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VPA '15: Proceedings of the 2nd Workshop on Visual Performance Analysis
ACM2015 Proceeding
Publisher:
  • Association for Computing Machinery
  • New York
  • NY
  • United States
Conference:
SC15: The International Conference for High Performance Computing, Networking, Storage and Analysis Austin Texas 15 November 2015
ISBN:
978-1-4503-4013-7
Published:
15 November 2015
Sponsors:
SIGHPC, SIGARCH, IEEE-CS\DATC
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Abstract

Over the last decades an incredible amount of resources has been devoted to building ever more powerful supercomputers. However, exploiting the full capabilities of these machines is becoming exponentially more difficult with each new generation of hardware. To help understand and optimize the behavior of massively parallel simulations the performance analysis community has created a wide range of tools and APIs to collect performance data, such as flop counts, network traffic or cache behavior at the largest scale. However, this success has created a new challenge, as the resulting data is far too large and too complex to be analyzed in a straightforward manner. Therefore, new automatic analysis approaches must be developed to allow application developers to intuitively understand the multiple, interdependent effects that their algorithmic choices have on the final performance.

This workshop brings together researchers and practitioners from the areas of performance analysis, application optimization, visualization, and data analysis and provides a forum to discuss novel ideas on how to improve performance understanding, analysis and optimization through novel techniques in scientific and information visualization.

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research-article
TABARNAC: visualizing and resolving memory access issues on NUMA architectures
Article No.: 1, Pages 1–9https://doi.org/10.1145/2835238.2835239

In modern parallel architectures, memory accesses represent a common bottleneck. Thus, optimizing the way applications access the memory is an important way to improve performance and energy consumption. Memory accesses are even more important with NUMA ...

research-article
Public Access
Visualizing execution traces with task dependencies
Article No.: 2, Pages 1–8https://doi.org/10.1145/2835238.2835240

Task-based scheduling has emerged as one method to reduce the complexity of parallel computing. When using task-based schedulers, developers must frame their computation as a series of tasks with various data dependencies. The scheduler can take these ...

research-article
DAGViz: a DAG visualization tool for analyzing task-parallel program traces
Article No.: 3, Pages 1–8https://doi.org/10.1145/2835238.2835241

In task-based parallel programming, programmers can expose logical parallelism of their programs by creating fine-grained tasks at arbitrary places in their code. All other burdens in the parallel execution of these tasks such as thread management, task ...

research-article
Separating the wheat from the chaff: identifying relevant and similar performance data with visual analytics
Article No.: 4, Pages 1–8https://doi.org/10.1145/2835238.2835242

Performance-analysis tools are indispensable for understanding and optimizing the behavior of parallel programs running on increasingly powerful supercomputers. However, with size and complexity of hardware and software on the rise, performance data ...

research-article
Relating memory performance data to application domain data using an integration API
Article No.: 5, Pages 1–8https://doi.org/10.1145/2835238.2835243

Understanding performance data, and more specifically memory access pattern is essential in optimizing scientific applications. Among the various factors affecting performance, such as the hardware architecture, the algorithms, or the system software ...

Contributors
  • Lawrence Livermore National Laboratory
  • Research Center Juelich GmbH
  • The University of Utah
  • Technical University of Munich
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Acceptance Rates

VPA '15 Paper Acceptance Rate 5 of 6 submissions, 83%;
Overall Acceptance Rate 5 of 6 submissions, 83%
YearSubmittedAcceptedRate
VPA '156583%
Overall6583%