NDLI: Automatic SIMD vectorization of fast fourier transforms for the larrabee and AVX instruction sets

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Automatic SIMD vectorization of fast fourier transforms for the larrabee and AVX instruction sets

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Automatic SIMD vectorization of fast fourier transforms for the larrabee and AVX instruction sets

Content Provider	ACM Digital Library
Author	McFarlin, Daniel S. Franchetti, Franz Arbatov, Volodymyr Püschel, Markus
Abstract	The well-known shift to parallelism in CPUs is often associated with multicores. However another trend is equally salient: the increasing parallelism in per-core single-instruction multiple-date (SIMD) vector units. Intel's SSE and IBM's VMX (compatible to AltiVec) both offer 4-way (single precision) floating point, but the recent Intel instruction sets AVX and Larrabee (LRB) offer 8-way and 16-way, respectively. Compilation and optimization for vector extensions is hard, and often the achievable speed-up by using vectorizing compilers is small compared to hand-optimization using intrinsic function interfaces. Unfortunately, the complexity of these intrinsics interfaces increases considerably with the vector length, making hand-optimization a nightmare. In this paper, we present a peephole-based vectorization system that takes as input the vector instruction semantics and outputs a library of basic data reorganization blocks such as small transpositions and perfect shuffles that are needed in a variety of high performance computing applications. We evaluate the system by generating the blocks needed by the program generator Spiral for vectorized fast Fourier transforms (FFTs). With the generated FFTs we achieve a vectorization speed-up of 5.5--6.5 for 8-way AVX and 10--12.5 for 16-way LRB. For the latter instruction counts are used since no timing information is available. The combination of the proposed system and Spiral thus automates the production of high performance FFTs for current and future vector architectures.
Starting Page	265
Ending Page	274
Page Count	10
File Format	PDF
ISBN	9781450301022
DOI	10.1145/1995896.1995938
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-05-31
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Autovectorization Fourier transform Program generation Simd Super-optimization
Content Type	Text
Resource Type	Article

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