Porting HPC applications to the cloud: A multi-frontal solver case study
| Autor: | Maciej Pawlik, Kamil Figiela, Maciej Malawski, Konrad Jopek, Bartosz Balis |
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| Rok vydání: | 2017 |
| Předmět: |
General Computer Science
Computer science business.industry Heuristic Distributed computing 020206 networking & telecommunications Cloud computing 02 engineering and technology Solver Porting Theoretical Computer Science Task (computing) Workflow Modeling and Simulation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing business Scientific workflow system Frontal solver |
| Zdroj: | Journal of Computational Science. 18:106-116 |
| ISSN: | 1877-7503 |
| DOI: | 10.1016/j.jocs.2016.09.006 |
| Popis: | In this paper we argue that scientific applications traditionally considered as representing typical HPC workloads can be successfully and efficiently ported to a cloud infrastructure. We propose a porting methodology that enables parallelization of communication – and memory-intensive applications while achieving a good communication to computation ratio and a satisfactory performance in a cloud infrastructure. This methodology comprises several aspects: (1) task agglomeration heuristic enabling increasing granularity of tasks while ensuring they will fit in memory; (2) task scheduling heuristic increasing data locality; and (3) two-level storage architecture enabling in-memory storage of intermediate data. We implement this methodology in a scientific workflow system and use it to parallelize a multi-frontal solver for finite-element meshes, deploy it in a cloud, and execute it as a workflow. The results obtained from the experiments confirm that the proposed porting methodology leads to a significant reduction of communication costs and achievement of a satisfactory performance. We believe that these results constitute a valuable step toward a wider adoption of cloud infrastructures for computational science applications. |
| Databáze: | OpenAIRE |
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