An Integrated Performance Visualizer for MPI/OpenMP Programs

Hoeflinger, Jay; Kuhn, Bob; Nagel, Wolfgang E.; Petersen, Paul; Rajic, Hrabri; Shah, Syed Afaq Ali; Vetter, Jeffrey S.; Voss, Michael; Woo, Renee

doi:10.1007/3-540-44587-0_5

Cited by 14 publications

(10 citation statements)

References 4 publications

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“…At step , the performance instrumentation captures and sorts the data at runtime or offline. Although users can examine the raw data immediately with a visualization tool like VGV [7] or Paraver [4], feature extraction tools and rule-based recommender systems [10,16] can support the visualization process. For example, at step , a decision tree algorithm could identify those messages that are performing abnormally [19] and identify them in the visualization with a special glyph or color.…”

Section: Introductionmentioning

confidence: 99%

“…We find that techniques such as statistical clustering offer promise for automatically extract important features from this performance counter data. These derived results can, in turn, be feed directly back to an application developer, or used as input to a more comprehensive performance analysis environment, such as a visualization [7] or an expert system [10]. …”

Section: Introductionmentioning

confidence: 99%

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Scalable Analysis Techniques for Microprocessor Performance Counter Metrics

Ahn¹,

Vetter²

2002

ACM/IEEE SC 2002 Conference (SC'02)

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Contemporary microprocessors provide a rich set of integrated performance counters that allow application developers and system architects alike the opportunity to gather important information about workload behaviors. Current techniques for analyzing data produced from these counters use raw counts, ratios, and visualization techniques help users make decisions about their application performance. While these techniques are appropriate for analyzing data from one process, they do not scale easily to new levels demanded by contemporary computing systems. Very simply, this paper addresses these concerns by evaluating several multivariate statistical techniques on these datasets. We find that several techniques, such as statistical clustering, can automatically extract important features from the data. These derived results can, in turn, be fed directly back to an application developer, or used as input to a more comprehensive performance analysis environment, such as a visualization or an expert system.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Scalable Analysis Techniques for Microprocessor Performance Counter Metrics

Ahn¹,

Vetter²

2002

ACM/IEEE SC 2002 Conference (SC'02)

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show abstract

“…There is an internal, unpublished version of VampirServer with remote control via a DBUS interface [18]. An older version was once combined with the KAU OpenMP compiler tools (Guide, GuideView) to the VGV tool [17]. OpenMP constructs were instrumented by the Guide compiler adding statistics into the trace.…”

Section: Related Workmentioning

confidence: 99%

Guided Performance Analysis Combining Profile and Trace Tools

Giménez¹,

Labarta²,

Pegenaute³

et al. 2011

Euro-Par 2010 Parallel Processing Workshops

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Abstract. Performance analysis is very important to understand the behavior of applications and to identify bottlenecks. Performance analysis tools should facilitate the exploration of the data collected and help to identify where the analyst has to look. While this functionality can promote the tools usage on small and medium size environments, it becomes mandatory for large-scale and many-core systems where the amount of data is dramatically increased. This paper proposes a new methodology based on the integration of profilers and timeline tools to improve and facilitate the performance analysis process.

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“…It has sophisticated analysis functions, including handling of multiple experiments and relating performance metrics to ideal values. VGV [7] extends GuideView to hybrid OpenMP and MPI applications. Unfortunately, neither GuideView nor VGV are open systems (now Intel proprietary), and, thus, are able to accommodate more general parallel profile data.…”

Section: Related Workmentioning

confidence: 99%

ParaProf: A Portable, Extensible, and Scalable Tool for Parallel Performance Profile Analysis

Bell

Malony

Shende

2003

Lecture Notes in Computer Science

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Abstract. This paper presents the design, implementation, and application of ParaProf, a portable, extensible, and scalable tool for parallel performance profile analysis. ParaProf attempts to offer "best of breed" capabilities to performance analysts -those inherited from a rich history of single processor profilers and those being pioneered in parallel tools research. We present ParaProf as a parallel profile analysis framework that can be retargeted and extended as required. ParaProf's design and operation is discussed, and its novel support for large-scale parallel analysis demonstrated with a 512-processor application profile generated using the TAU performance system.

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An Integrated Performance Visualizer for MPI/OpenMP Programs

Abstract: This is a preprint of a paper intended for publication in a journal or proceedings. Since changes may be made before publication, this preprint is made available with the understanding that it will not be cited or reproduced without the permission of the author.

Cited by 14 publications

References 4 publications

Scalable Analysis Techniques for Microprocessor Performance Counter Metrics

Scalable Analysis Techniques for Microprocessor Performance Counter Metrics

Guided Performance Analysis Combining Profile and Trace Tools

ParaProf: A Portable, Extensible, and Scalable Tool for Parallel Performance Profile Analysis

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