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* Make EIGEN_HAS_C99_MATH user configurableGravatar Gael Guennebaud2016-05-20
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* Make EIGEN_HAS_RVALUE_REFERENCES user configurableGravatar Gael Guennebaud2016-05-20
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* Rename EIGEN_HAVE_RVALUE_REFERENCES to EIGEN_HAS_RVALUE_REFERENCESGravatar Gael Guennebaud2016-05-20
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* polygamma is C99/C++11 onlyGravatar Gael Guennebaud2016-05-20
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* Add a EIGEN_MAX_CPP_VER option to limit the C++ version to be used.Gravatar Gael Guennebaud2016-05-20
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* Improve doc of special math functionsGravatar Gael Guennebaud2016-05-20
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* Rename UniformRandom to UnitRandom.Gravatar Gael Guennebaud2016-05-20
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* Fix coding practice in Quaternion::UniformRandomGravatar Gael Guennebaud2016-05-20
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* bug #823: add static method to Quaternion for uniform random rotations.Gravatar Joseph Mirabel2016-05-20
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* Remove std:: to enable custom scalar types.Gravatar Gael Guennebaud2016-05-19
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* made a fix to the GMRES solver so that it now correctly reports the error ↵Gravatar David Dement2016-05-16
| | | | achieved in the solution process
* Fix unit test.Gravatar Gael Guennebaud2016-05-19
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* Improve unit tests of zeta, polygamma, and digammaGravatar Gael Guennebaud2016-05-19
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* zeta and polygamma are not unary functions, but binary ones.Gravatar Gael Guennebaud2016-05-19
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* zeta and digamma do not require C++11/C99Gravatar Gael Guennebaud2016-05-19
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* Add some c++11 flags in documentationGravatar Gael Guennebaud2016-05-19
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* bug #1201: optimize affine*vector productsGravatar Gael Guennebaud2016-05-19
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* bug #1221: disable gcc 6 warning: ignoring attributes on template argumentGravatar Gael Guennebaud2016-05-19
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* Fix SelfAdjointEigenSolver for some input expression types, and add new ↵Gravatar Gael Guennebaud2016-05-19
| | | | regression unit tests for sparse and selfadjointview inputs.
* DiagonalWrapper is a vector, so it must expose the LinearAccessBit flag.Gravatar Gael Guennebaud2016-05-19
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* Add support for SelfAdjointView::diagonal()Gravatar Gael Guennebaud2016-05-19
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* Fix SelfAdjointView::triangularView for complexes.Gravatar Gael Guennebaud2016-05-19
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* bug #1230: add support for SelfadjointView::triangularView.Gravatar Gael Guennebaud2016-05-19
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* Advertize the packet api of the tensor reducers iff the corresponding packet ↵Gravatar Benoit Steiner2016-05-18
| | | | primitives are available.
* bug #1231: fix compilation regression regarding complex_array/=real_array ↵Gravatar Gael Guennebaud2016-05-18
| | | | and add respective unit tests
* Use coeff(i,j) instead of operator().Gravatar Gael Guennebaud2016-05-18
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* bug #1224: fix regression in (dense*dense).sparseView() by specializing ↵Gravatar Gael Guennebaud2016-05-18
| | | | evaluator<SparseView<Product>> for sparse products only.
* Use default sorting strategy for square products.Gravatar Gael Guennebaud2016-05-18
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* Extend sparse*sparse product unit test to check that the expected ↵Gravatar Gael Guennebaud2016-05-18
| | | | implementation is used (conservative vs auto pruning).
* bug #1229: bypass usage of Derived::Options which is available for plain ↵Gravatar Gael Guennebaud2016-05-18
| | | | matrix types only. Better use column-major storage anyway.
* Pass argument by const ref instead of by value in pow(AutoDiffScalar...)Gravatar Gael Guennebaud2016-05-18
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* bug #1223: fix compilation of AutoDiffScalar's min/max operators, and add ↵Gravatar Gael Guennebaud2016-05-18
| | | | regression unit test.
* bug #1222: fix compilation in AutoDiffScalar and add respective unit testGravatar Gael Guennebaud2016-05-18
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* Big 1213: add regression unit test.Gravatar Gael Guennebaud2016-05-18
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* bug #1213: rename some enums type for consistency.Gravatar Gael Guennebaud2016-05-18
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* #if defined(EIGEN_USE_NONBLOCKING_THREAD_POOL) is now #if ↵Gravatar Benoit Steiner2016-05-17
| | | | !defined(EIGEN_USE_SIMPLE_THREAD_POOL): the non blocking thread pool is the default since it's more scalable, and one needs to request the old thread pool explicitly.
* Fixed compilation errorGravatar Benoit Steiner2016-05-17
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* Fixed compilation error in the tensor thread poolGravatar Benoit Steiner2016-05-17
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* Merge upstream.Gravatar Rasmus Munk Larsen2016-05-17
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* | Roll back changes to core. Move include of TensorFunctors.h up to satisfy ↵Gravatar Rasmus Munk Larsen2016-05-17
| | | | | | | | dependence in TensorCostModel.h.
| * Merged eigen/eigen into defaultGravatar Rasmus Larsen2016-05-17
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| * Enable the use of the packet api to evaluate tensor broadcasts. This speed ↵Gravatar Benoit Steiner2016-05-17
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | things up quite a bit: Before" M_broadcasting/10 500000 3690 27.10 MFlops/s BM_broadcasting/80 500000 4014 1594.24 MFlops/s BM_broadcasting/640 100000 14770 27731.35 MFlops/s BM_broadcasting/4K 5000 632711 39512.48 MFlops/s After: BM_broadcasting/10 500000 4287 23.33 MFlops/s BM_broadcasting/80 500000 4455 1436.41 MFlops/s BM_broadcasting/640 200000 10195 40173.01 MFlops/s BM_broadcasting/4K 5000 423746 58997.57 MFlops/s
| * Allow vectorized padding on GPU. This helps speed things up a littleGravatar Benoit Steiner2016-05-17
| | | | | | | | | | | | | | | | | | | | | | | | | | Before: BM_padding/10 5000000 460 217.03 MFlops/s BM_padding/80 5000000 460 13899.40 MFlops/s BM_padding/640 5000000 461 888421.17 MFlops/s BM_padding/4K 5000000 460 54316322.55 MFlops/s After: BM_padding/10 5000000 454 220.20 MFlops/s BM_padding/80 5000000 455 14039.86 MFlops/s BM_padding/640 5000000 452 904968.83 MFlops/s BM_padding/4K 5000000 411 60750049.21 MFlops/s
| * Pulled latest updates from trunk.Gravatar Benoit Steiner2016-05-17
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| * | Don't rely on c++11 extension when we don't have to.Gravatar Benoit Steiner2016-05-17
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| * | Avoid float to double conversionGravatar Benoit Steiner2016-05-17
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| | * Added missing costPerCoeff methodGravatar Benoit Steiner2016-05-16
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| | * 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
| | * Fixed syntax errorGravatar Benoit Steiner2016-05-16
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| | * Turnon the new thread pool by default since it scales much better over ↵Gravatar Benoit Steiner2016-05-13
| | | | | | | | | | | | multiple cores. It is still possible to revert to the old thread pool by compiling with the EIGEN_USE_SIMPLE_THREAD_POOL define.
generated by cgit v1.2.3 (git 2.39.1) at 2024-07-09 14:38:49 +0000