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High-performance dataflow computing in hybrid memory systems with UPC++ DepSpawn

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Abstract

Dataflow computing is a very attractive paradigm for high-performance computing, given its ability to trigger computations as soon as their inputs are available. UPC++ DepSpawn is a novel task-based library that supports this model in hybrid shared/distributed memory systems on top of a Partitioned Global Address Space environment. While the initial version of the library provided good results, it suffered from a key restriction that heavily limited its performance and scalability. Namely, each process had to consider all the tasks in the application rather than only those of interest to it, an overhead that naturally grows with both the number of processes and tasks in the system. In this paper, this restriction is lifted, enabling our library to provide higher levels of performance. This way, in experiments using 768 cores the performance improved up to 40.1%, the average improvement being 16.1%.

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Acknowledgements

This research was supported by the Ministry of Science and Innovation of Spain (TIN2016-75845-P and PID2019-104184RB-I00, AEI/FEDER/EU, 10.13039/501100011033), and by the Xunta de Galicia co-funded by the European Regional Development Fund (ERDF) under the Consolidation Programme of Competitive Reference Groups (ED431C 2017/04). We acknowledge also the support from the Centro Singular de Investigación de Galicia “CITIC”, funded by Xunta de Galicia and the European Union (European Regional Development Fund—Galicia 2014–2020 Program), by Grant ED431G 2019/01. We also acknowledge the Centro de Supercomputación de Galicia (CESGA) for the use of their computers.

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  1. Universidade da Coruña, CITIC-Research Center of Information and Communication Technologies, A Coruña, 15071, Spain

    Basilio B. Fraguela & Diego Andrade

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  1. Basilio B. Fraguela
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  2. Diego Andrade
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Correspondence to Basilio B. Fraguela.

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Fraguela, B.B., Andrade, D. High-performance dataflow computing in hybrid memory systems with UPC++ DepSpawn. J Supercomput 77, 7676–7689 (2021). https://doi.org/10.1007/s11227-020-03607-1

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