DiP: A Parallel Program Development Environment
Euro-Par '96 Proceedings of the Second International Euro-Par Conference on Parallel Processing-Volume II
ASPLOS XI Proceedings of the 11th international conference on Architectural support for programming languages and operating systems
High Performance Event Trace Visualization
PDP '05 Proceedings of the 13th Euromicro Conference on Parallel, Distributed and Network-Based Processing
On-line automated performance diagnosis on thousands of processes
Proceedings of the eleventh ACM SIGPLAN symposium on Principles and practice of parallel programming
Wavelet-based phase classification
Proceedings of the 15th international conference on Parallel architectures and compilation techniques
Discovering and Exploiting Program Phases
IEEE Micro
Scalable parallel trace-based performance analysis
EuroPVM/MPI'06 Proceedings of the 13th European PVM/MPI User's Group conference on Recent advances in parallel virtual machine and message passing interface
Automatic structure extraction from MPI applications tracefiles
Euro-Par'07 Proceedings of the 13th international Euro-Par conference on Parallel Processing
Scalable load-balance measurement for SPMD codes
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Guided performance analysis combining profile and trace tools
Euro-Par 2010 Proceedings of the 2010 conference on Parallel processing
Extracting the optimal sampling frequency of applications using spectral analysis
Concurrency and Computation: Practice & Experience
On the usefulness of object tracking techniques in performance analysis
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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The intricacy of high performance computing applications has been growing very fast in the last years. Only skilled analysts are able to determine the factors that are undermining the performance of up-to-date applications. Analyst time is a very expensive resource and, for that reason, a strong effort to develop automatic performance analysis methodologies has been made by the scientific community. In this paper, we propose a methodology that is able to automatically detect the main performance problems of applications. This methodology is based on, first, a size reduction of the performance data obtained from the executions and, second, an analytical model obtained from this performance data which fits the speedup of the applications in terms of several parameters related to several performance issues. The paper also shows results obtained from real up-to-date applications and validates the conclusions automatically derived from the methodology.