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Publication . Part of book or chapter of book . Article . 2018

Diagnosing Highly-Parallel OpenMP Programs with Aggregated Grain Graphs

Nico Reissmann; Ananya Muddukrishna;
Open Access
Published: 01 Jan 2018
Publisher: Springer International Publishing
Country: Norway
Abstract
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
Subjects by Vocabulary

Microsoft Academic Graph classification: Parallel computing Perspective (graphical) Task (computing) Graph Parallelism (grammar) Computer science

Funded by
EC| TULIPP
Project
TULIPP
Towards Ubiquitous Low-power Image Processing Platforms
  • Funder: European Commission (EC)
  • Project Code: 688403
  • Funding stream: H2020 | IA
,
EC| READEX
Project
READEX
Runtime Exploitation of Application Dynamism for Energy-efficient eXascale computing
  • Funder: European Commission (EC)
  • Project Code: 671657
  • Funding stream: H2020 | RIA
Validated by funder
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