################## # 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 2c1922b7 819200 0.000000e+00 2.469013e+03 5.595193e+01 2.765294e+05 6.831054e+08 112 d39bff17 3276800 0.000000e+00 1.667528e+04 1.964808e+02 1.300672e+06 2.169208e+10 78 ff82dda0 7372800 0.000000e+00 5.216745e+04 4.664151e+02 3.443052e+06 1.796296e+11 66 #################### # COMB_5 # 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 (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 2c1922b7 819200 0.000000e+00 7.490410e+02 1.344248e+02 7.415506e+04 5.733412e+07 99 d39bff17 3276800 0.000000e+00 2.737524e+03 2.974057e+02 3.942034e+05 1.091878e+09 144 ff82dda0 7372800 0.000000e+00 7.212728e+03 1.319942e+03 6.924219e+05 5.161506e+09 96 #################### # COMB_6 # 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 (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 2c1922b7 819200 0.000000e+00 7.688939e+02 1.457751e+02 6.843156e+04 5.450789e+07 89 d39bff17 3276800 0.000000e+00 2.735563e+03 2.889694e+02 2.899697e+05 8.020820e+08 106 ff82dda0 7372800 0.000000e+00 6.820126e+03 9.314994e+02 7.638542e+05 5.306763e+09 112 #################### # COMB_2 # 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 (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 2c1922b7 819200 0.000000e+00 7.150281e+02 1.235393e+02 6.363750e+04 4.686092e+07 89 d39bff17 3276800 0.000000e+00 2.835249e+03 4.125186e+02 1.899617e+05 5.499903e+08 67 ff82dda0 7372800 0.000000e+00 6.720945e+03 7.632032e+02 6.989783e+05 4.758372e+09 104 #################### # COMB_4 # 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 (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 2c1922b7 819200 0.000000e+00 7.190609e+02 1.144317e+02 7.406327e+04 5.460474e+07 103 d39bff17 3276800 0.000000e+00 2.867186e+03 4.168496e+02 2.838514e+05 8.310575e+08 99 ff82dda0 7372800 0.000000e+00 6.809425e+03 9.031920e+02 6.400859e+05 4.435298e+09 94 #################### # 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 2c1922b7 819200 0.000000e+00 7.136273e+02 1.258701e+02 7.350362e+04 5.408605e+07 103 d39bff17 3276800 0.000000e+00 2.942246e+03 4.585544e+02 1.706502e+05 5.142907e+08 58 ff82dda0 7372800 0.000000e+00 6.744194e+03 8.416374e+02 5.597681e+05 3.833978e+09 83 #################### # COMB_7 # 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 (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 2c1922b7 819200 0.000000e+00 7.204798e+02 9.746533e+01 1.080720e+05 7.928859e+07 150 d39bff17 3276800 0.000000e+00 2.539831e+03 4.296517e+02 3.885942e+05 1.015208e+09 153 ff82dda0 7372800 0.000000e+00 7.293979e+03 1.385713e+03 6.929280e+05 5.236621e+09 95 #################### # COMB_1 # 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 (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 2c1922b7 819200 0.000000e+00 7.460951e+02 1.203288e+02 7.386342e+04 5.654256e+07 99 d39bff17 3276800 0.000000e+00 2.972783e+03 5.066224e+02 2.259315e+05 6.911522e+08 76 ff82dda0 7372800 0.000000e+00 6.643349e+03 8.230064e+02 6.510482e+05 4.391520e+09 98 #################### # COMB_3 # 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 (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 2c1922b7 819200 0.000000e+00 7.518059e+02 1.406096e+02 8.495406e+04 6.610309e+07 113 d39bff17 3276800 0.000000e+00 2.794983e+03 3.357608e+02 4.164524e+05 1.180775e+09 149 ff82dda0 7372800 0.000000e+00 6.735838e+03 7.525487e+02 6.803197e+05 4.639723e+09 101