Data-Flow/Dependence Profiling for Structured Transformations

Abstract : Profiling feedback is an important technique used by developers for performance debugging, where it is usually used to pinpoint performance bottlenecks and also to find optimization opportunities. Assessing the validity and potential benefit of a program transformation requires accurate knowledge of the data flow and dependencies, which can be uncovered by profiling a particular execution of the program. In this work we develop poly-prof, an end-to-end infrastructure for dynamic binary analysis, which produces feedback about the potential to apply complex program rescheduling. Our tool can handle both inter-and intraproce-dural aspects of the program in a unified way, thus providing interprocedural transformation feedback.
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Communication dans un congrès
PPoPP 2019 - 24th Symposium on Principles and Practice of Parallel Programming, Feb 2019, Washington, D.C., United States. ACM, pp.173-185, 〈10.1145/3293883.3295737〉
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Contributeur : Fabian Gruber <>
Soumis le : jeudi 7 mars 2019 - 16:15:34
Dernière modification le : vendredi 8 mars 2019 - 08:41:57

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Fabian Gruber, Manuel Selva, Diogo Sampaio, Christophe Guillon, Antoine Moynault, et al.. Data-Flow/Dependence Profiling for Structured Transformations. PPoPP 2019 - 24th Symposium on Principles and Practice of Parallel Programming, Feb 2019, Washington, D.C., United States. ACM, pp.173-185, 〈10.1145/3293883.3295737〉. 〈hal-02060796〉

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