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MemAxes: Visualization and Analytics for Characterizing Complex Memory Performance Behaviors
University of California, USA.
Lawrence Livermore National Laboratory, USA.
Linnaeus University, Faculty of Technology, Department of Media Technology.ORCID iD: 0000-0001-6745-4398
Lawrence Livermore National Laboratory, USA.
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2017 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506Article in journal (Refereed) Epub ahead of print
Abstract [en]

Memory performance is often a major bottleneck for high-performance computing (HPC) applications. Deepening memory hierarchies, complex memory management, and non-uniform access times have made memory performance behavior difficult to characterize, and users require novel, sophisticated tools to analyze and optimize this aspect of their codes. Existing tools target only specific factors of memory performance, such as hardware layout, allocations, or access instructions. However, today's tools do not suffice to characterize the complex relationships between these factors. Further, they require advanced expertise to be used effectively. We present MemAxes, a tool based on a novel approach for analytic-driven visualization of memory performance data. MemAxes uniquely allows users to analyze the different aspects related to memory performance by providing multiple visual contexts for a centralized dataset. We define mappings of sampled memory access data to new and existing visual metaphors, each of which enabling a user to perform different analysis tasks. We present methods to guide user interaction by scoring subsets of the data based on known performance problems. This scoring is used to provide visual cues and automatically extract clusters of interest. We designed MemAxes in collaboration with experts in HPC and demonstrate its effectiveness in case studies.

Place, publisher, year, edition, pages
2017.
Keyword [en]
Performance Visualization, High-Performance Computing, Memory Visualization
National Category
Computer Science
Research subject
Computer and Information Sciences Computer Science, Computer Science; Computer Science, Information and software visualization
Identifiers
URN: urn:nbn:se:lnu:diva-65915DOI: 10.1109/TVCG.2017.2718532OAI: oai:DiVA.org:lnu-65915DiVA: diva2:1116917
Available from: 2017-06-28 Created: 2017-06-28 Last updated: 2017-06-28

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CiteExportLink to record
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Citation style
  • apa
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  • modern-language-association-8th-edition
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