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Providing high‐level self‐adaptive abstractions for stream parallelism on multicores
Author(s) -
Vogel Adriano,
Griebler Dalvan,
Fernandes Luiz Gustavo
Publication year - 2021
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/spe.2948
Subject(s) - computer science , task parallelism , implicit parallelism , parallelism (grammar) , parallel computing , data parallelism , compiler , task (project management) , abstraction , set (abstract data type) , instruction level parallelism , abstraction layer , domain (mathematical analysis) , programming language , software , mathematical analysis , philosophy , mathematics , management , epistemology , economics
Stream processing applications are common computing workloads that demand parallelism to increase their performance. As in the past, parallel programming remains a difficult task for application programmers. The complexity increases when application programmers must set nonintuitive parallelism parameters, that is, the degree of parallelism. The main problem is that state‐of‐the‐art libraries use a static degree of parallelism and are not sufficiently abstracted for developing stream processing applications. In this article, we propose a self‐adaptive regulation of the degree of parallelism to provide higher‐level abstractions. Flexibility is provided to programmers with two new self‐adaptive strategies, one is for performance experts, and the other abstracts the need to set a performance goal. We evaluated our solution using compiler transformation rules to generate parallel code with the SPar domain‐specific language. The experimental results with real‐world applications highlighted higher abstraction levels without significant performance degradation in comparison to static executions. The strategy for performance experts achieved slightly higher performance than the one that works without user‐defined performance goals.