,142517,135657,131749,135829,130975,124693 "3",134297,142174,146524,136352,142288,160501,139563,145628,148718,145105,130704,143667 "OpenCV 4.4 - Test: Object Detection", Lower Results Are Better "1",66879,67645,64452 "2",63115,65548,66357 "3",61784,65429,61294,67655,61332,63989,66900,67331,66013,70636,67469,72552,67193,68946,72831 "OpenCV 4.4 - Test: DNN - Deep Neural Network", Lower Results Are Better "1",4758,3297,3417,4857,4155,4114,3872,3897,3556,4373,5349,4690,4357,5674,5395 "2",4995,4432,4920,3950,4065,4381,3946,3778,5356,4359,4318,3746 "3",4626,3993,4498,4394,5073,4174,3686,5486,4100,4975,4535,4423,5022,3863,3797 "InfluxDB 1.8.2 - Concurrent Streams: 4 - Batch Size: 10000 - Tags: 2,5000,1 - Points Per Series: 10000", Higher Results Are Better "1",1313406.6,1307465.6,1310471.6 "2",1310259.9,1313985.8,1314914.3 "3",1310868.3,1311076.3,1319682.6 "InfluxDB 1.8.2 - Concurrent Streams: 64 - Batch Size: 10000 - Tags: 2,5000,1 - Points Per Series: 10000", Higher Results Are Better "1",1449934.4,1439437.9,1445211.9 "2",1446104.3,1450122,1446079.6 "3",1450387.8,1441999.9,1443655 "InfluxDB 1.8.2 - Concurrent Streams: 1024 - Batch Size: 10000 - Tags: 2,5000,1 - Points Per Series: 10000", Higher Results Are Better "1",1471228.2,1472192.4,1472635 "2",1471628.2,1465141,1467497.8 "3",1476459.5,1466936.3,1471813.5