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- W291873105 abstract "Data intensive workloads have become a popular use of HPC in recent years and the question of how data scientists, who might not be HPC experts, can effectively program these machines is important to address. Whilst using models such as Partitioned Global Address Space (PGAS) is attractive from a simplicity point of view, the abstractions that these impose upon the programmer can impact performance. We propose an approach, type-oriented programming, where all aspects of parallelism are encoded via types and the type system which allows for the programmer to write simple PGAS data intensive HPC codes and then, if they so wish, tune the fundamental aspects by modifying type information. This paper considers the suitability of using type-oriented programming, with the PGAS memory model, in data intensive workloads. We compare a type-oriented implementation of the Graph500 benchmark against MPI reference implementations both in terms of programmability and performance, and evaluate how orienting their parallel codes around types can assist in the data intensive HPC field." @default.
- W291873105 created "2016-06-24" @default.
- W291873105 creator A5046877321 @default.
- W291873105 date "2014-01-01" @default.
- W291873105 modified "2023-09-25" @default.
- W291873105 title "A Type-Oriented Graph500 Benchmark" @default.
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- W291873105 doi "https://doi.org/10.1007/978-3-319-07518-1_31" @default.
- W291873105 hasPublicationYear "2014" @default.
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