Visualization of Repetitive Patterns in Event Traces

Performance optimization, especially in the field of HPC, is an integral part of today's software development process. One powerful way of optimizing applications is to analyze their event traces. Yet, the comparison of traces of multiple application runs is cumbersome. The impact of optimizations in the source code or the usage of different compiler flags has to be tracked manually. The challenge is to automatically identify exactly those areas that changed in the large amount of trace data.We propose a novel solution that combines sequence alignment algorithms with call graph analysis to compare and highlight traces event-wise. Our approach is able to automatically detect differences by aligning event traces. Fine-grained execution time differences can be extracted and displayed in performance charts. The results of our implementation are presented and discussed.

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“…Knüpfer et al [5] propose a solution based on call graphs. Their approach exploits redundancy in application runs.…”

Section: Related Workmentioning

confidence: 99%

Trace File Comparison with a Hierarchical Sequence Alignment Algorithm

Weber

Brendel

Brunst

2012

2012 IEEE 10th International Symposium on Parallel and Distributed Processing With Applications

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“…Visual methods are effective when the analyst prefers more direct access to the data, but they can also be coupled with automatic pattern analysis in order to produce powerful results [21]. However, many well-known visual analysis tools for parallel trace data focus on distilling information from MPI traces.…”

Section: B Data Analysis and Visualizationmentioning

confidence: 99%

A Visual Network Analysis Method for Large-Scale Parallel I/O Systems

Sigovan

Muelder

et al. 2013

2013 IEEE 27th International Symposium on Parallel and Distributed Processing

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Abstract-Parallel applications rely on I/O to load data, store end results, and protect partial results from being lost to system failure. Parallel I/O performance thus has a direct and significant impact on application performance. Because supercomputer I/O systems are large and complex, one cannot directly analyze their activity traces. While several visual or automated analysis tools for large-scale HPC log data exist, analysis research in the high-performance computing field is geared toward computation performance rather than I/O performance. Additionally, existing methods usually do not capture the network characteristics of HPC I/O systems. We present a visual analysis method for I/O trace data that takes into account the fact that HPC I/O systems can be represented as networks. We illustrate performance metrics in a way that facilitates the identification of abnormal behavior or performance problems. We demonstrate our approach on I/O traces collected from existing systems at different scales.

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“…The development of automatic pattern detection appeared to avoid the change of the source code to try to reduce trace size. Although there is development to provide intra‐process pattern detection , the combination of this with inter‐process pattern detection is also already explored . The automatic approach for trace analysis is also used to correlate the application‐level communication topology with possible performance issues .…”

Section: Related Workmentioning

confidence: 99%

Detection and analysis of resource usage anomalies in large distributed systems through multi‐scale visualization

Schnorr

Legrand

Vincent

2011

Concurrency and Computation

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International audienceUnderstanding the behavior of large scale distributed systems is generally extremely difficult as it requires to observe a very large number of components over very large time.Most analysis tools for distributed systems gather basic information such as individual processor or network utilization. Although scalable because of the data reduction techniques applied before the analysis, these tools are often insufficient to detect or fully understand anomalies in the dynamic behavior of resource utilization and their influence on the applications performance.In this paper, we propose a methodology for detecting resource usage anomalies in large scale distributed systems. The methodology relies on four functionalities: characterized trace collection, multi-scale data aggregation, specifically tailored user interaction techniques, and visualization techniques. We show the efficiency of this approach through the analysis of simulations of the volunteer computing Berkeley Open Infrastructure for Network Computing architecture. Three scenarios are analyzed in this paper: analysis of the resource sharing mechanism, resource usage considering response time instead of throughput, and the evaluation of input file size on Berkeley Open Infrastructure for Network Computing architecture. The results show that our methodology enables to easily identify resource usage anomalies, such as unfair resource sharing, contention, moving network bottlenecks, and harmful short-term resource sharing

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Visualization of Repetitive Patterns in Event Traces

Cited by 13 publications

References 6 publications

Trace File Comparison with a Hierarchical Sequence Alignment Algorithm

Trace File Comparison with a Hierarchical Sequence Alignment Algorithm

A Visual Network Analysis Method for Large-Scale Parallel I/O Systems

Detection and analysis of resource usage anomalies in large distributed systems through multi‐scale visualization

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