Automatic data layout transformation for heterogeneous many-core systems

Ying Yu Tseng, Yu Hao Huang, Bo-Cheng Lai, Jiun Liang Lin

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    5 Scopus citations


    Applying appropriate data structures is critical to attain superior performance in heterogeneous many-core systems. A heterogeneous many-core system is comprised of a host for control flow management, and a device for massive parallel data processing. However, the host and device require different types of data structures. The host prefers Array-of-Structures (AoS) to ease the programming, while the device requires Structure-of-Arrays (SoA) for efficient data accesses. The conflicted preferences cost excessive effort for programmers to transform the data structures between two parts. The separately designed kernels with different coding styles also cause difficulty in maintaining programs. This paper addresses this issue by proposing a fully automated data layout transformation framework. Programmers can maintain the code in AoS style on the host, while the data layout is converted into SoA when being transferred to the device. The proposed framework streamlines the design flow and demonstrates up to 177% performance improvement.

    Original languageEnglish
    Title of host publicationNetwork and Parallel Computing - 11th IFIP WG 10.3 International Conference, NPC 2014, Proceedings
    PublisherSpringer Verlag
    Number of pages12
    ISBN (Print)9783662449165
    StatePublished - 1 Jan 2014
    Event11th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2014 - Ilan, Taiwan
    Duration: 18 Sep 201420 Sep 2014

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume8707 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference11th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2014


    • GPGPU
    • data layout transformation
    • heterogeneous systems
    • many-core


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