Efficient flow profiling for detecting performance bugs

Abstract

Performance issues in large applications arise only in particular scenarios under heavy load conditions. It is therefore difficult to catch them during testing and they easily escape into production. This necessitates the design of a common and efficient instrumentation strategy that profiles the flow of objects during an execution. Designing such a strategy which enables profile generation precisely with low overhead is non-trivial due to the number of objects created, accessed and paths traversed by them in an execution. In this paper, we design and implement an efficient instrumentation technique that efficiently generates object flow profiles for Java programs, without requiring any modifications to the underlying virtual machine. We achieve this by applying Ball-Larus numbering on a specialized hybrid flow graph (hfg). The hfg path profiles that are collected during runtime are post-processed offline to derive the object flow profiles. We implemented the profiler and validated its efficacy by applying it on Java programs. The results demonstrate the scalability of our approach, which handles 0.2M to 0.55B object accesses with an average runtime overhead of 8x. We also demonstrate the effectiveness of the generated profiles by implementing a client analysis that consumes the profiles to detect performance bugs. The analysis detects 38 performance bugs which when refactored result in significant performance gains (up to 30%) in running times.