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- W2884549144 abstract "Optimized software execution on parallel computing systems demands consideration of many parameters at run-time. Determining the optimal set of parameters in a given execution context is a complex task, and therefore to address this issue researchers have proposed different approaches that use heuristic search or machine learning. In this paper, we undertake a systematic literature review to aggregate, analyze and classify the existing software optimization methods for parallel computing systems. We review approaches that use machine learning or meta-heuristics for scheduling parallel computing systems. Additionally, we discuss challenges and future research directions. The results of this study may help to better understand the state-of-the-art techniques that use machine learning and meta-heuristics to deal with the complexity of scheduling parallel computing systems. Furthermore, it may aid in understanding the limitations of existing approaches and identification of areas for improvement." @default.
- W2884549144 created "2018-08-03" @default.
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- W2884549144 date "2018-05-02" @default.
- W2884549144 modified "2023-10-01" @default.
- W2884549144 title "A Review of Machine Learning and Meta-heuristic Methods for Scheduling Parallel Computing Systems" @default.
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- W2884549144 doi "https://doi.org/10.1145/3230905.3230906" @default.
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