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- W207399500 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." @default.
- W207399500 created "2016-06-24" @default.
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- W207399500 date "2009-01-01" @default.
- W207399500 modified "2023-10-16" @default.
- W207399500 title "Short-Vector SIMD Parallelization in Signal Processing" @default.
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- W207399500 doi "https://doi.org/10.1007/978-1-84882-409-6_13" @default.
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