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Vapro: performance variance detection and diagnosis for production-run parallel applications

Liyan Zheng, Jidong Zhai, Xiongchao Tang, Haojie Wang, Teng Yu, Yuyang Jin, Shuaiwen Leon Song, Wenguang Chen

Abstract

Performance variance is a serious problem for parallel applications, which can cause performance degradation and make applications' behavior hard to understand. Therefore, detecting and diagnosing performance variance are of crucial importance for users and application developers. However, previous detection approaches either bring too large overhead and hurt applications' performance, or rely on nontrivial source code analysis that is impractical for production-run parallel applications.

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