Diagnosing Highly-Parallel OpenMP Programs with Aggregated Grain Graphs

Autor:	Ananya Muddukrishna, Nico Reissmann
Jazyk:	angličtina
Rok vydání:	2018
Předmět:	Task (computing) Computer science Perspective (graphical) 0202 electrical engineering electronic engineering information engineering Parallelism (grammar) 020207 software engineering 020201 artificial intelligence & image processing 02 engineering and technology Parallel computing Graph
Zdroj:	Lecture Notes in Computer Science Euro-Par 2018: Parallel Processing ISBN: 9783319969824 Euro-Par Lecture Notes in Computer Science-Euro-Par 2018: Parallel Processing
ISSN:	0302-9743 1611-3349
Popis:	Grain graphs simplify OpenMP performance analysis by visualizing performance problems from a fork-join perspective that is familiar to programmers. However, when programmers decide to expose a high amount of parallelism by creating thousands of task and parallel for-loop chunk instances, the resulting grain graph becomes large and tedious to understand. We present an aggregation method that hierarchically groups related nodes together to reduce grain graphs of any size to one single node. This aggregated graph is then navigated by progressively uncovering groups and following visual clues that guide programmers towards problems while hiding non-problematic regions. Our approach enhances productivity by enabling programmers to understand problems in highly-parallel OpenMP programs with less effort than before. This is a post-peer-review, pre-copyedit version of an article published in [Lecture Notes in Computer Science] Locked until 1.8.2019 due to copyright restrictions. The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-96983-1_8
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d3bf31f8c646c422d2ffa52f6c90ab99 https://hdl.handle.net/11250/2560809 Zobrazit plný text záznamu