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Function Call Re-Vectorization

Abstract : Programming languages such as C for CUDA, OpenCL or ISPC have contributed to increase the programmability of SIMD accelerators and graphics processing units. However , these languages still lack the flexibility offered by low-level SIMD programming on explicit vectors. To close this expressiveness gap while preserving performance, this paper introduces the notion of Call Re-Vectorization (CREV). CREV allows changing the dimension of vectorization during the execution of a kernel, exposing it as a nested parallel kernel call. CREV affords programmability close to dynamic parallelism, a feature that allows the invocation of kernels from inside kernels, but at much lower cost. In this paper, we present a formal semantics of CREV, and an implementation of it on the ISPC compiler. We have used CREV to implement some classic algorithms, including string matching, depth first search and Bellman-Ford, with minimum effort. These algorithms, once compiled by ISPC to Intel-based vector instructions, are as fast as state-of-the-art implementations , yet much simpler. Thus, CREV gives developers the elegance of dynamic programming, and the performance of explicit SIMD programming.
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Contributor : Caroline Collange <>
Submitted on : Wednesday, October 7, 2020 - 6:31:40 PM
Last modification on : Wednesday, October 14, 2020 - 4:21:58 AM


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Rubens Moreira, Caroline Collange, Fernando Pereira. Function Call Re-Vectorization. ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), Feb 2017, Austin, Texas, United States. ⟨10.1145/3018743.3018751⟩. ⟨hal-01410186v2⟩



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