PiPA: pipelined profiling and analysis on multi-core systems

Dynamic instrumentation systems are gaining popularity as means of constructing customized program profiling and analysis tools. However, dynamic instrumentation based analysis tools still suffer from performance problems. The overhead of such systems can be broken down into two com-ponents – the overhead of dynamic instrumentation and the time consumed in the user-defined analysis tools. While im-portant progress has been made in reducing the performance penalty of the dynamic instrumentation itself, less attention has been paid to the user-defined component. In this paper, we present PiPA – Pipelined Profiling and Analysis, which is a novel technique for parallelizing dynamic program profiling and analysis by taking advantage of multi-core systems. We implemented a prototype of PiPA using the dynamic instru-mentation system DynamoRIO. Our experiments show that PiPA is able to speed up the overall profiling and analysis tasks significantly. Compared to the more than 100x slow-down of Cachegrind and the 32x slowdown of Pin dcache, we achieved a mere 10.5x slowdown on an 8-core system