Traveler: Navigating Task Parallel Traces for Performance Analysis

Understanding the behavior of software in execution is a key step in identifying and fixing performance issues. This is especially important in high performance computing contexts where even minor performance tweaks can translate into large savings in terms of computational resource use. To aid perf...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics Vol. 29; no. 1; pp. 1 - 10
Main Authors: Sakin, Sayef Azad, Bigelow, Alex, Tohid, R., Scully-Allison, Connor, Scheidegger, Carlos, Brandt, Steven R., Taylor, Christopher, Huck, Kevin A., Kaiser, Hartmut, Isaacs, Katherine E.
Format: Journal Article
Language:English
Published: United States IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Arrays
Codes
Computation
Data collection
Data visualization
Design analysis
event sequence visualization
High performance computing
Iterative methods
Measurement
Navigation
parallel computing
Parallel programming
Performance analysis
Software engineering
Software visualization
Task analysis
traces
Visualization
ISSN:1077-2626, 1941-0506, 1941-0506
Online Access:Get full text
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Summary:Understanding the behavior of software in execution is a key step in identifying and fixing performance issues. This is especially important in high performance computing contexts where even minor performance tweaks can translate into large savings in terms of computational resource use. To aid performance analysis, developers may collect an execution trace -a chronological log of program activity during execution. As traces represent the full history, developers can discover a wide array of possibly previously unknown performance issues, making them an important artifact for exploratory performance analysis. However, interactive trace visualization is difficult due to issues of data size and complexity of meaning. Traces represent nanosecond-level events across many parallel processes, meaning the collected data is often large and difficult to explore. The rise of asynchronous task parallel programming paradigms complicates the relation between events and their probable cause. To address these challenges, we conduct a continuing design study in collaboration with high performance computing researchers. We develop diverse and hierarchical ways to navigate and represent execution trace data in support of their trace analysis tasks. Through an iterative design process, we developed Traveler , an integrated visualization platform for task parallel traces. Traveler provides multiple linked interfaces to help navigate trace data from multiple contexts. We evaluate the utility of Traveler through feedback from users and a case study, finding that integrating multiple modes of navigation in our design supported performance analysis tasks and led to the discovery of previously unknown behavior in a distributed array library.
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USDOE
None
SC0022044
ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2022.3209375