Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
A linear space algorithm for computing maximal common subsequences
Communications of the ACM
Experiment management support for performance tuning
SC '97 Proceedings of the 1997 ACM/IEEE conference on Supercomputing
Performance Optimization for Large Scale Computing: The Scalable VAMPIR Approach
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
An Algebra for Cross-Experiment Performance Analysis
ICPP '04 Proceedings of the 2004 International Conference on Parallel Processing
Understanding program performance using temporal vertical profiles
Understanding program performance using temporal vertical profiles
Open | SpeedShop: An open source infrastructure for parallel performance analysis
Scientific Programming - Large-Scale Programming Tools and Environments
Dynamic Programming
Modeling the performance of an algebraic multigrid cycle on HPC platforms
Proceedings of the international conference on Supercomputing
Introducing the open trace format (OTF)
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
Trace File Comparison with a Hierarchical Sequence Alignment Algorithm
ISPA '12 Proceedings of the 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications
Practical differential profiling
Euro-Par'07 Proceedings of the 13th international Euro-Par conference on Parallel Processing
Towards scalable event tracing for high end systems
HPCC'07 Proceedings of the Third international conference on High Performance Computing and Communications
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Due to the complexity of today's architectures and applications, performance analysis and optimization are essential, and tracebased techniques have proven to be a powerful approach. However, a manual comparison of traces is difficult and time consuming because of the large volume of detailed data and the need to correctly line up trace events. Our solution is a set of techniques that automatically align traces so they can be compared, along with novel metrics that quantify the differences between traces, both in terms of differences in the event stream and timing differences across events. Further, we introduce visualization techniques that highlight and facilitate understanding of the sources of the differences. We demonstrate the effectiveness of our solution by showing automatically detected performance and code differences across different versions of two real-world applications.