DuctTeip: An efficient programming model for distributed task-based parallel computing

•We introduce a hierarchical task parallel programming model for distributed memory systems.•We show that the new model provides both flexibility and performance.•We use the model to implement a Cholesky factorization and a solver for the shallow water equations.•We have compared our implementation...

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Bibliographic Details
Published in:Parallel computing Vol. 90; p. 102582
Main Authors: Zafari, Afshin, Larsson, Elisabeth, Tillenius, Martin
Format: Journal Article
Language:English
Published: Elsevier B.V 01.12.2019
Subjects:
Data versioning
Distributed memory system
Hierarchical decomposition
High performance computing
Scientific computing
Task-based parallel programming
Hierarchical decomposition
Distributed memory system
Scientific computing
High performance computing
Task-based parallel programming
65Y10
65Y05
68Q10
Data versioning
ISSN:0167-8191, 1872-7336, 1872-7336
Online Access:Get full text
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Summary:•We introduce a hierarchical task parallel programming model for distributed memory systems.•We show that the new model provides both flexibility and performance.•We use the model to implement a Cholesky factorization and a solver for the shallow water equations.•We have compared our implementation with other frameworks and shown that it is competitive. Current high-performance computer systems used for scientific computing typically combine shared memory computational nodes in a distributed memory environment. Extracting high performance from these complex systems requires tailored approaches. Task-based parallel programming has been successful both in simplifying the programming and in exploiting the available hardware parallelism for shared memory systems. In this paper we focus on how to extend task-parallel programming to distributed memory systems. We use a hierarchical decomposition of tasks and data in order to accommodate the different levels of hardware. We test the proposed programming model on two different applications, a Cholesky factorization, and a solver for the Shallow Water Equations. We also compare the performance of our implementation with that of other frameworks for distributed task-parallel programming, and show that it is competitive.
ISSN:0167-8191
1872-7336
1872-7336
DOI:10.1016/j.parco.2019.102582