AdapTracer:Adaptive path profiling using arithmetic coding

Gonglong Chen

doi:10.1016/j.sysarc.2018.05.011

AdapTracer:Adaptive path profiling using arithmetic coding

2018-08-01

2

OA

AI

DOI：10.1016/j.sysarc.2018.05.011

Original

Save

View PDF

Abstract

En 中文

Path profiling, which aims to trace the execution path of programs, 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 the BLPP (Ball-Larus Path Profiling) algorithm, and PAP (Profiling All Path). 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 AdapTracer. 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.

Keywords:

Path profiling

Arithmetic coding

Adaptive tracing

AdapTracer:Adaptive path profiling using arithmetic coding

Abstract

Journal

Organization

Cited Papers