An approach to task-based parallel programming for undergraduate students

This paper presents the description of a compulsory parallel programming course in the bachelor degree in Informatics Engineering at the Barcelona School of Informatics, Universitat Politècnica de Catalunya UPC-BarcelonaTech. The main focus of the course is on the shared-memory programming paradigm,...

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Veröffentlicht in:Journal of parallel and distributed computing 2018-08, Vol.118, p.140-156
Hauptverfasser: Ayguadé, Eduard, Jiménez-González, Daniel
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Sprache:eng
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Zusammenfassung:This paper presents the description of a compulsory parallel programming course in the bachelor degree in Informatics Engineering at the Barcelona School of Informatics, Universitat Politècnica de Catalunya UPC-BarcelonaTech. The main focus of the course is on the shared-memory programming paradigm, which facilitates the presentation of fundamental aspects and notions of parallel computing. Unlike the “traditional” loop-based approach, which is the focus of parallel programming courses in other universities, this course presents the parallel programming concepts using a task-based approach. Tasking allows students to explore a broader set of parallel decomposition strategies, including linear, iterative and recursive strategies, and their implementation using the current version of OpenMP (OpenMP 4.5), which offers mechanisms (pragmas and intrinsic functions) to easily map these strategies into parallel programs. Simple models to understand the benefits of a task decomposition and the trade-offs introduced by different kinds of overheads are included in the course, together with the use of tools that allow an easy exploration of different task decomposition strategies and their potential parallelism (Tareador) and instrumentation and analysis of task parallel executions on real machines (Extrae and Paraver). •Proposal of contents for a parallel programming course for undergraduates.•Use of tasking model in OpenMP to express task decomposition strategies.•Simple performance models to understand the potential of task decomposition strategies.
ISSN:0743-7315
1096-0848
DOI:10.1016/j.jpdc.2018.02.022