################## # Performance Model Version 45 #################### # COMBs # number of combinations 9 #################### # COMB_0 # number of types devices 1 #################### # DEV_0 # device type (CPU - 0, CUDA - 1, OPENCL - 2, MIC - 3) 0 #################### # DEV_0 # device id 0 #################### # DEV_0 # number of cores 1 ########## # number of implementations 1 ##### # Model for cpu0_impl0 (Comb0) # number of entries 3 # sumlnx sumlnx2 sumlny sumlnxlny alpha beta n minx maxx 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 nan nan 0 0 0 # a b c nan nan nan # not multiple-regression-base 0 # hash size flops mean (us) dev (us) sum sum2 n ff82dda0 7372800 8.856576e+08 4.711469e+04 4.337925e+02 3.203799e+06 1.509588e+11 68 2c1922b7 819200 3.287040e+07 1.979166e+03 8.798869e+01 6.828124e+05 1.354070e+09 345 d39bff17 3276800 2.625536e+08 1.482664e+04 2.506296e+02 2.298130e+06 3.408328e+10 155 #################### # COMB_4 # number of types devices 1 #################### # DEV_0 # device type (CPU - 0, CUDA - 1, OPENCL - 2, MIC - 3) 1 #################### # DEV_0 # device id 1 #################### # DEV_0 # number of cores 1 ########## # number of implementations 1 ##### # Model for cuda1_impl0 (Comb4) # number of entries 3 # sumlnx sumlnx2 sumlny sumlnxlny alpha beta n minx maxx 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 nan nan 0 0 0 # a b c nan nan nan # not multiple-regression-base 0 # hash size flops mean (us) dev (us) sum sum2 n ff82dda0 7372800 8.856576e+08 6.573848e+03 6.169449e+02 1.360787e+06 9.024393e+09 207 2c1922b7 819200 3.287040e+07 6.955196e+02 8.976154e+01 1.286711e+05 9.098386e+07 185 d39bff17 3276800 2.625536e+08 2.647434e+03 2.520462e+02 4.685958e+05 1.251821e+09 177 #################### # COMB_2 # number of types devices 1 #################### # DEV_0 # device type (CPU - 0, CUDA - 1, OPENCL - 2, MIC - 3) 1 #################### # DEV_0 # device id 3 #################### # DEV_0 # number of cores 1 ########## # number of implementations 1 ##### # Model for cuda3_impl0 (Comb2) # number of entries 3 # sumlnx sumlnx2 sumlny sumlnxlny alpha beta n minx maxx 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 nan nan 0 0 0 # a b c nan nan nan # not multiple-regression-base 0 # hash size flops mean (us) dev (us) sum sum2 n ff82dda0 7372800 8.856576e+08 6.555664e+03 6.950469e+02 1.252132e+06 8.300825e+09 191 2c1922b7 819200 3.287040e+07 6.812499e+02 8.342802e+01 1.273937e+05 8.808853e+07 187 d39bff17 3276800 2.625536e+08 2.596800e+03 1.668067e+02 5.894736e+05 1.537061e+09 227 #################### # COMB_3 # number of types devices 1 #################### # DEV_0 # device type (CPU - 0, CUDA - 1, OPENCL - 2, MIC - 3) 1 #################### # DEV_0 # device id 2 #################### # DEV_0 # number of cores 1 ########## # number of implementations 1 ##### # Model for cuda2_impl0 (Comb3) # number of entries 3 # sumlnx sumlnx2 sumlny sumlnxlny alpha beta n minx maxx 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 nan nan 0 0 0 # a b c nan nan nan # not multiple-regression-base 0 # hash size flops mean (us) dev (us) sum sum2 n ff82dda0 7372800 8.856576e+08 6.446442e+03 5.413553e+02 1.276395e+06 8.286236e+09 198 2c1922b7 819200 3.287040e+07 6.941204e+02 8.002896e+01 1.277182e+05 8.983023e+07 184 d39bff17 3276800 2.625536e+08 2.630763e+03 2.300111e+02 4.603835e+05 1.220418e+09 175 #################### # COMB_1 # number of types devices 1 #################### # DEV_0 # device type (CPU - 0, CUDA - 1, OPENCL - 2, MIC - 3) 1 #################### # DEV_0 # device id 0 #################### # DEV_0 # number of cores 1 ########## # number of implementations 1 ##### # Model for cuda0_impl0 (Comb1) # number of entries 3 # sumlnx sumlnx2 