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Author SHA1 Message Date
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
Benoit Steiner
2a54b70d45 Fixed potential race condition in the non blocking thread pool 2016-05-12 11:45:48 -07:00
Benoit Steiner
a071629fec Replace implicit cast with an explicit one 2016-05-12 10:40:07 -07:00
Benoit Steiner
2f9401b061 Worked around compilation errors with older versions of gcc 2016-05-11 23:39:20 -07:00
Benoit Steiner
09653e1f82 Improved the portability of the tensor code 2016-05-11 23:29:09 -07:00
Benoit Steiner
fae0493f98 Fixed a couple of bugs related to the Pascalfamily of GPUs 
H: Enter commit message.  Lines beginning with 'HG:' are removed.
 2016-05-11 23:02:26 -07:00
Benoit Steiner
886445ce4d Avoid unnecessary conversions between floats and doubles 2016-05-11 23:00:03 -07:00
Benoit Steiner
595e890391 Added more tests for half floats 2016-05-11 21:27:15 -07:00
Benoit Steiner
b6a517c47d Added the ability to load fp16 using the texture path. 
Improved the performance of some reductions on fp16
 2016-05-11 21:26:48 -07:00
Benoit Steiner
518149e868 Misc fixes for fp16 2016-05-11 20:11:14 -07:00
Benoit Steiner
56a1757d74 Made predux_min and predux_max on fp16 less noisy 2016-05-11 17:37:34 -07:00
Benoit Steiner
9091351dbe __ldg is only available with cuda architectures >= 3.5 2016-05-11 15:22:13 -07:00
Benoit Steiner
02f76dae2d Fixed a typo 2016-05-11 15:08:38 -07:00
Christoph Hertzberg
131e5a1a4a Do not copy for trivial 1x1 case. This also avoids a "maybe-uninitialized" warning in some situations. 2016-05-11 23:50:13 +02:00
Benoit Steiner
70195a5ff7 Added missing EIGEN_DEVICE_FUNC 2016-05-11 14:10:09 -07:00
Benoit Steiner
09a19c33a8 Added missing EIGEN_DEVICE_FUNC qualifiers 2016-05-11 14:07:43 -07:00
Christoph Hertzberg
1a1ce6ff61 Removed deprecated flag (which apparently was ignored anyway) 2016-05-11 23:05:37 +02:00
Christoph Hertzberg
2150f13d65 fixed some double-promotion and sign-compare warnings 2016-05-11 23:02:26 +02:00
Christoph Hertzberg
7268b10203 Split unit test 2016-05-11 19:41:53 +02:00
Christoph Hertzberg
8d4ef391b0 Don't flood test output with successful VERIFY_IS_NOT_EQUAL tests. 2016-05-11 19:40:45 +02:00
Christoph Hertzberg
bda21407dd Fix help output of buildtests and check scripts 2016-05-11 19:39:09 +02:00
Christoph Hertzberg
33ca7e3c8d bug #1207: Add and fix logical-op warnings 2016-05-11 19:36:34 +02:00
Benoit Steiner
217d984abc Fixed a typo in my previous commit 2016-05-11 10:22:15 -07:00
Benoit Steiner
08348b4e48 Fix potential race condition in the CUDA reduction code. 2016-05-11 10:08:51 -07:00
Benoit Steiner
cbb14ed47e Added a few tests to validate the generation of random tensors on GPU. 2016-05-11 10:05:56 -07:00
Benoit Steiner
6a5717dc74 Explicitely initialize all the atomic variables. 2016-05-11 10:04:41 -07:00