Performance evaluation of convolution on the Cell Broadband Engine processor

Leila Ismail, Driss Guerchi

    Research output: Contribution to journalArticlepeer-review

    14 Citations (Scopus)


    Convolution represents a major computational load for many scientific and engineering applications, including seismic surface simulations and seismic imaging. Since convolution presents a heavy computational load, increasing its efficiency can significantly enhance the performance of associated applications. In this work, we present an in-depth analysis of the convolution algorithm and its complexity in order to develop adequate parallel algorithms. The implementation of these algorithms and their evaluation on the IBM Cell Broadband Engine (BE) processor reveals the gains and losses achieved by parallelizing the direct convolution. The performance results show that despite the complexity of the convolution processing, a speedup gain of at least 71.4 is obtained. The parallel vectorized algorithm requires the development effort of considering three independent vectorization strategies. Given the wide availability of Cell processors, the proposed parallelization approach can be widely adopted by any convolution-based application.

    Original languageEnglish
    Article number5445090
    Pages (from-to)337-351
    Number of pages15
    JournalIEEE Transactions on Parallel and Distributed Systems
    Issue number2
    Publication statusPublished - 2011


    • IBM Cell BE
    • Parallel computing
    • convolution
    • performance

    ASJC Scopus subject areas

    • Signal Processing
    • Hardware and Architecture
    • Computational Theory and Mathematics


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