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 |
|
David Dement
|
ccc7563ac5
|
made a fix to the GMRES solver so that it now correctly reports the error achieved in the solution process
|
2016-05-16 14:26:41 -04:00 |
|
Gael Guennebaud
|
575bc44c3f
|
Fix unit test.
|
2016-05-19 22:48:16 +02:00 |
|
Gael Guennebaud
|
ccb408ee6a
|
Improve unit tests of zeta, polygamma, and digamma
|
2016-05-19 18:34:41 +02:00 |
|
Gael Guennebaud
|
6761c64d60
|
zeta and polygamma are not unary functions, but binary ones.
|
2016-05-19 18:34:16 +02:00 |
|
Gael Guennebaud
|
7a54032408
|
zeta and digamma do not require C++11/C99
|
2016-05-19 17:36:47 +02:00 |
|
Gael Guennebaud
|
ce12562710
|
Add some c++11 flags in documentation
|
2016-05-19 17:35:30 +02:00 |
|
Gael Guennebaud
|
b6ed8244b4
|
bug #1201: optimize affine*vector products
|
2016-05-19 16:09:15 +02:00 |
|
Gael Guennebaud
|
73693b5de6
|
bug #1221: disable gcc 6 warning: ignoring attributes on template argument
|
2016-05-19 15:21:53 +02:00 |
|
Gael Guennebaud
|
df9a5e13c6
|
Fix SelfAdjointEigenSolver for some input expression types, and add new regression unit tests for sparse and selfadjointview inputs.
|
2016-05-19 13:07:33 +02:00 |
|
Gael Guennebaud
|
6a2916df80
|
DiagonalWrapper is a vector, so it must expose the LinearAccessBit flag.
|
2016-05-19 13:06:21 +02:00 |
|
Gael Guennebaud
|
a226f6af6b
|
Add support for SelfAdjointView::diagonal()
|
2016-05-19 13:05:33 +02:00 |
|
Gael Guennebaud
|
ee7da3c7c5
|
Fix SelfAdjointView::triangularView for complexes.
|
2016-05-19 13:01:51 +02:00 |
|
Gael Guennebaud
|
b6b8578a67
|
bug #1230: add support for SelfadjointView::triangularView.
|
2016-05-19 11:36:38 +02:00 |
|
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 |
|
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 |
|
Benoit Steiner
|
a80d875916
|
Added missing costPerCoeff method
|
2016-05-16 09:31:10 -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
|
b789a26804
|
Fixed syntax error
|
2016-05-16 08:51:08 -07:00 |
|
Benoit Steiner
|
83dfb40f66
|
Turnon the new thread pool by default since it scales much better over multiple cores. It is still possible to revert to the old thread pool by compiling with the EIGEN_USE_SIMPLE_THREAD_POOL define.
|
2016-05-13 17:23:15 -07:00 |
|
Benoit Steiner
|
97605c7b27
|
New multithreaded contraction that doesn't rely on the thread pool to run the closure in the order in which they are enqueued. This is needed in order to switch to the new non blocking thread pool since this new thread pool can execute the closure in any order.
|
2016-05-13 17:11:29 -07:00 |
|
Benoit Steiner
|
069a0b04d7
|
Added benchmarks for contraction on CPU.
|
2016-05-13 14:32:17 -07:00 |
|
Benoit Steiner
|
c4fc8b70ec
|
Removed unnecessary thread synchronization
|
2016-05-13 10:49:38 -07:00 |
|
Benoit Steiner
|
7aa3557d31
|
Fixed compilation errors triggered by old versions of gcc
|
2016-05-12 18:59:04 -07:00 |
|
Rasmus Munk Larsen
|
5005b27fc8
|
Diasbled cost model by accident. Revert.
|
2016-05-12 16:55:21 -07:00 |
|
Rasmus Munk Larsen
|
989e419328
|
Address comments by bsteiner.
|
2016-05-12 16:54:19 -07:00 |
|
Rasmus Munk Larsen
|
e55deb21c5
|
Improvements to parallelFor.
Move some scalar functors from TensorFunctors. to Eigen core.
|
2016-05-12 14:07:22 -07:00 |
|
Benoit Steiner
|
ae9688f313
|
Worked around a compilation error triggered by nvcc when compiling a tensor concatenation kernel.
|
2016-05-12 12:06:51 -07:00 |
|