core-i5-4670-december , "Timed HMMer Search 3.3.1 - Pfam Database Search", Lower Results Are Better "1",140.043,140.116,140.014 "2",140.603,140.178,140.735 "3",140.433,140.235,140.099 "4",140.596,140.101,140.024 "Timed MAFFT Alignment 7.471 - Multiple Sequence Alignment - LSU RNA", Lower Results Are Better "1",12.624,12.923,12.305 "2",12.487,12.512,12.556 "3",12.646,12.342,12.33 "4",12.574,12.419,12.396 "simdjson 0.7.1 - Throughput Test: Kostya", Higher Results Are Better "1",0.59,0.59,0.59 "2",0.59,0.59,0.59 "3",0.59,0.59,0.59 "4",0.59,0.59,0.59 "oneDNN 2.0 - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU", Lower Results Are Better "1a",10.7347,10.7508,10.8383 "2",11.1862,11.272,11.2567 "3",11.0405,11.1493,11.0378 "4",11.4596,11.0824,11.6594 "oneDNN 2.0 - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU", Lower Results Are Better "1a",13.6048,13.6539,13.6674 "2",16.3626,16.3778,16.3775 "3",17.2683,17.3542,17.1747 "4",17.1362,17.0419,17.1315 "oneDNN 2.0 - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "1a",6.25704,6.29383,6.29204 "2",6.33537,6.38906,6.34353 "3",6.33122,6.35106,6.45328 "4",6.34142,6.27328,6.37622 "oneDNN 2.0 - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "1a",4.91168,4.9262,4.93388 "2",5.6459,5.66797,5.66363 "3",5.71237,5.72696,5.73391 "4",5.71539,5.73925,5.74854 "oneDNN 2.0 - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU", Lower Results Are Better "1a",30.2219,30.2909,30.4009 "2",31.3892,31.4834,31.4365 "3",31.3965,31.4746,31.4059 "4",31.4215,31.2657,31.4356 "oneDNN 2.0 - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU", Lower Results Are Better "1a",12.5696,12.551,12.6344 "2",12.6644,12.6729,12.7072 "3",12.6544,12.7245,12.6851 "4",12.575,12.7518,12.5932 "oneDNN 2.0 - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU", Lower Results Are Better "1a",17.5004,17.6187,17.5325 "2",17.6998,17.6459,17.7341 "3",17.6726,17.7492,17.7634 "4",18.4,18.497,18.0117 "oneDNN 2.0 - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "1a",30.5131,30.585,30.5259 "2",31.3248,31.3102,31.1883 "3",31.2557,31.3399,31.2503 "4",31.1735,31.2135,31.23 "oneDNN 2.0 - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "1a",15.1265,15.2169,15.2639 "2",15.1382,15.1699,15.1548 "3",15.1463,15.1526,15.1764 "4",15.1422,15.1578,15.1469 "oneDNN 2.0 - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "1a",13.9185,13.9439,13.8084 "2",14.0366,14.0322,14.0329 "3",13.8307,13.873,14.049 "4",14.0391,14.0775,14.0558 "oneDNN 2.0 - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU", Lower Results Are Better "1a",8539.06,8739.8,8832.84 "2",8688.33,8829.05,9004.39 "3",8780.76,8941.95,9079.09 "4",8712.96,9050.01,9056.26 "oneDNN 2.0 - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU", Lower Results Are Better "1a",5411.48,5458.6,5510.38 "2",5561.45,5556.45,5558.42 "3",5493.85,5621.74,5557.35 "4",5614.49,5560.14,5587.12 "oneDNN 2.0 - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "1a",9018.59,9013.96,9062.74 "2",9141.75,9094.03,9108.68 "3",9171.05,9307.71,9220.07 "4",9267.75,9185.53,9175.04 "oneDNN 2.0 - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "1a",5379.27,5378.26,5464.61 "2",5510.78,5521.06,5595.91 "3",5585.81,5659.32,5629.39 "4",5644.49,5536.48,5506.02 "oneDNN 2.0 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU", Lower Results Are Better "1a",7.70386,7.70346,7.69306 "2",7.81118,7.82343,7.82472 "3",7.82754,7.87597,7.83164 "4",7.81807,7.83379,7.82839 "oneDNN 2.0 - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU", Lower Results Are Better "1a",9249.56,8953.98,9031.94 "2",9205.34,9169.47,9105.04 "3",9167.45,9216.8,9140.01 "4",9125.81,9175.29,9214.43 "oneDNN 2.0 - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU", Lower Results Are Better "1a",5510.11,5390.26,5498.64 "2",5560.06,5629.79,5623.7 "3",5527.04,5586.75,5575.42 "4",5620.92,5660.91,5676.4 "oneDNN 2.0 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "1a",7.50348,7.51528,7.5245 "2",7.5052,7.51728,7.5195 "3",7.51063,7.515,7.51834 "4",7.52616,7.61325,7.59128 "Coremark 1.0 - CoreMark Size 666 - Iterations Per Second", Higher Results Are Better "1a",96946.194862,96223.237912,93115.288366 "2",95728.132105,95894.516032,95006.234784 "3",88760.679019,95385.715989,90991.810737,98045.223359,94725.00148,93962.884661,95096.582467,102518.100852,94640.955874,96741.036338,99058.940069,91266.898637,96130.737803,90976.289305,95986.561881 "4",93759.156168,101297.879076,100212.952524,89450.438866,101233.786776,97205.346294,90359.744734,94434.279644,98165.531628,97140.428632,99881.390848,96327.513546,89315.619069,94809.196492,94663.353449 "Timed FFmpeg Compilation 4.2.2 - Time To Compile", Lower Results Are Better "1a",163.438,163.401,162.517 "2",162.466,161.65,162.582 "3",162.163,161.92,162.143 "4",162.497,162.665,162.804 "Build2 0.13 - Time To Compile", Lower Results Are Better "1a",364.031,363.422,366.852 "2",364.054,361.03,360.9 "3",365.626,364.676,362.191 "4",367.298,368.701,368.328 "Node.js V8 Web Tooling Benchmark - ", Higher Results Are Better "1a",9.66,9.84,9.61 "2",9.74,9.98,9.67 "3",9.89,9.76,9.87 "4",9.63,9.75,9.84 "SQLite Speedtest 3.30 - Timed Time - Size 1,000", Lower Results Are Better "1a",78.535,79.074,78.945 "2",78.338,78.894,78.705 "3",79.424,79.043,79.107 "4",78.951,78.918,79.447 "NCNN 20201218 - Target: CPU - Model: mobilenet", Lower Results Are Better "1a",36.31,36.37,36.31 "2",36.69,36.81,37.36 "3",36.8,37.46,36.34 "4",37.66,36.55,36.59 "NCNN 20201218 - Target: CPU-v2-v2 - Model: mobilenet-v2", Lower Results Are Better "1a",9.27,9.33,9.3 "2",9.28,9.3,9.38 "3",10.19,9.3,9.28 "4",9.25,9.28,10.2 "NCNN 20201218 - Target: CPU-v3-v3 - Model: mobilenet-v3", Lower Results Are Better "1a",8.14,8.06,8.1 "2",8.46,8.09,8.08 "3",8.07,8.47,8.07 "4",8.15,8.07,8.44 "NCNN 20201218 - Target: CPU - Model: shufflenet-v2", Lower Results Are Better "1a",10.78,10.79,10.78 "2",10.78,10.81,10.76 "3",10.81,10.8,10.83 "4",10.8,10.77,10.79 "NCNN 20201218 - Target: CPU - Model: mnasnet", Lower Results Are Better "1a",7.81,8.21,7.78 "2",7.8,7.91,8.06 "3",8.17,8.22,7.79 "4",7.82,7.74,7.75 "NCNN 20201218 - Target: CPU - Model: efficientnet-b0", Lower Results Are Better "1a",13.21,13.33,13.18 "2",13.7,13.21,13.32 "3",13.26,13.69,13.21 "4",13.2,13.17,13.74 "NCNN 20201218 - Target: CPU - Model: blazeface", Lower Results Are Better "1a",2.67,2.68,2.68 "2",2.69,2.68,2.68 "3",2.68,2.7,2.68 "4",2.68,2.68,2.69 "NCNN 20201218 - Target: CPU - Model: googlenet", Lower Results Are Better "1a",29.09,28.55,28.56 "2",28.59,28.61,28.58 "3",28.64,28.59,28.59 "4",28.56,28.58,28.66 "NCNN 20201218 - Target: CPU - Model: vgg16", Lower Results Are Better "1a",134.88,134.06,134.21 "2",134.24,133.99,133.89 "3",133.84,134.19,133.75 "4",135.16,133.61,134.67 "NCNN 20201218 - Target: CPU - Model: resnet18", Lower Results Are Better "1a",30.83,30.8,32.06 "2",31.29,31.16,31.57 "3",30.82,30.81,31.09 "4",31.83,30.67,30.78 "NCNN 20201218 - Target: CPU - Model: alexnet", Lower Results Are Better "1a",25.18,25.16,25.22 "2",25.72,25.72,25.68 "3",25.18,25.73,25.67 "4",25.47,25.24,25.25 "NCNN 20201218 - Target: CPU - Model: resnet50", Lower Results Are Better "1a",64.31,61.9,63.48 "2",63.17,64.82,61.91 "3",64.04,61.83,61.81 "4",61.89,63.84,61.9 "NCNN 20201218 - Target: CPU - Model: yolov4-tiny", Lower Results Are Better "1a",52.5,52.48,51.94 "2",52.83,52,51.98 "3",52.85,52.94,54.9 "4",51.84,51.94,53.55 "NCNN 20201218 - Target: CPU - Model: squeezenet_ssd", Lower Results Are Better "1a",52.17,51.32,52.34 "2",51.77,52.33,52.66 "3",51.27,53.26,51.46 "4",51.22,51.81,51.17 "NCNN 20201218 - Target: CPU - Model: regnety_400m", Lower Results Are Better "1a",16.17,16.15,16.12 "2",16.12,16.29,16.19 "3",16.67,16.67,16.18 "4",16.15,16.17,16.18