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2015 International Conference on Parallel Architecture and Compilation (PACT) (2015)
San Francisco, CA, USA
Oct. 18, 2015 to Oct. 21, 2015
ISSN: 1089-795X
ISBN: 978-1-4673-9524-3
pp: 432-444
SIMD vectors are widely adopted in modern general purpose processors as they can boost performance and energy efficiency for certain applications. Compiler-based automatic vectorization is one approach for generating codethat makes efficient use of the SIMD units, and has the benefit of avoiding hand development and platform-specific optimizations. The Superword-Level Parallelism (SLP) vectorization algorithm is the most well-known implementation of automatic vectorization when starting from straight-line scalar code, and is implemented in several major compilers. The existing SLP algorithm greedily packs scalar instructions into vectors starting from stores and traversing the data dependence graph upwards until it reaches loads or non-vectorizable instructions. Choosing whether to vectorize is a one-off decision for the whole graph that has been generated. This, however, is sub-optimal because the graph may contain code that is harmful to vectorization due to the need to move data from scalar registers into vectors. The decision does not consider the potential benefits of throttling the graph by removing this harmful code. In this work we propose asolution to overcome this limitation by introducing Throttled SLP (TSLP), a novel vectorization algorithm that finds the optimal graph to vectorize, forcing vectorization to stop earlier whenever this is beneficial. Our experiments show that TSLP improves performance across a number of kernels extractedfrom widely-used benchmark suites, decreasing execution time compared to SLP by 9% on average and up to 14% in the best case.
Registers, Parallel processing, Algorithm design and analysis, Program processors, Transforms, Computational modeling, Parallel architectures

V. Porpodas and T. M. Jones, "Throttling Automatic Vectorization: When Less is More," 2015 International Conference on Parallel Architecture and Compilation (PACT), San Francisco, CA, USA, 2015, pp. 432-444.
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