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13 Commits

Author SHA1 Message Date
Gael Guennebaud
b3fd93207b Fix typos found using codespell 2018-06-07 14:43:02 +02:00
Rasmus Munk Larsen
1b7294f6fc Fix cut-and-paste error. 2017-09-08 16:35:58 -07:00
Rasmus Munk Larsen
94e2213b38 Avoid undefined behavior in Eigen::TensorCostModel::numThreads. 
If the cost is large enough then the thread count can be larger than the maximum
representable int, so just casting it to an int is undefined behavior.

Contributed by phurst@google.com.
 2017-09-08 15:49:55 -07:00
Benoit Steiner
488ad7dd1b Added missing EIGEN_DEVICE_FUNC qualifiers 2016-09-14 13:35:00 -07:00
Benoit Steiner
7944d4431f Made the cost model cwiseMax and cwiseMin methods consts to help the PowerPC cuda compiler compile this code. 2016-08-18 13:46:36 -07:00
Rasmus Munk Larsen
7df811cfe5 Minor cleanups: 1. Get rid of unused variables. 2. Get rid of last uses of EIGEN_USE_COST_MODEL. 2016-05-18 15:09:48 -07:00
Benoit Steiner
83ef39e055 Turn on the cost model by default. This results in some significant speedups for smaller tensors. For example, below are the results for the various tensor reductions. 
Before:
BM_colReduction_12T/10       1000000       1949    51.29 MFlops/s
BM_colReduction_12T/80        100000      15636   409.29 MFlops/s
BM_colReduction_12T/640        20000      95100  4307.01 MFlops/s
BM_colReduction_12T/4K           500    4573423  5466.36 MFlops/s
BM_colReduction_4T/10        1000000       1867    53.56 MFlops/s
BM_colReduction_4T/80         500000       5288  1210.11 MFlops/s
BM_colReduction_4T/640         10000     106924  3830.75 MFlops/s
BM_colReduction_4T/4K            500    9946374  2513.48 MFlops/s
BM_colReduction_8T/10        1000000       1912    52.30 MFlops/s
BM_colReduction_8T/80         200000       8354   766.09 MFlops/s
BM_colReduction_8T/640         20000      85063  4815.22 MFlops/s
BM_colReduction_8T/4K            500    5445216  4591.19 MFlops/s
BM_rowReduction_12T/10       1000000       2041    48.99 MFlops/s
BM_rowReduction_12T/80        100000      15426   414.87 MFlops/s
BM_rowReduction_12T/640        50000      39117 10470.98 MFlops/s
BM_rowReduction_12T/4K           500    3034298  8239.14 MFlops/s
BM_rowReduction_4T/10        1000000       1834    54.51 MFlops/s
BM_rowReduction_4T/80         500000       5406  1183.81 MFlops/s
BM_rowReduction_4T/640         50000      35017 11697.16 MFlops/s
BM_rowReduction_4T/4K            500    3428527  7291.76 MFlops/s
BM_rowReduction_8T/10        1000000       1925    51.95 MFlops/s
BM_rowReduction_8T/80         200000       8519   751.23 MFlops/s
BM_rowReduction_8T/640         50000      33441 12248.42 MFlops/s
BM_rowReduction_8T/4K           1000    2852841  8763.19 MFlops/s


After:
BM_colReduction_12T/10      50000000         59  1678.30 MFlops/s
BM_colReduction_12T/80       5000000        725  8822.71 MFlops/s
BM_colReduction_12T/640        20000      90882  4506.93 MFlops/s
BM_colReduction_12T/4K           500    4668855  5354.63 MFlops/s
BM_colReduction_4T/10       50000000         59  1687.37 MFlops/s
BM_colReduction_4T/80        5000000        737  8681.24 MFlops/s
BM_colReduction_4T/640         50000     108637  3770.34 MFlops/s
BM_colReduction_4T/4K            500    7912954  3159.38 MFlops/s
BM_colReduction_8T/10       50000000         60  1657.21 MFlops/s
BM_colReduction_8T/80        5000000        726  8812.48 MFlops/s
BM_colReduction_8T/640         20000      91451  4478.90 MFlops/s
BM_colReduction_8T/4K            500    5441692  4594.16 MFlops/s
BM_rowReduction_12T/10      20000000         93  1065.28 MFlops/s
BM_rowReduction_12T/80       2000000        950  6730.96 MFlops/s
BM_rowReduction_12T/640        50000      38196 10723.48 MFlops/s
BM_rowReduction_12T/4K           500    3019217  8280.29 MFlops/s
BM_rowReduction_4T/10       20000000         93  1064.30 MFlops/s
BM_rowReduction_4T/80        2000000        959  6667.71 MFlops/s
BM_rowReduction_4T/640         50000      37433 10941.96 MFlops/s
BM_rowReduction_4T/4K            500    3036476  8233.23 MFlops/s
BM_rowReduction_8T/10       20000000         93  1072.47 MFlops/s
BM_rowReduction_8T/80        2000000        959  6670.04 MFlops/s
BM_rowReduction_8T/640         50000      38069 10759.37 MFlops/s
BM_rowReduction_8T/4K           1000    2758988  9061.29 MFlops/s
 2016-05-16 08:55:21 -07:00
Benoit Steiner
09653e1f82 Improved the portability of the tensor code 2016-05-11 23:29:09 -07:00
Benoit Steiner
968ec1c2ae Use numext::isfinite instead of std::isfinite 2016-05-03 19:56:40 -07:00
Benoit Steiner
c07404f6a1 Restore Tensor support for non c++11 compilers 2016-04-29 15:19:19 -07:00
Rasmus Munk Larsen
07ac4f7e02 Eigen Tensor cost model part 2: Thread scheduling for standard evaluators and reductions. The cost model is turned off by default. 2016-04-14 18:28:23 -07:00
Rasmus Munk Larsen
aeb5494a0b Improvements to cost model. 2016-04-14 15:52:58 -07:00
Rasmus Munk Larsen
235e83aba6 Eigen cost model part 1. This implements a basic recursive framework to estimate the cost of evaluating tensor expressions. 2016-04-14 13:57:35 -07:00