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    The stapl Skeleton Framework

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    Type
    Book Chapter
    Authors
    Zandifar, Mani
    Thomas, Nathan
    Amato, Nancy M.
    Rauchwerger, Lawrence
    KAUST Grant Number
    KUS-C1-016-04
    Date
    2015-05-01
    Online Publication Date
    2015-05-01
    Print Publication Date
    2015
    Permanent link to this record
    http://hdl.handle.net/10754/599966
    
    Metadata
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    Abstract
    © Springer International Publishing Switzerland 2015. This paper describes the stapl Skeleton Framework, a highlevel skeletal approach for parallel programming. This framework abstracts the underlying details of data distribution and parallelism from programmers and enables them to express parallel programs as a composition of existing elementary skeletons such as map, map-reduce, scan, zip, butterfly, allreduce, alltoall and user-defined custom skeletons. Skeletons in this framework are defined as parametric data flow graphs, and their compositions are defined in terms of data flow graph compositions. Defining the composition in this manner allows dependencies between skeletons to be defined in terms of point-to-point dependencies, avoiding unnecessary global synchronizations. To show the ease of composability and expressivity, we implemented the NAS Integer Sort (IS) and Embarrassingly Parallel (EP) benchmarks using skeletons and demonstrate comparable performance to the hand-optimized reference implementations. To demonstrate scalable performance, we show a transformation which enables applications written in terms of skeletons to run on more than 100,000 cores.
    Citation
    Zandifar M, Thomas N, Amato NM, Rauchwerger L (2015) The stapl Skeleton Framework. Lecture Notes in Computer Science: 176–190. Available: http://dx.doi.org/10.1007/978-3-319-17473-0_12.
    Sponsors
    This research supported in part by NSF awards CNS-0551685, CCF-0833199, CCF-0830753, IIS-0916053, IIS-0917266, EFRI-1240483, RI-1217991, by NIH NCI R25 CA090301-11, by DOE awards DE-AC02-06CH11357, DE-NA0002376, B575363, by Samsung, Chevron, IBM, Intel, Oracle/Sun and by Award KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.
    Publisher
    Springer Nature
    Journal
    Languages and Compilers for Parallel Computing
    DOI
    10.1007/978-3-319-17473-0_12
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-319-17473-0_12
    Scopus Count
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    Publications Acknowledging KAUST Support

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