################## # Performance Model Version 45 #################### # COMBs # number of combinations 4 #################### # COMB_3 # 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 (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 1638400 0.000000e+00 3.789658e+04 4.182352e+03 1.250587e+06 4.797021e+10 33 617e5fe6 3686400 0.000000e+00 1.286436e+05 1.271269e+04 2.958803e+06 3.843483e+11 23 cea37d6d 409600 0.000000e+00 4.236597e+03 2.366692e+02 2.372495e+05 1.008267e+09 56 #################### # COMB_0 # 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 (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 afdd228b 1638400 0.000000e+00 2.864580e+04 3.233071e+03 5.156243e+05 1.495862e+10 18 617e5fe6 3686400 0.000000e+00 5.948740e+04 4.910517e+03 1.070773e+06 6.413154e+10 18 cea37d6d 409600 0.000000e+00 1.060245e+04 4.247968e+02 1.060245e+05 1.125924e+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 afdd228b 1638400 0.000000e+00 3.046163e+04 4.754796e+03 5.483094e+05 1.710934e+10 18 617e5fe6 3686400 0.000000e+00 5.865963e+04 4.672589e+03 1.349171e+06 7.964405e+10 23 cea37d6d 409600 0.000000e+00 1.042618e+04 1.817032e+02 1.042618e+05 1.087383e+09 10 #################### # COMB_1 # 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 (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 afdd228b 1638400 0.000000e+00 2.939722e+04 4.040622e+03 4.409582e+05 1.320784e+10 15 617e5fe6 3686400 0.000000e+00 5.704610e+04 3.429433e+03 1.255014e+06 7.185241e+10 22 cea37d6d 409600 0.000000e+00 1.049902e+04 4.776188e+02 1.049902e+05 1.104575e+09 10