Name
Abstract | ||
|---|---|---|
Abstract 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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.jocs.2016.09.006 | Journal of Computational Science |
Keywords | Field | DocType |
HPC in the cloud,Multi-frontal direct solver,Scientific workflows,Mesh-based solver | Scientific workflow system,Locality,Heuristic,Frontal solver,Computer science,Theoretical computer science,Porting,Solver,Workflow,Cloud computing,Distributed computing | Journal |
Volume | ISSN | Citations |
18 | 1877-7503 | 4 |
PageRank | References | Authors |
0.42 | 27 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bartosz Balis | 1 | 189 | 24.14 |
| Kamil Figiela | 2 | 90 | 6.20 |
| Konrad Jopek | 3 | 25 | 4.44 |
| Maciej Malawski | 4 | 553 | 46.80 |
| Maciej Pawlik | 5 | 19 | 3.87 |