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Exploring the Impact of Node Failures on the Resource Allocation for Parallel Jobs

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Euro-Par 2021: Parallel Processing Workshops (Euro-Par 2021)

Abstract

Increasing the size and complexity of modern HPC systems also increases the probability of various types of failures. Failures may disrupt application execution and waste valuable system resources due to failed executions. In this work, we explore the effect of node failures on the completion times of MPI parallel jobs. We introduce a simulation environment that generates synthetic traces of node failures, assuming that the times between failures for each node are independently distributed, following the same distribution but with different parameters. To highlight the importance of failure-awareness for resource allocation, we compare two failure-oblivious resource allocation approaches with one that considers node failure probabilities before assigning a partition to a job: a heuristic that randomly selects the partition for a job, and Slurm’s linear resource allocation policy. We present results for a case study that assumes a 4D-torus topology and a Weibull distribution for each node’s time between failures, and considers several different traces of node failures, capturing different failure patterns. For the synthetic traces explored, the benefit is more prominent for longer jobs, up to 82% depending on the trace, when compared with Slurm and a failure-oblivious heuristic. For shorter jobs, benefits are noticeable for systems with more frequent failures.

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Acknowledgments

This research has received funding from the European Union’s Horizon 2020/EuroHPC research and innovation programme under grant agreements 955606 (DEEP-SEA) and 754337 (EuroEXA). National contributions from the involved state members match the EuroHPC funding.

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Correspondence to Ioannis Vardas .

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Vardas, I., Ploumidis, M., Marazakis, M. (2022). Exploring the Impact of Node Failures on the Resource Allocation for Parallel Jobs. In: Chaves, R., et al. Euro-Par 2021: Parallel Processing Workshops. Euro-Par 2021. Lecture Notes in Computer Science, vol 13098. Springer, Cham. https://doi.org/10.1007/978-3-031-06156-1_24

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  • DOI: https://doi.org/10.1007/978-3-031-06156-1_24

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