Exploiting hardware performance counters with flow and context sensitive profiling
Proceedings of the ACM SIGPLAN 1997 conference on Programming language design and implementation
Adaptive online context-sensitive inlining
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
Fast, accurate call graph profiling
Software—Practice & Experience
Low-overhead call path profiling of unmodified, optimized code
Proceedings of the 19th annual international conference on Supercomputing
Accurate, efficient, and adaptive calling context profiling
Proceedings of the 2006 ACM SIGPLAN conference on Programming language design and implementation
On the Prediction of Java Object Lifetimes
IEEE Transactions on Computers
The DaCapo benchmarks: java benchmarking development and analysis
Proceedings of the 21st annual ACM SIGPLAN conference on Object-oriented programming systems, languages, and applications
Proceedings of the 22nd annual ACM SIGPLAN conference on Object-oriented programming systems and applications
Mining hot calling contexts in small space
Proceedings of the 32nd ACM SIGPLAN conference on Programming language design and implementation
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
DIME: time-aware dynamic binary instrumentation using rate-based resource allocation
Proceedings of the Eleventh ACM International Conference on Embedded Software
Efficient and accurate stack trace sampling in the Java hotspot virtual machine
Proceedings of the 5th ACM/SPEC international conference on Performance engineering
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We present an approach for building calling context information useful for program understanding, performance analysis and optimizations. Our approach exploits a lightweight profiling mechanism providing partial call traces. The goal is to reconstruct calling context information as accurately as possible, and to help the user navigate through it.We propose three steps to merge partial call traces into a smaller number of partial calling context trees. We intend to minimize errors such that the final partial contexts represent actual components of the real calling context tree with a very high probability. The first step concatenates call traces based on their common sequences. The second step converts call traces into partial calling context trees, and the last step merges partial context trees through maximal matching. To gauge how well the merged trees represent the full calling context tree, several criteria are presented. Our results indicate that call traces are successfully merged into a small number of large calling context trees. The merged trees are highly accurate.We have also developed a semi-automatic tool to navigate across partial calling context trees for program understanding and performance analysis purposes. Our results for several Java benchmarks show that our merging strategies exhibit a maximum 1% inaccuracy when compared to the exact solution.