Letting future programmers experience performance-related tasks

•Presentation of fine tuned assignments for HPC and parallel programming courses.•All assignments tested on students in three interconnected courses.•Covering vectorization, caches, multicore CPUs, GPUs, and multi-node problems.•Introducing 5 widely used technologies (TBB, OpenMP, CUDA, OpenMPI, and...

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Bibliographic Details
Published inJournal of parallel and distributed computing Vol. 155; pp. 74 - 86
Main Authors Bednárek, David, Kruliš, Martin, Yaghob, Jakub
Format Journal Article
LanguageEnglish
Published Elsevier Inc 01.09.2021
Subjects
Assignments
Education
GPU
HPC
Parallel computing
Assignments
Parallel computing
HPC
Education
GPU
Online AccessGet full text
ISSN0743-7315
DOI10.1016/j.jpdc.2021.04.014

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Summary:•Presentation of fine tuned assignments for HPC and parallel programming courses.•All assignments tested on students in three interconnected courses.•Covering vectorization, caches, multicore CPUs, GPUs, and multi-node problems.•Introducing 5 widely used technologies (TBB, OpenMP, CUDA, OpenMPI, and Spark). Programming courses usually focus on software-engineering problems like software decomposition and code maintenance. While computer-science lessons emphasize algorithm complexity, technological problems are usually neglected although they may significantly affect the performance in terms of wall time. As the technological problems are best explained by hands-on experience, we present a set of homework assignments focused on a range of technologies from instruction-level parallelism to GPU programming to cluster computing. These assignments are a product of a decade of development and testing on live subjects – the students of three performance-related software courses at the Faculty of Mathematics and Physics of the Charles University in Prague.
ISSN:0743-7315
DOI:10.1016/j.jpdc.2021.04.014