PerfExplorer: A Performance Data Mining Framework For Large-Scale Parallel Computing

Huck, Kevin; Malony, Allen D.

doi:10.1109/sc.2005.55

Cited by 79 publications

(49 citation statements)

References 15 publications

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“…It does not provide ready-to-use solutions for task-based performance analysis. One such solution based on ParaProf is PerfExplorer [11], an interactive data mining application for performance analysis. However, ParaProf's existing components and those of PerfExplorer have little overlap with the specialized ones required for OpenMP loops and tasks applications.…”

Section: Related Workmentioning

confidence: 99%

Language-Centric Performance Analysis of OpenMP Programs with Aftermath

Drebes

Bréjon

Pop

et al. 2016

OpenMP: Memory, Devices, and Tasks

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Abstract. We present a new set of tools for the language-centric performance analysis and debugging of OpenMP programs that allows programmers to relate dynamic information from parallel execution to OpenMP constructs. Users can visualize execution traces, examine aggregate metrics on parallel loops and tasks, such as load imbalance or synchronization overhead, and obtain detailed information on specific events, such as the partitioning of a loop's iteration space, its distribution to workers according to the scheduling policy and fine-grain synchronization. Our work is based on the Aftermath performance analysis tool and a ready-to-use, instrumented version of the LLVM/clang OpenMP runtime with negligible overhead for tracing. By analyzing the performance of the MG application of the NPB suite, we show that language-centric performance analysis in general and our tools in particular can help improve the performance of large-scale OpenMP applications significantly.

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Section: Related Workmentioning

confidence: 99%

Language-Centric Performance Analysis of OpenMP Programs with Aftermath

Drebes

Bréjon

Pop

et al. 2016

OpenMP: Memory, Devices, and Tasks

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“…This section will detail describe the scaling mechanism, namely the theoretical foundation of knowledge scaling [16][17][18][19].…”

Section: Theoretical Foundationmentioning

confidence: 99%

Deep Data Anaylizing Application Based on Scale Space Theory in Big Data Environment

Hu¹,

Gao²,

Xu³

2016

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-This paper introduces the basic scientific idea of the multi-scale to the field of big data analyzes, proposes a multi-scale framework of data analyzes in big data environment, present the multi-scale algorithm framework of knowledge conversion theory and apply the algorithm framework to the multi dimension association rules analysis. The proposed multi-scale association rule analysis algorithm uses the benchmark data set of analyzing results and the influence weight of benchmark data sets for target scale data sets to derived the association rules behind object scale data set, realize knowledge across scales derived and provide the possibility for multi-scale decision.

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“…Again, though, this work only supports visualizations of how the observed data relates to the application source code, and not how it relates to application semantics. Finally, many existing parallel performance tracing frameworks [7], [9], [15], [16], [17] attempt to visualize the behavior of large-scale parallel programs, either by visualizing communication between processes, by visualizing hardware metrics on a torus, or by examining communication traces using three-dimensional views. None of these, however, support the projection of application data into performance domains or vice versa, limiting their ability to pinpoint performance bottlenecks through the kind of correlation analysis presented in this paper.…”

Section: Per-core Per-phase Datamentioning

confidence: 99%

Novel views of performance data to analyze large-scale adaptive applications

Bhatelé

Gamblin

Isaacs

et al. 2012

2012 International Conference for High Performance Computing, Networking, Storage and Analysis

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Abstract-Performance analysis of parallel scientific codes is becoming increasingly difficult due to the rapidly growing complexity of applications and architectures. Existing tools fall short in providing intuitive views that facilitate the process of performance debugging and tuning. In this paper, we extend recent ideas of projecting and visualizing performance data for faster, more intuitive analysis of applications. We collect detailed per-level and per-phase measurements for a dynamically load-balanced, structured AMR library and project per-core data collected in the hardware domain on to the application's communication topology. We show how our projections and visualizations lead to a rapid diagnosis of and mitigation strategy for a previously elusive scaling bottleneck in the library that is hard to detect using conventional tools. Our new insights have resulted in a 22% performance improvement for a 65,536-core run of the AMR library on an IBM Blue Gene/P system.

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PerfExplorer: A Performance Data Mining Framework For Large-Scale Parallel Computing

Cited by 79 publications

References 15 publications

Language-Centric Performance Analysis of OpenMP Programs with Aftermath

Language-Centric Performance Analysis of OpenMP Programs with Aftermath

Deep Data Anaylizing Application Based on Scale Space Theory in Big Data Environment

Novel views of performance data to analyze large-scale adaptive applications

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