Adaptive Path Profiling Using Arithmetic Coding

Abstract

Path profiling, which aims to trace a program's execution path, has been widely adopted in various areas such as record and replay, program optimizations, performance diagnosis, and etc. Many path profiling approaches have been proposed in the literature, including B.L. algorithm, and PAP. Unfortunately, both approaches suffer from large tracing overhead for representing long execution paths. In this paper, we propose AdapTracer, a path profiling approach based on arithmetic coding. There are two salient features in Adap-Tracer. First, it is space efficient by adopting a path profiling algorithm based on arithmetic coding. Second, it is adaptive by explicitly considering the execution frequency of each edge. We have implemented AdapTracer to profile Android applications. Our experimental evaluation uses modified JGF benchmarks to show AdapTracer's efficiency. Experimental results show that AdapTracer reduces the trace size by 44% on average and incurs execution overhead by 10% at most compared to PAP.