28279

An Asynchronous Dataflow-Driven Execution Model For Distributed Accelerator Computing

Philip Salzmann, Fabian Knorr, Peter Thoman, Philipp Gschwandtner, Biagio Cosenza, Thomas Fahringer
Distributed and Parallel Systems Group, University of Innsbruck, Austria
23rd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid), 2023

@article{salzmann2023asynchronous,

   title={An Asynchronous Dataflow-Driven Execution Model For Distributed Accelerator Computing},

   author={Salzmann, Philip and Knorr, Fabian and Thoman, Peter and Gschwandtner, Philipp and Cosenza, Biagio and Fahringer, Thomas},

   year={2023}

}

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While domain-specific HPC software packages continue to thrive and are vital to many scientific communities, a general purpose high-productivity GPU cluster programming model that facilitates experimentation for non-experts remains elusive. We demonstrate how Celerity, a high-level C++ programming model for distributed accelerator computing based on the open SYCL standard, allows for the quick development of – and experimentation with – distributed applications. To achieve scalability on large machines, we replace Celerity’s existing master/worker scheduling model with a fully distributed scheme that reduces the worst-case scheduling complexity from quadratic to linear while maintaining the existing programming interface. We then show how this declarative, data-flow based API paired with a point-to-point communication model with eager data pushing can effectively expose and leverage opportunities for latency hiding and computation/communication overlapping with minimal or no manual guidance. We demonstrate how Celerity exhibits very good scalability on multiple benchmarks from several scientific domains and up to 128 GPUs.
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