Elasticity in a Task-based Dataflow Runtime Through Inter-node GPU Work Stealing
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John, Joseph
Milthorpe, Josh
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Institute of Electrical and Electronics Engineers Inc.
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Abstract
Most contemporary HPC programming models assume an inelastic runtime in which the resources allocated to an application remain fixed throughout its execution. Conversely, elastic runtimes can expand and shrink resources based on availability and/or dynamic application requirements. In this paper, we implement elasticity for PaRSEC, a task-based dataflow runtime, using inter-node GPU work stealing. In addition to supporting elasticity, we demonstrate that inter-node GPU work stealing can enhance the performance of imbalanced applications by up to 45%.
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Proceedings of the 2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2024
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