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Author SHA1 Message Date
Benoit Steiner
7d980d74e5 Started to vectorize the processing of 16bit floats on CPU. 2016-05-23 15:21:40 -07:00
Benoit Steiner
5d51a7f12c Don't optimize the processing of the last rows of a matrix matrix product in cases that violate the assumptions made by the optimized code path. 2016-05-23 15:13:16 -07:00
Benoit Steiner
7aa5bc9558 Fixed a typo in the array.cpp test 2016-05-23 14:39:51 -07:00
Benoit Steiner
a09cbf9905 Merged in rmlarsen/eigen (pull request PR-188) 
Minor cleanups: 1. Get rid of a few unused variables. 2. Get rid of last uses of EIGEN_USE_COST_MODEL.
 2016-05-23 12:55:12 -07:00
Christoph Hertzberg
88654762da Replace multiple constructors of half-type by a generic/templated constructor. This fixes an incompatibility with long double, exposed by the previous commit. 2016-05-23 10:03:03 +02:00
Christoph Hertzberg
718521d5cf Silenced several double-promotion warnings 2016-05-22 18:17:04 +02:00
Christoph Hertzberg
b5a7603822 fixed macro name 2016-05-22 16:49:29 +02:00
Christoph Hertzberg
25a03c02d6 Fix some sign-compare warnings 2016-05-22 16:42:27 +02:00
Christoph Hertzberg
0851d5d210 Identify clang++ even if it is not named llvm-clang++ 2016-05-22 15:21:14 +02:00
Gael Guennebaud
6a15e14cda Document EIGEN_MAX_CPP_VER and user controllable compiler features. 2016-05-20 15:26:09 +02:00
Gael Guennebaud
ccaace03c9 Make EIGEN_HAS_CONSTEXPR user configurable 2016-05-20 15:10:08 +02:00
Gael Guennebaud
c3410804cd Make EIGEN_HAS_VARIADIC_TEMPLATES user configurable 2016-05-20 15:05:38 +02:00
Gael Guennebaud
abd1c1af7a Make EIGEN_HAS_STD_RESULT_OF user configurable 2016-05-20 15:01:27 +02:00
Gael Guennebaud
1395056fc0 Make EIGEN_HAS_C99_MATH user configurable 2016-05-20 14:58:19 +02:00
Gael Guennebaud
48bf5ec216 Make EIGEN_HAS_RVALUE_REFERENCES user configurable 2016-05-20 14:54:20 +02:00
Gael Guennebaud
f43ae88892 Rename EIGEN_HAVE_RVALUE_REFERENCES to EIGEN_HAS_RVALUE_REFERENCES 2016-05-20 14:48:51 +02:00
Gael Guennebaud
8d6bd5691b polygamma is C99/C++11 only 2016-05-20 14:45:33 +02:00
Gael Guennebaud
998f2efc58 Add a EIGEN_MAX_CPP_VER option to limit the C++ version to be used. 2016-05-20 14:44:28 +02:00
Gael Guennebaud
c028d96089 Improve doc of special math functions 2016-05-20 14:18:48 +02:00
Gael Guennebaud
0ba32f99bd Rename UniformRandom to UnitRandom. 2016-05-20 13:21:34 +02:00
Gael Guennebaud
7a9d9cde94 Fix coding practice in Quaternion::UniformRandom 2016-05-20 13:19:52 +02:00
Joseph Mirabel
eb0cc2573a bug #823: add static method to Quaternion for uniform random rotations. 2016-05-20 13:15:40 +02:00
Gael Guennebaud
2f656ce447 Remove std:: to enable custom scalar types. 2016-05-19 23:13:47 +02:00
Rasmus Larsen
b1e080c752 Merged eigen/eigen into default 2016-05-18 15:21:50 -07:00
Rasmus Munk Larsen
5624219b6b Merge. 2016-05-18 15:16:06 -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
bb3ff8e9d9 Advertize the packet api of the tensor reducers iff the corresponding packet primitives are available. 2016-05-18 14:52:49 -07:00
Gael Guennebaud
84df9142e7 bug #1231: fix compilation regression regarding complex_array/=real_array and add respective unit tests 2016-05-18 23:00:13 +02:00
Gael Guennebaud
21d692d054 Use coeff(i,j) instead of operator(). 2016-05-18 17:09:20 +02:00
Gael Guennebaud
8456bbbadb bug #1224: fix regression in (dense*dense).sparseView() by specializing evaluator<SparseView<Product>> for sparse products only. 2016-05-18 16:53:28 +02:00
Gael Guennebaud
b507b82326 Use default sorting strategy for square products. 2016-05-18 16:51:54 +02:00
Gael Guennebaud
1fa15ceee6 Extend sparse*sparse product unit test to check that the expected implementation is used (conservative vs auto pruning). 2016-05-18 16:50:54 +02:00
Gael Guennebaud
548a487800 bug #1229: bypass usage of Derived::Options which is available for plain matrix types only. Better use column-major storage anyway. 2016-05-18 16:44:05 +02:00
Gael Guennebaud
43790e009b Pass argument by const ref instead of by value in pow(AutoDiffScalar...) 2016-05-18 16:28:02 +02:00
Gael Guennebaud
1fbfab27a9 bug #1223: fix compilation of AutoDiffScalar's min/max operators, and add regression unit test. 2016-05-18 16:26:26 +02:00
Gael Guennebaud
448d9d943c bug #1222: fix compilation in AutoDiffScalar and add respective unit test 2016-05-18 16:00:11 +02:00
Gael Guennebaud
5a71eb5985 Big 1213: add regression unit test. 2016-05-18 14:03:03 +02:00
Gael Guennebaud
747e3290c0 bug #1213: rename some enums type for consistency. 2016-05-18 13:26:56 +02:00
Rasmus Munk Larsen
f519fca72b Reduce overhead for small tensors and cheap ops by short-circuiting the const computation and block size calculation in parallelFor. 2016-05-17 16:06:00 -07:00
Benoit Steiner
86ae94462e #if defined(EIGEN_USE_NONBLOCKING_THREAD_POOL) is now #if !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. 2016-05-17 14:06:15 -07:00
Benoit Steiner
997c335970 Fixed compilation error 2016-05-17 12:54:18 -07:00
Benoit Steiner
ebf6ada5ee Fixed compilation error in the tensor thread pool 2016-05-17 12:33:46 -07:00
Rasmus Munk Larsen
0bb61b04ca Merge upstream. 2016-05-17 10:26:10 -07:00
Rasmus Munk Larsen
0dbd68145f Roll back changes to core. Move include of TensorFunctors.h up to satisfy dependence in TensorCostModel.h. 2016-05-17 10:25:19 -07:00
Rasmus Larsen
00228f2506 Merged eigen/eigen into default 2016-05-17 09:49:31 -07:00
Benoit Steiner
e7e64c3277 Enable the use of the packet api to evaluate tensor broadcasts. This speed 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
 2016-05-17 09:24:35 -07:00
Benoit Steiner
5fa27574dd Allow vectorized padding on GPU. This helps speed things up a little 
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
 2016-05-17 09:17:26 -07:00
Benoit Steiner
86da77cb9b Pulled latest updates from trunk. 2016-05-17 07:21:48 -07:00
Benoit Steiner
92fc6add43 Don't rely on c++11 extension when we don't have to. 2016-05-17 07:21:22 -07:00
Benoit Steiner
2d74ef9682 Avoid float to double conversion 2016-05-17 07:20:11 -07:00