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Composing Low-Overhead Scheduling Strategies for Improving Performance of Scientific Applications

Part of the Lecture Notes in Computer Science book series (LNPSE,volume 9342)

Abstract

Many different sources of overheads impact the efficiency of a scheduling strategy applied to a parallel loop within a scientific application. In prior work, we handled these overheads using multiple loop scheduling strategies, with each scheduling strategy focusing on mitigating a subset of the overheads. However, mitigating the impact of one source of overhead can lead to an increase in the impact of another source of overhead, and vice versa. In this work, we show that in order to improve efficiency of loop scheduling strategies, one must adapt the loop scheduling strategies so as to handle all overheads simultaneously. To show this, we describe a composition of our existing loop scheduling strategies, and experiment with the composed scheduling strategy on standard benchmarks and application codes. Applying the composed scheduling strategy to three MPI+OpenMP scientific codes run on a cluster of SMPs improves performance an average of 31 % over standard OpenMP static scheduling.

Keywords

  • Schedule Strategy
  • Dynamic Fraction
  • Performance Loss
  • Static Fraction
  • Loop Iteration

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Acknowledgements

This material is based in part upon work supported by the Department of Energy, National Nuclear Security Administration, under Award Number DE-NA0002374. This work was supported in part by the Office of Advanced Scientific Computing Research, Office of Science, U.S. Department of Energy award DE-FG02-13ER26138/DE-SC0010049.

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Correspondence to Vivek Kale .

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Kale, V., Gropp, W.D. (2015). Composing Low-Overhead Scheduling Strategies for Improving Performance of Scientific Applications. In: Terboven, C., de Supinski, B., Reble, P., Chapman, B., Müller, M. (eds) OpenMP: Heterogenous Execution and Data Movements. IWOMP 2015. Lecture Notes in Computer Science(), vol 9342. Springer, Cham. https://doi.org/10.1007/978-3-319-24595-9_2

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  • DOI: https://doi.org/10.1007/978-3-319-24595-9_2

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