article

Auto-vectorization of interleaved data for SIMD

Authors:

Ayal ZaksAuthors Info & Claims

ACM SIGPLAN Notices, Volume 41, Issue 6

Pages 132 - 143

https://doi.org/10.1145/1133255.1133997

Published: 11 June 2006 Publication History

Abstract

Most implementations of the Single Instruction Multiple Data (SIMD) model available today require that data elements be packed in vector registers. Operations on disjoint vector elements are not supported directly and require explicit data reorganization manipulations. Computations on non-contiguous and especially interleaved data appear in important applications, which can greatly benefit from SIMD instructions once the data is reorganized properly. Vectorizing such computations efficiently is therefore an ambitious challenge for both programmers and vectorizing compilers. We demonstrate an automatic compilation scheme that supports effective vectorization in the presence of interleaved data with constant strides that are powers of 2, facilitating data reorganization. We demonstrate how our vectorization scheme applies to dominant SIMD architectures, and present experimental results on a wide range of key kernels, showing speedups in execution time up to 3.7 for interleaving levels (stride) as high as 8.

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Index Terms

Auto-vectorization of interleaved data for SIMD
1. Computer systems organization
  1. Architectures
    1. Parallel architectures
      1. Single instruction, multiple data
      2. Very long instruction word
    2. Serial architectures
      1. Complex instruction set computing
      2. Reduced instruction set computing
2. Software and its engineering
  1. Software notations and tools
    1. Compilers

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Published In

cover image ACM SIGPLAN Notices

ACM SIGPLAN Notices Volume 41, Issue 6

Proceedings of the 2006 PLDI Conference

June 2006

426 pages

ISSN:0362-1340

EISSN:1558-1160

DOI:10.1145/1133255

Issue’s Table of Contents

PLDI '06: Proceedings of the 27th ACM SIGPLAN Conference on Programming Language Design and Implementation
June 2006
438 pages
ISBN:1595933204
DOI:10.1145/1133981
General Chair:
Michael Schwartzbach
University of Aarhus, Denmark
,
Program Chair:
Thomas Ball
Microsoft Research

Copyright © 2006 ACM.

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Association for Computing Machinery

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Publication History

Published: 11 June 2006

Published in SIGPLAN Volume 41, Issue 6

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https://dl.acm.org/doi/10.1109/SC41406.2024.00021
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He YMarkidis S(2024)High-Performance FFT Code Generation via MLIR Linalg Dialect and SIMD Micro-Kernels2024 IEEE International Conference on Cluster Computing (CLUSTER)10.1109/CLUSTER59578.2024.00021(155-165)Online publication date: 24-Sep-2024
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