Automatically characterizing large scale program behavior
Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
An Algebra for Cross-Experiment Performance Analysis
ICPP '04 Proceedings of the 2004 International Conference on Parallel Processing
A Portable Programming Interface for Performance Evaluation on Modern Processors
International Journal of High Performance Computing Applications
An API for Runtime Code Patching
International Journal of High Performance Computing Applications
PerfExplorer: A Performance Data Mining Framework For Large-Scale Parallel Computing
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
The Tau Parallel Performance System
International Journal of High Performance Computing Applications
ACM Computing Surveys (CSUR)
Automatic analysis of speedup of MPI applications
Proceedings of the 22nd annual international conference on Supercomputing
Automatic detection of parallel applications computation phases
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Automatic Evaluation of the Computation Structure of Parallel Applications
PDCAT '09 Proceedings of the 2009 International Conference on Parallel and Distributed Computing, Applications and Technologies
Moving object segmentation using the flux tensor for biological video microscopy
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Detailed performance analysis using coarse grain sampling
Euro-Par'09 Proceedings of the 2009 international conference on Parallel processing
Performance Data Extrapolation in Parallel Codes
ICPADS '10 Proceedings of the 2010 IEEE 16th International Conference on Parallel and Distributed Systems
Unveiling Internal Evolution of Parallel Application Computation Phases
ICPP '11 Proceedings of the 2011 International Conference on Parallel Processing
Further improving the scalability of the scalasca toolset
PARA'10 Proceedings of the 10th international conference on Applied Parallel and Scientific Computing - Volume 2
A new scalable parallel DBSCAN algorithm using the disjoint-set data structure
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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Understanding the behavior of a parallel application is crucial if we are to tune it to achieve its maximum performance. Yet the behavior the application exhibits may change over time and depend on the actual execution scenario: particular inputs and program settings, the number of processes used, or hardware-specific problems. So beyond the details of a single experiment a far more interesting question arises: how does the application behavior respond to changes in the execution conditions? In this paper, we demonstrate that object tracking concepts from computer vision have huge potential to be applied in the context of performance analysis. We leverage tracking techniques to analyze how the behavior of a parallel application evolves through multiple scenarios where the execution conditions change. This method provides comprehensible insights on the influence of different parameters on the application behavior, enabling us to identify the most relevant code regions and their performance trends.