Efficient Online Path Profiling

Abstract

Most dynamic program analysis techniques such as profile-driven compiler optimizations, software testing and runtime property checking infer program properties by profiling one or more executions of a program. Unfortunately, program profiling does not come for free. For example, even the most efficient techniques for profiling acyclic, intra-procedural paths can slow down program execution by a factor of 2. In this thesis, we propose techniques that significantly lower the overheads of profiling paths, enabling the use of path-based dynamic analyzes in cost-sensitive environments. Preferential path profiling (PPP) is a novel software-only path profiling scheme that efficiently profiles given subsets of paths, which we refer to as interesting paths. The algorithm is based on the observation that most consumers of path profiles are only interested in profiling a small set of paths known a priori. Our algorithm can be viewed as a generalization of the Ball-Larus path profiling algorithm. Whereas the Ball-Larus algorithm assigns weights to the edges of a given CFG such that the sum of the weights of the edges along each path through the CFG is unique, our algorithm assigns weights to the edges such that the sum of the weights along the edges of interesting paths is unique. Furthermore, our algorithm attempts to achieve a minimal and compact encoding of the interesting paths; such an encoding significantly reduces the overheads of path profiling by eliminating expensive hash operations during profiling. Interestingly, we find that both the Ball-Larus algorithm and PPP are essentially a form of arithmetic coding. We use this connection to prove that the numbering produced by PPP is optimal. We also propose a programmable, non-intrusive hardware path profiler (HPP). The hardware profiler consists of a path detector that detects paths by monitoring the stream of retiring branch instructions emanating from the processor pipeline. The path detector can be programmed to detect various types of paths and track architectural events that occur along paths. The second component of the hardware profiling infrastructure is a Hot Path Table (HPT), that collects accurate hot path profiles. Our experimental evaluation shows that PPP reduces the overheads of profiling paths to 15% on average (with a maximum of 26%). The algorithm can be easily extended to profile inter-procedural paths at minimal additional overheads (average of 26%). We modeled HPP using a cycle-accurate superscalar processor simulator and find that HPP generates accurate path profiles at extremely low overheads (0.6% on average) with a moderate hardware budget. We also evaluated the use of PPP and HPP in a realistic profiling scenarios. We find that the profiles generated by HPP can effectively replace expensive profiles used in profile-driven optimizations. We also find that even well-tested programs tend to exercise a large number of untested paths in the field, emphasizing the need for efficient profiling schemes that can be deployed in production environments.