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Improving resource utilization and fault tolerance in large simulations via actors

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Abstract

Large simulations with many independent sub-simulations are common in scientific computing. There are numerous challenges, however, associated with performing such simulations in shared computing environments. For example, sub-simulations may have wildly varying completion times or not complete at all, leading to unpredictable runtimes as well as unbalanced and inefficient use of human and computational resources. In this study, we use the actor model of concurrent computation to improve both the resource utilization and fault tolerance for large-scale scientific computing simulations. More specifically, we use actors in the SUMMA model to manage a large-scale hydrological simulation over the North American continent with over 500,000 independent sub-simulations. We find that the actors implementation outperforms a standard array job submission as well as the job submission tool GNU Parallel by better balancing the computational load across processors. The actors implementation also improves fault tolerance and can eliminate the user intervention required to detect and re-submit failed jobs.

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Notes

  1. Running a large set of jobs through the Slurm workload manager [26] is typically done through the use of array jobs

  2. https://docs.alliancecan.ca/wiki/National_systems.

  3. https://www.intel.com/content/www/us/en/developer/tools/oneapi/vtune-profiler.html

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Acknowledgements

The authors would like to thank Dr. Reza Zolfaghari and Dr. Kevin R. Green for their help in the initial development of SUMMA-Actors and this paper.

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  1. Department of Computer Science, University of Saskatchewan, Saskatoon, SK, S7N 5C9, Canada

    Kyle Klenk & Raymond J. Spiteri

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  1. Kyle Klenk
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Klenk, K., Spiteri, R.J. Improving resource utilization and fault tolerance in large simulations via actors. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04318-5

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