sumlny sumlnxlny alpha beta n minx maxx 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 nan nan 0 0 0 # a b c nan nan nan # not multiple-regression-base 0 # hash size flops mean (us) dev (us) sum sum2 n ff82dda0 7372800 8.856576e+08 6.554622e+03 7.028631e+02 1.238824e+06 8.213390e+09 189 2c1922b7 819200 3.287040e+07 6.905951e+02 7.284704e+01 1.353566e+05 9.451674e+07 196 d39bff17 3276800 2.625536e+08 2.623425e+03 2.211699e+02 4.905805e+05 1.296149e+09 187 #################### # COMB_7 # number of types devices 1 #################### # DEV_0 # device type (CPU - 0, CUDA - 1, OPENCL - 2, MIC - 3) 1 #################### # DEV_0 # device id 4 #################### # DEV_0 # number of cores 1 ########## # number of implementations 1 ##### # Model for cuda4_impl0 (Comb7) # number of entries 3 # sumlnx sumlnx2 sumlny sumlnxlny alpha beta n minx maxx 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 nan nan 0 0 0 # a b c nan nan nan # not multiple-regression-base 0 # hash size flops mean (us) dev (us) sum sum2 n ff82dda0 7372800 8.856576e+08 6.504271e+03 6.367049e+02 9.951534e+05 6.534773e+09 153 2c1922b7 819200 3.287040e+07 7.029111e+02 9.289767e+01 7.169693e+04 5.127683e+07 102 d39bff17 3276800 2.625536e+08 2.684586e+03 3.481310e+02 4.080571e+05 1.113886e+09 152 #################### # COMB_8 # number of types devices 1 #################### # DEV_0 # device type (CPU - 0, CUDA - 1, OPENCL - 2, MIC - 3) 1 #################### # DEV_0 # device id 5 #################### # DEV_0 # number of cores 1 ########## # number of implementations 1 ##### # Model for cuda5_impl0 (Comb8) # number of entries 3 # sumlnx sumlnx2 sumlny sumlnxlny alpha beta n minx maxx 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 nan nan 0 0 0 # a b c nan nan nan # not multiple-regression-base 0 # hash size flops mean (us) dev (us) sum sum2 n ff82dda0 7372800 8.856576e+08 6.618862e+03 8.843940e+02 8.405955e+05 5.663119e+09 127 2c1922b7 819200 3.287040e+07 7.079333e+02 9.356613e+01 6.796160e+04 4.895273e+07 96 d39bff17 3276800 2.625536e+08 2.800887e+03 4.371231e+02 3.221020e+05 9.241450e+08 115 #################### # COMB_5 # number of types devices 1 #################### # DEV_0 # device type (CPU - 0, CUDA - 1, OPENCL - 2, MIC - 3) 1 #################### # DEV_0 # device id 6 #################### # DEV_0 # number of cores 1 ########## # number of implementations 1 ##### # Model for cuda6_impl0 (Comb5) # number of entries 3 # sumlnx sumlnx2 sumlny sumlnxlny alpha beta n minx maxx 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 nan nan 0 0 0 # a b c nan nan nan # not multiple-regression-base 0 # hash size flops mean (us) dev (us) sum sum2 n ff82dda0 7372800 8.856576e+08 6.576395e+03 7.489644e+02 8.878133e+05 5.914339e+09 135 2c1922b7 819200 3.287040e+07 7.050156e+02 1.025857e+02 8.037177e+04 5.786307e+07 114 d39bff17 3276800 2.625536e+08 2.645162e+03 2.750078e+02 4.205807e+05 1.124529e+09 159 #################### # COMB_6 # number of types devices 1 #################### # DEV_0 # device type (CPU - 0, CUDA - 1, OPENCL - 2, MIC - 3) 1 #################### # DEV_0 # device id 7 #################### # DEV_0 # number of cores 1 ########## # number of implementations 1 ##### # Model for cuda7_impl0 (Comb6) # number of entries 3 # sumlnx sumlnx2 sumlny sumlnxlny alpha beta n minx maxx 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 nan nan 0 0 0 # a b c nan nan nan # not multiple-regression-base 0 # hash size flops mean (us) dev (us) sum sum2 n ff82dda0 7372800 8.856576e+08 6.544427e+03 6.576164e+02 9.358531e+05 6.186464e+09 143 2c1922b7 819200 3.287040e+07 7.150712e+02 1.054194e+02 8.223319e+04 6.008061e+07 115 d39bff17 3276800 2.625536e+08 2.613530e+03 2.505172e+02 3.972565e+05 1.047781e+09 152