| Commit message (Collapse) | Author | Age |
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module required to run it on devices supporting SYCL.
* Abstracting the pointer type so that both SYCL memory and pointer can be captured.
* Converting SYCL virtual pointer to SYCL device memory in Eigen evaluator class.
* Binding SYCL placeholder accessor to command group handler by using bind method in Eigen evaluator node.
* Adding SYCL macro for controlling loop unrolling.
* Modifying the TensorDeviceSycl.h and SYCL executor method to adopt the above changes.
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evaluators
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here should be based on CoeffreturnType not the Scalar type. Therefore, Similar to reduction and evalTo function, it should have its own MakePointer class. In this case, for other device the type is defaulted to CoeffReturnType and no changes is required on users' code. However, in SYCL, on the device, we can recunstruct the device Type.
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codeplaysoftware/eigen-upstream-pure/separating_internal_memory_allocation (pull request PR-446)
Distinguishing between internal memory allocation/deallocation from explicit user memory allocation/deallocation.
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user memory allocation/deallocation.
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DataDependancy
* Wrapping data type to the pointer class for sycl in non-terminal nodes; not having that breaks Tensorflow Conv2d code.
* Applying Ronnan's Comments.
* Applying benoit's comments
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issue on sycl when the rhs is TensorContraction, reduction or convolution; Fixing the partial modification for memset when sycl backend is used.
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estimate the cost of evaluating tensor expressions.
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All the vectorization is now defined in the tensor evaluators. This will make it possible to relialably support devices with different packet types in the same compilation unit.
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the evaluation of an expression.
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