DPX10: An Efficient X10 Framework for Dynamic Programming Applications

Software-intensive systems in most domains, from autonomous vehicles to health, are becoming predominantly parallel to efficiently manage large amount of data in short (even real-) time. There is an incredibly rich literature on languages for parallel computing, thus it is difficult for researchers and practitioners, even experienced in this very field, to get a grasp on them. With this work we provide a comprehensive, structured, and detailed snapshot of documented research on those languages to identify trends, technical characteristics, open challenges, and research directions. In this article, we report on planning, execution, and results of our systematic peer-reviewed as well as grey literature review, which aimed at providing such a snapshot by analysing 225 studies.

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DPX10: An Efficient X10 Framework for Dynamic Programming Applications

Cited by 2 publications

References 21 publications

EasyNUSC: An Efficient Heterogeneous Computing Framework for Non-uniform Sampling Two-Dimensional Convolution Applications

EasyNUSC: An Efficient Heterogeneous Computing Framework for Non-uniform Sampling Two-Dimensional Convolution Applications

A Comprehensive Exploration of Languages for Parallel Computing

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