Towards data-flow parallelization for adaptive mesh refinement applications
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
European Commission's projectDEEP-EST - DEEP (EC-H2020-754304)
Adaptive Mesh Refinement (AMR) is a prevalent method used by distributed-memory simulation applications to adapt the accuracy of their solutions depending on the turbulent conditions in each of their domain regions. These applications are usually dynamic since their domain areas are refined or coarsened in various refinement stages during their execution. Thus, they periodically redistribute their workloads among processes to avoid load imbalance. Although the defacto standard for scientific computing in distributed environments is MPI, in recent years, pure MPI applications are being ported to hybrid ones, attempting to cope with modern multi-core systems. Recently, the Task-Aware MPI library was proposed to efficiently integrate MPI communications and tasking models, providing also the transparent management of communications issued by tasks. In this paper, we demonstrate the benefits of porting AMR applications to data-flow programming models leveraging that novel hybrid approach. We exploit most of the application parallelism by taskifying all stages, allowing their natural overlap. We employ these techniques on the miniAMR proxy application, which mimics the refinement, load balancing, communication, and computation patterns of general AMR applications. We evaluate how this approach reduces the time in its computation and communication phases while achieving better programmability than other conventional hybrid techniques.
CitationSala, K.; Rico, A.; Beltran, V. Towards data-flow parallelization for adaptive mesh refinement applications. A: IEEE International Conference on Cluster Computing. "2020 IEEE International Conference on Cluster Computing: 14–17 September 2020, Kobe, Japan: proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 314-325. ISBN 978-1-7281-6677-3. DOI 10.1109/CLUSTER49012.2020.00042.
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