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path: root/unsupported/test/cxx11_tensor_thread_pool.cpp
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* Add support for asynchronous evaluation of tensor casting expressions.Gravatar Rasmus Munk Larsen2019-09-19
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* Allow move-only done callback in TensorAsyncDeviceGravatar Eugene Zhulenev2019-09-03
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* evalSubExprsIfNeededAsync + async TensorContractionThreadPoolGravatar Eugene Zhulenev2019-08-30
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* Asynchronous expression evaluation with TensorAsyncDeviceGravatar Eugene Zhulenev2019-08-30
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* Merged in ezhulenev/eigen-01 (pull request PR-514)Gravatar Rasmus Munk Larsen2018-09-28
|\ | | | | | | Add tests for evalShardedByInnerDim contraction + fix bugs
| * Add tests for evalShardedByInnerDim contraction + fix bugsGravatar Eugene Zhulenev2018-09-28
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* | Provide EIGEN_OVERRIDE and EIGEN_FINAL macros to mark virtual function overridesGravatar Christoph Hertzberg2018-09-24
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* Fix several integer conversion and sign-compare warningsGravatar Christoph Hertzberg2018-08-24
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* Fixed the tensor contraction code.Gravatar Benoit Steiner2018-08-15
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* Fixed compilation errors.Gravatar Benoit Steiner2018-08-13
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* Merged in paultucker/eigen (pull request PR-431)Gravatar Benoit Steiner2018-08-01
|\ | | | | | | | | | | Optional ThreadPoolDevice allocator Approved-by: Benoit Steiner <benoit.steiner.goog@gmail.com>
| * Add test coverage for ThreadPoolDevice optional allocator.Gravatar Paul Tucker2018-07-19
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* | Get rid of EIGEN_TEST_FUNC, unit tests must now be declared with ↵Gravatar Gael Guennebaud2018-07-17
| | | | | | | | | | | | | | | | | | EIGEN_DECLARE_TEST(mytest) { /* code */ }. This provide several advantages: - more flexibility in designing unit tests - unit tests can be glued to speed up compilation - unit tests are compiled with same predefined macros, which is a requirement for zapcc
* | Fuse computations into the Tensor contractions using output kernelGravatar Eugene Zhulenev2018-07-10
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* Avoid unnecessary conversions between floats and doublesGravatar Benoit Steiner2016-05-11
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* fixed some double-promotion and sign-compare warningsGravatar Christoph Hertzberg2016-05-11
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* Avoided unecessary type promotionGravatar Benoit Steiner2016-05-05
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* Added tests for full contractions using thread pools and gpu devices.Gravatar Benoit Steiner2016-05-05
| | | | Fixed a couple of issues in the corresponding code.
* Added a test to cover threaded tensor shufflingGravatar Benoit Steiner2016-01-30
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* Made the comparison between single and multithreaded contraction results ↵Gravatar Benoit Steiner2016-01-30
| | | | more resistant to numerical noise to prevent spurious test failures.
* Made sure to use a tensor of rank 0 to store the result of a full reduction ↵Gravatar Benoit Steiner2016-01-30
| | | | in the tensor thread pool test
* Sharded the tensor thread pool testGravatar Benoit Steiner2016-01-30
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* Protect further isnan/isfinite/isinf callsGravatar Christoph Hertzberg2015-08-16
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* Added a test for multithreaded full reductionsGravatar Benoit Steiner2015-06-30
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* Fixed a compilation warningGravatar Benoit Steiner2015-06-17
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* Moved away from std::async and std::future as the underlying mechnism for ↵Gravatar Benoit Steiner2015-05-20
| | | | | | the thread pool device. On several platforms, the functions passed to std::async are not scheduled in the order in which they are given to std::async, which leads to massive performance issues in the contraction code. Instead we now have a custom thread pool that ensures that the functions are picked up by the threads in the pool in the order in which they are enqueued in the pool.
* Fixed compilation errorGravatar Benoit Steiner2015-02-10
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* Fix clang compilationGravatar Gael Guennebaud2015-02-06
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* Created many additional testsGravatar Benoit Steiner2015-01-14
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* Misc improvements and cleanupsGravatar Benoit Steiner2014-10-13
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* Fixed the thread pool testGravatar Benoit Steiner2014-10-10
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* Fixed the threadpool testGravatar Benoit Steiner2014-06-09
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* Created additional tests for the tensor code.Gravatar Benoit Steiner2014-06-05
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* Vectorized the evaluation of tensor expression (using SSE, AVX, NEON, ...)Gravatar Benoit Steiner2014-05-16
Added the ability to parallelize the evaluation of a tensor expression over multiple cpu cores. Added the ability to offload the evaluation of a tensor expression to a GPU.