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Journal Articles ACM Transactions on Architecture and Code Optimization Year : 2023

Autovesk: Automatic vectorized code generation from unstructured static kernels using graph transformations

Abstract

Leveraging the SIMD capability of modern CPU architectures is mandatory to take full advantage of their increased performance. To exploit this capability, binary executables must be vectorized, either manually by developers or automatically by a tool. For this reason, the compilation research community has developed several strategies for transforming scalar code into a vectorized implementation. However, most existing automatic vectorization techniques in modern compilers are designed for regular codes, leaving irregular applications with non-contiguous data access patterns at a disadvantage. In this paper, we present a new tool, Autovesk, that automatically generates vectorized code from scalar code, specifically targeting irregular data access patterns. We describe how our method transforms a graph of scalar instructions into a vectorized one, using different heuristics to reduce the number or cost of instructions. Finally, we demonstrate the effectiveness of our approach on various computational kernels using Intel AVX-512 and ARM SVE. We compare the speedups of Autovesk vectorized code over GCC, Clang LLVM and Intel automatic vectorization optimizations. We achieve competitive results on linear kernels and up to 11x speedups on irregular kernels.
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Dates and versions

hal-03914178 , version 1 (28-12-2022)
hal-03914178 , version 2 (08-07-2023)
hal-03914178 , version 3 (04-12-2023)

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Hayfa Tayeb, Ludovic Paillat, Bérenger Bramas. Autovesk: Automatic vectorized code generation from unstructured static kernels using graph transformations. ACM Transactions on Architecture and Code Optimization, 2023, ⟨10.1145/3631709⟩. ⟨hal-03914178v3⟩
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