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path: root/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h
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* Add missing EIGEN_DEPRECATED annotations to deprecated functions and fix few ↵Gravatar Eugene Zhulenev2019-04-23
| | | | other doxygen warnings
* Parallelize tensor contraction over the inner dimension in cases where where ↵Gravatar Rasmus Munk Larsen2018-09-26
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | one or both of the outer dimensions (m and n) are small but k is large. This speeds up individual matmul microbenchmarks by up to 85%. Naming below is BM_Matmul_M_K_N_THREADS, measured on a 2-socket Intel Broadwell-based server. Benchmark Base (ns) New (ns) Improvement ------------------------------------------------------------------ BM_Matmul_1_80_13522_1 387457 396013 -2.2% BM_Matmul_1_80_13522_2 406487 230789 +43.2% BM_Matmul_1_80_13522_4 395821 123211 +68.9% BM_Matmul_1_80_13522_6 391625 97002 +75.2% BM_Matmul_1_80_13522_8 408986 113828 +72.2% BM_Matmul_1_80_13522_16 399988 67600 +83.1% BM_Matmul_1_80_13522_22 411546 60044 +85.4% BM_Matmul_1_80_13522_32 393528 57312 +85.4% BM_Matmul_1_80_13522_44 390047 63525 +83.7% BM_Matmul_1_80_13522_88 387876 63592 +83.6% BM_Matmul_1_1500_500_1 245359 248119 -1.1% BM_Matmul_1_1500_500_2 401833 143271 +64.3% BM_Matmul_1_1500_500_4 210519 100231 +52.4% BM_Matmul_1_1500_500_6 251582 86575 +65.6% BM_Matmul_1_1500_500_8 211499 80444 +62.0% BM_Matmul_3_250_512_1 70297 68551 +2.5% BM_Matmul_3_250_512_2 70141 52450 +25.2% BM_Matmul_3_250_512_4 67872 58204 +14.2% BM_Matmul_3_250_512_6 71378 63340 +11.3% BM_Matmul_3_250_512_8 69595 41652 +40.2% BM_Matmul_3_250_512_16 72055 42549 +40.9% BM_Matmul_3_250_512_22 70158 54023 +23.0% BM_Matmul_3_250_512_32 71541 56042 +21.7% BM_Matmul_3_250_512_44 71843 57019 +20.6% BM_Matmul_3_250_512_88 69951 54045 +22.7% BM_Matmul_3_1500_512_1 369328 374284 -1.4% BM_Matmul_3_1500_512_2 428656 223603 +47.8% BM_Matmul_3_1500_512_4 205599 139508 +32.1% BM_Matmul_3_1500_512_6 214278 139071 +35.1% BM_Matmul_3_1500_512_8 184149 142338 +22.7% BM_Matmul_3_1500_512_16 156462 156983 -0.3% BM_Matmul_3_1500_512_22 163905 158259 +3.4% BM_Matmul_3_1500_512_32 155314 157662 -1.5% BM_Matmul_3_1500_512_44 235434 158657 +32.6% BM_Matmul_3_1500_512_88 156779 160275 -2.2% BM_Matmul_1500_4_512_1 363358 349528 +3.8% BM_Matmul_1500_4_512_2 303134 263319 +13.1% BM_Matmul_1500_4_512_4 176208 130086 +26.2% BM_Matmul_1500_4_512_6 148026 115449 +22.0% BM_Matmul_1500_4_512_8 131656 98421 +25.2% BM_Matmul_1500_4_512_16 134011 82861 +38.2% BM_Matmul_1500_4_512_22 134950 85685 +36.5% BM_Matmul_1500_4_512_32 133165 90081 +32.4% BM_Matmul_1500_4_512_44 133203 90644 +32.0% BM_Matmul_1500_4_512_88 134106 100566 +25.0% BM_Matmul_4_1500_512_1 439243 435058 +1.0% BM_Matmul_4_1500_512_2 451830 257032 +43.1% BM_Matmul_4_1500_512_4 276434 164513 +40.5% BM_Matmul_4_1500_512_6 182542 144827 +20.7% BM_Matmul_4_1500_512_8 179411 166256 +7.3% BM_Matmul_4_1500_512_16 158101 155560 +1.6% BM_Matmul_4_1500_512_22 152435 155448 -1.9% BM_Matmul_4_1500_512_32 155150 149538 +3.6% BM_Matmul_4_1500_512_44 193842 149777 +22.7% BM_Matmul_4_1500_512_88 149544 154468 -3.3%
* Fix typos found using codespellGravatar Gael Guennebaud2018-06-07
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* Fix cut-and-paste error.Gravatar Rasmus Munk Larsen2017-09-08
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* Avoid undefined behavior in Eigen::TensorCostModel::numThreads.Gravatar Rasmus Munk Larsen2017-09-08
| | | | | | | 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.
* Added missing EIGEN_DEVICE_FUNC qualifiersGravatar Benoit Steiner2016-09-14
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* Made the cost model cwiseMax and cwiseMin methods consts to help the PowerPC ↵Gravatar Benoit Steiner2016-08-18
| | | | cuda compiler compile this code.
* Minor cleanups: 1. Get rid of unused variables. 2. Get rid of last uses of ↵Gravatar Rasmus Munk Larsen2016-05-18
| | | | EIGEN_USE_COST_MODEL.
* Turn on the cost model by default. This results in some significant speedups ↵Gravatar Benoit Steiner2016-05-16
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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
* Improved the portability of the tensor codeGravatar Benoit Steiner2016-05-11
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* Use numext::isfinite instead of std::isfiniteGravatar Benoit Steiner2016-05-03
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* Restore Tensor support for non c++11 compilersGravatar Benoit Steiner2016-04-29
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* Eigen Tensor cost model part 2: Thread scheduling for standard evaluators ↵Gravatar Rasmus Munk Larsen2016-04-14
| | | | and reductions. The cost model is turned off by default.
* Improvements to cost model.Gravatar Rasmus Munk Larsen2016-04-14
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* Eigen cost model part 1. This implements a basic recursive framework to ↵Gravatar Rasmus Munk Larsen2016-04-14
estimate the cost of evaluating tensor expressions.
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