################## # Performance Model Version 45 #################### # COMBs # number of combinations 9 #################### # COMB_8 # 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 (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 617e5fe6 7372800 0.000000e+00 2.127055e+05 1.216918e+04 3.190582e+06 6.808756e+11 15 afdd228b 3276800 0.000000e+00 6.346686e+04 7.329654e+02 6.346686e+05 4.028580e+10 10 cea37d6d 819200 0.000000e+00 7.969263e+03 1.770463e+02 1.354775e+05 1.080188e+09 17 #################### # COMB_4 # 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 (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 617e5fe6 7372800 0.000000e+00 8.656100e+04 6.943816e+03 1.471537e+06 1.281974e+11 17 afdd228b 3276800 0.000000e+00 3.567215e+04 3.302464e+03 3.567215e+05 1.283409e+10 10 cea37d6d 819200 0.000000e+00 1.101988e+04 5.146633e+02 1.101988e+05 1.217027e+09 10 #################### # COMB_3 # 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 (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 afdd228b 3276800 0.000000e+00 3.935885e+04 6.351673e+03 3.935885e+05 1.589463e+10 10 cea37d6d 819200 0.000000e+00 1.194615e+04 1.359754e+03 1.194615e+05 1.445595e+09 10 617e5fe6 7372800 0.000000e+00 8.781176e+04 9.198610e+03 1.317176e+06 1.169328e+11 15 #################### # COMB_1 # 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 (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 617e5fe6 7372800 0.000000e+00 8.754335e+04 8.654029e+03 1.575780e+06 1.392972e+11 18 afdd228b 3276800 0.000000e+00 3.542725e+04 1.501284e+03 3.542725e+05 1.257344e+10 10 cea37d6d 819200 0.000000e+00 1.193774e+04 1.685032e+03 1.193774e+05 1.453490e+09 10 #################### # COMB_0 # 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 (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 617e5fe6 7372800 0.000000e+00 8.763521e+04 5.876858e+03 9.639873e+05 8.485914e+10 11 afdd228b 3276800 0.000000e+00 3.909159e+04 6.650440e+03 4.300075e+05 1.729619e+10 11 cea37d6d 819200 0.000000e+00 1.211577e+04 1.649480e+03 1.211577e+05 1.495126e+09 10 #################### # COMB_2 # 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 (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 617e5fe6 7372800 0.000000e+00 8.616388e+04 4.981316e+03 1.550950e+06 1.340825e+11 18 afdd228b 3276800 0.000000e+00 3.647899e+04 2.965394e+03 4.377479e+05 1.607412e+10 12 cea37d6d 819200 0.000000e+00 1.073272e+04 1.010096e+02 1.073272e+05 1.152015e+09 10 #################### # COMB_6 # 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 (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 617e5fe6 7372800 0.000000e+00 8.786078e+04 7.200822e+03 1.317912e+06 1.165705e+11 15 afdd228b 3276800 0.000000e+00 3.795195e+04 3.399141e+03 3.795195e+05 1.451905e+10 10 cea37d6d 819200 0.000000e+00 1.163527e+04 1.023060e+03 1.163527e+05 1.364262e+09 10 #################### # COMB_7 # 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 (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 617e5fe6 7372800 0.000000e+00 8.814631e+04 6.725805e+03 1.498487e+06 1.328551e+11 17 cea37d6d 819200 0.000000e+00 1.170806e+04 1.094676e+03 1.170806e+05 1.382770e+09 10 afdd228b 3276800 0.000000e+00 4.283079e+04 7.621190e+03 4.283079e+05 1.892559e+10 10 #################### # COMB_5 # 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 (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 617e5fe6 7372800 0.000000e+00 9.172766e+04 1.075608e+04 1.375915e+06 1.279449e+11 15 cea37d6d 819200 0.000000e+00 1.117240e+04 8.447401e+02 1.117240e+05 1.255362e+09 10 afdd228b 3276800 0.000000e+00 3.472448e+04 1.278416e+03 3.819693e+05 1.328166e+10 11