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Super-Node SLP: Optimized Vectorization for Code Sequences Containing Operators and Their Inverse Elements.

, , , , and . CGO, page 206-216. IEEE, (2019)

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Automatic parallelization of recursive functions with rewriting rules., , and . Sci. Comput. Program., (2019)Automatic Partitioning of Stencil Computations on Heterogeneous Systems., , , , and . SBAC-PAD Workshops, page 43-48. IEEE Computer Society, (2017)Super-Node SLP: Optimized Vectorization for Code Sequences Containing Operators and Their Inverse Elements., , , , and . CGO, page 206-216. IEEE, (2019)Function Merging by Sequence Alignment., , , , and . CGO, page 149-163. IEEE, (2019)VW-SLP: auto-vectorization with adaptive vector width., , and . PACT, page 12:1-12:15. ACM, (2018)Enabling efficient stencil code generation in OpenACC., , , , and . ICCS, volume 108 of Procedia Computer Science, page 2333-2337. Elsevier, (2017)An Algebraic Framework for Parallelizing Recurrence in Functional Programming., , and . SBLP, volume 9889 of Lecture Notes in Computer Science, page 140-155. Springer, (2016)Extending OpenACC for Efficient Stencil Code Generation and Execution by Skeleton Frameworks., , , , and . HPCS, page 719-726. IEEE, (2017)TOAST: Automatic tiling for iterative stencil computations on GPUs., , , and . Concurrency and Computation: Practice and Experience, (2017)Look-ahead SLP: auto-vectorization in the presence of commutative operations., , and . CGO, page 163-174. ACM, (2018)