Visualizing Hierarchical Performance Profiles of Parallel Codes Using CallFlow

Huu Tan Nguyen, Abhinav Bhatele, Nikhil Jain, Suraj P. Kesavan, Harsh Bhatia, Todd Gamblin, Kwan Liu Ma, Peer Timo Bremer

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Calling context trees (CCTs) couple performance metrics with call paths, helping understand the execution and performance of parallel programs. To identify performance bottlenecks, programmers and performance analysts visually explore CCTs to form and validate hypotheses regarding degraded performance. However, due to the complexity of parallel programs, existing visual representations do not scale to applications running on a large number of processors. We present CallFlow, an interactive visual analysis tool that provides a high-level overview of CCTs together with semantic refinement operations to progressively explore CCTs. Using a flow-based metaphor, we visualize a CCT by treating execution time as a resource spent during the call chain, and demonstrate the effectiveness of our design with case studies on large-scale, production simulation codes.

Original languageEnglish (US)
Article number8901998
Pages (from-to)2455-2468
Number of pages14
JournalIEEE Transactions on Visualization and Computer Graphics
Volume27
Issue number4
DOIs
StatePublished - Apr 1 2021

Keywords

  • coordinated and multiple views
  • hierarchical data
  • Performance analysis
  • software visualization
  • visual analytics

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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