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Short-Vector SIMD Parallelization in Signal Processing

  • Rade KutilEmail author

Abstract

Short-vector Single-instruction-multiple-data (SIMD) units have become common in signal processors. Moreover, almost all modern general-purpose processors include SIMD extensions, which makes SIMD also important in high performance computing. This chapter gives an overview of approaches to the vectorization of signal processing algorithms. Despite their complexity, these algorithms have a relatively regular data flow. This regularity makes them good candidates for SIMD vectorization. They fall in two categories: filter banks that operate on streaming signal data, and Fourier-like transforms that operate on blocks of data. For the first category, simple FIR filters, IIR filters and more complicated filter banks from the field of wavelet transforms are investigated to develop and present general vectorization strategies. Well-known loop transformations as well as novel vectorization approaches are combined and evaluated. For the second category, basic approaches for the fast Fourier transform (FFT) are shown and the workings of automatic vectorizing performance tuning systems are explained. The presented solutions are tested on Intel processors with SIMD extensions and the results are compared. Wherever possible, the reasons for performance gains or losses are uncovered so that good vectorization strategies can be derived for arbitrary signal processing algorithms.

Keywords

Sequential Algorithm Lift Scheme Signal Processing Algorithm Vectorization Strategy Small Data Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Computer SciencesUniversity of SalzburgJ.-Haringer-Strasse 25020 SalzburgAustria

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