Distributed Calculations with Algorithmic Skeletons for Heterogeneous Computing Environments

Contemporary HPC hardware typically provides several levels of parallelism, e.g. multiple nodes, each having multiple cores (possibly with vectorization) and accelerators. Efficiently programming such systems usually requires skills in combining several low-level frameworks such as MPI, OpenMP, and...

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Bibliographic Details
Published in:International journal of parallel programming Vol. 51; no. 2-3; pp. 172 - 185
Main Authors: Herrmann, Nina, Kuchen, Herbert
Format: Journal Article
Language:English
Published: New York Springer US 01.06.2023
Springer Nature B.V
Subjects:
Algorithms
C plus plus
Computer Science
Libraries
Parallel programming
Processor Architectures
Programmers
Skills
Software Engineering/Programming and Operating Systems
Special Issue on High-Level Parallel Programming and Applications (HLPP 2022)
Theory of Computation
Vector processing (computers)
Workloads
Heterogeneous computing environments
Parallel programming
Skeleton programming
Usability
High-level frameworks
ISSN:0885-7458, 1573-7640
Online Access:Get full text
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Summary:Contemporary HPC hardware typically provides several levels of parallelism, e.g. multiple nodes, each having multiple cores (possibly with vectorization) and accelerators. Efficiently programming such systems usually requires skills in combining several low-level frameworks such as MPI, OpenMP, and CUDA. This overburdens programmers without substantial parallel programming skills. One way to overcome this problem and to abstract from details of parallel programming is to use algorithmic skeletons. In the present paper, we evaluate the multi-node, multi-CPU and multi-GPU implementation of the most essential skeletons Map, Reduce, and Zip. Our main contribution is a discussion of the efficiency of using multiple parallelization levels and the consideration of which fine-tune settings should be offered to the user.
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ISSN:0885-7458
1573-7640
DOI:10.1007/s10766-022-00742-5