KAUST Grant NumberKUS-C1-016-04
Online Publication Date2015-05-01
Print Publication Date2015
Permanent link to this recordhttp://hdl.handle.net/10754/599966
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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.
CitationZandifar 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.
SponsorsThis 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.