| Commit message (Collapse) | Author | Age |
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* Force-inline implementations. They pass around pointers to shared memory
blocks. Without inlining compiler must operate via generic pointers.
Inlining allows compiler to detect that we're operating on shared memory
which allows generation of substantially faster code.
* Fixed a long-standing typo which resulted in launching 8x more kernels
than we needed (.z dimension of the block is unused by the kernel).
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branch.
* Unifying all loadLocalTile from lhs and rhs to an extract_block function.
* Adding get_tensor operation which was missing in TensorContractionMapper.
* Adding the -D method missing from cmake for Disable_Skinny Contraction operation.
* Wrapping all the indices in TensorScanSycl into Scan parameter struct.
* Fixing typo in Device SYCL
* Unifying load to private register for tall/skinny no shared
* Unifying load to vector tile for tensor-vector/vector-tensor operation
* Removing all the LHS/RHS class for extracting data from global
* Removing Outputfunction from TensorContractionSkinnyNoshared.
* Combining the local memory version of tall/skinny and normal tensor contraction into one kernel.
* Combining the no-local memory version of tall/skinny and normal tensor contraction into one kernel.
* Combining General Tensor-Vector and VectorTensor contraction into one kernel.
* Making double buffering optional for Tensor contraction when local memory is version is used.
* Modifying benchmark to accept custom Reduction Sizes
* Disabling AVX optimization for SYCL backend on the host to allow SSE optimization to the host
* Adding Test for SYCL
* Modifying SYCL CMake
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device.deallocate() / evaluator.cleanup() complete, since the device might be destroyed after on_done() runs.
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Add a new EIGEN_HAS_INTRINSIC_INT128 macro, and use this instead of __SIZEOF_INT128__. This fixes related issues with TensorIntDiv.h when building with Clang for Windows, where support for 128-bit integer arithmetic is advertised but broken in practice.
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https://bitbucket.org/eigen/eigen/commits/668ab3fc474e54c7919eda4fbaf11f3a99246494
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std::array is still not supported in CUDA device code on Windows.
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* The specialization of array class in the different namespace for GCC<=6.4
* The implicit call to `std::array` constructor using the initializer list for GCC <=6.1
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Fix for the HIP build+test errors.
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Add async evaluation to a number of ops.
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The errors were introduced by this commit :
After the above mentioned commit, some of the tests started failing with the following error
```
Built target cxx11_tensor_reduction
Building HIPCC object unsupported/test/CMakeFiles/cxx11_tensor_reduction_gpu_5.dir/cxx11_tensor_reduction_gpu_5_generated_cxx11_tensor_reduction_gpu.cu.o
In file included from /home/rocm-user/eigen/unsupported/test/cxx11_tensor_reduction_gpu.cu:16:
In file included from /home/rocm-user/eigen/unsupported/Eigen/CXX11/Tensor:117:
/home/rocm-user/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorBlockV2.h:155:5: error: the field type is not amp-compatible
DestinationBufferKind m_kind;
^
/home/rocm-user/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorBlockV2.h:211:3: error: the field type is not amp-compatible
DestinationBuffer m_destination;
^
```
For some reason HIPCC does not like device code to contain enum types which do not have the base-type explicitly declared. The fix is trivial, explicitly state "int" as the basetype
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c++11 functionality with older compilers.
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reverse op
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TensorSlicing
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553caeb6a3bb545aef895f8fc9f219be44679017
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- Split SpecialFunctions files in to a separate BesselFunctions file.
In particular add:
- Modified bessel functions of the second kind k0, k1, k0e, k1e
- Bessel functions of the first kind j0, j1
- Bessel functions of the second kind y0, y1
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