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- ##################
- # Performance Model Version
- 45
- ####################
- # COMBs
- # number of combinations
- 5
- ####################
- # COMB_2
- # 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 (Comb2)
- # number of entries
- 9
- # 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
- 492beed5 33177600 7.077888e+09 2.745578e+03 3.064191e+02 6.616844e+05 1.839335e+09 241
- 9c6670ef 29491200 7.077888e+09 2.745578e+03 3.064191e+02 6.616844e+05 1.839335e+09 241
- c00cf6b7 29491200 7.077888e+09 2.745578e+03 3.064191e+02 6.616844e+05 1.839335e+09 241
- 78a2cc08 29491200 7.077888e+09 2.745578e+03 3.064191e+02 6.616844e+05 1.839335e+09 241
- a7cdf15b 44236800 1.415578e+10 5.491156e+03 6.128382e+02 1.323369e+06 7.357340e+09 241
- 24c84a50 11059200 1.769472e+09 6.863945e+02 7.660478e+01 1.654211e+05 1.149584e+08 241
- 0b0b0ce8 3686400 2.621440e+08 1.582927e+02 3.333442e+01 3.434951e+04 5.678402e+06 217
- 4220e23d 14745600 2.097152e+09 8.206871e+02 1.017181e+02 1.148962e+05 9.574235e+07 140
- 87a7dc42 74649600 2.388787e+10 9.813897e+03 7.998509e+02 1.570224e+06 1.551237e+10 160
- ####################
- # 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
- 9
- # 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
- 492beed5 33177600 7.077888e+09 2.686428e+03 2.002215e+02 6.716071e+05 1.814247e+09 250
- 9c6670ef 29491200 7.077888e+09 2.686428e+03 2.002215e+02 6.716071e+05 1.814247e+09 250
- c00cf6b7 29491200 7.077888e+09 2.686428e+03 2.002215e+02 6.716071e+05 1.814247e+09 250
- 78a2cc08 29491200 7.077888e+09 2.686428e+03 2.002215e+02 6.716071e+05 1.814247e+09 250
- a7cdf15b 44236800 1.415578e+10 5.372856e+03 4.004430e+02 1.343214e+06 7.256988e+09 251
- 24c84a50 11059200 1.769472e+09 6.716070e+02 5.005537e+01 1.679018e+05 1.133904e+08 250
- 0b0b0ce8 3686400 2.621440e+08 1.630480e+02 3.438768e+01 3.097912e+04 5.275762e+06 190
- 4220e23d 14745600 2.097152e+09 8.448030e+02 7.773742e+01 2.433033e+05 2.072837e+08 288
- 87a7dc42 74649600 2.388787e+10 9.873153e+03 8.026227e+02 1.579704e+06 1.569974e+10 160
- ####################
- # COMB_0
- # 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 (Comb0)
- # number of entries
- 9
- # 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
- 492beed5 33177600 7.077888e+09 2.791098e+03 3.147711e+02 6.503258e+05 1.838209e+09 233
- 9c6670ef 29491200 7.077888e+09 2.791098e+03 3.147711e+02 6.503258e+05 1.838209e+09 233
- c00cf6b7 29491200 7.077888e+09 2.791098e+03 3.147711e+02 6.503258e+05 1.838209e+09 233
- 78a2cc08 29491200 7.077888e+09 2.791098e+03 3.147711e+02 6.503258e+05 1.838209e+09 233
- a7cdf15b 44236800 1.415578e+10 5.582196e+03 6.295422e+02 1.300652e+06 7.352836e+09 233
- 24c84a50 11059200 1.769472e+09 6.977745e+02 7.869277e+01 1.625815e+05 1.148881e+08 233
- 0b0b0ce8 3686400 2.621440e+08 1.624855e+02 3.298013e+01 2.940987e+04 4.975550e+06 181
- 4220e23d 14745600 2.097152e+09 8.152506e+02 1.017614e+02 1.173961e+05 9.719839e+07 144
- 87a7dc42 74649600 2.388787e+10 1.001360e+04 7.827579e+02 1.582149e+06 1.593981e+10 158
- ####################
- # 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
- 9
- # 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
- 492beed5 33177600 7.077888e+09 2.754203e+03 2.682327e+02 6.830422e+05 1.899080e+09 248
- 9c6670ef 29491200 7.077888e+09 2.754203e+03 2.682327e+02 6.830422e+05 1.899080e+09 248
- c00cf6b7 29491200 7.077888e+09 2.754203e+03 2.682327e+02 6.830422e+05 1.899080e+09 248
- 78a2cc08 29491200 7.077888e+09 2.754203e+03 2.682327e+02 6.830422e+05 1.899080e+09 248
- a7cdf15b 44236800 1.415578e+10 5.508406e+03 5.364654e+02 1.366084e+06 7.596320e+09 248
- 24c84a50 11059200 1.769472e+09 6.885507e+02 6.705818e+01 1.707605e+05 1.186925e+08 248
- 0b0b0ce8 3686400 2.621440e+08 1.622246e+02 3.553894e+01 3.714942e+04 6.315779e+06 229
- 4220e23d 14745600 2.097152e+09 8.611626e+02 9.290485e+01 2.411255e+05 2.100651e+08 280
- 87a7dc42 74649600 2.388787e+10 9.935915e+03 7.366769e+02 1.569875e+06 1.568389e+10 158
- ####################
- # COMB_4
- # 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 (Comb4)
- # number of entries
- 9
- # 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
- 492beed5 33177600 7.077888e+09 1.712078e+05 4.163047e+04 2.773567e+07 5.029326e+12 162
- 9c6670ef 29491200 7.077888e+09 1.712078e+05 4.163047e+04 2.773567e+07 5.029326e+12 162
- c00cf6b7 29491200 7.077888e+09 1.712078e+05 4.163047e+04 2.773567e+07 5.029326e+12 162
- 78a2cc08 29491200 7.077888e+09 1.712078e+05 4.163047e+04 2.773567e+07 5.029326e+12 162
- a7cdf15b 44236800 1.415578e+10 3.424156e+05 8.326094e+04 5.547134e+07 2.011730e+13 162
- 24c84a50 11059200 1.769472e+09 4.280195e+04 1.040762e+04 6.933918e+06 3.143329e+11 162
- 0b0b0ce8 3686400 2.621440e+08 6.441655e+03 1.152866e+03 3.220827e+05 2.141201e+09 50
- 4220e23d 14745600 2.097152e+09 4.927734e+04 1.166029e+04 5.913281e+06 3.077063e+11 120
- 87a7dc42 74649600 2.388787e+10 5.091210e+05 1.022002e+05 6.974957e+07 3.694192e+13 137
|
