Shadow Profiling Hiding Instrumentation Costs with Parallelism

• Published in: Code Generation and Optimization: Proceedings of the International Symposium on Code Generation and Optimization; 11-14 Mar. 2007 pp. 198 - 208
• Main Authors: Moseley, Tipp, Shye, Alex, Reddi, Vijay Janapa, Grunwald, Dirk, Peri, Ramesh
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 11.03.2007; IEEE
• Series: ACM Conferences
• Subjects: Computer science; Cost function; Environmental management; General and reference > Cross-computing tools and techniques > Performance; Hardware; Instruments; Operating systems; Optimizing compilers; Parallel processing; Runtime; Sampling methods; Software and its engineering > Software creation and management > Software verification and validation > Operational analysis; Software and its engineering > Software notations and tools > Compilers; Software and its engineering > Software notations and tools > Compilers > Runtime environments; Software and its engineering > Software notations and tools > Compilers > Source code generation; Theory of computation > Models of computation > Concurrency; Theory of computation > Models of computation > Concurrency > Parallel computing models
• ISBN: 9780769527642; 0769527647
• DOI: 10.1109/CGO.2007.35

In profiling, a tradeoff exists between information and overhead. For example, hardware-sampling profilers incur negligible overhead, but the information they collect is consequently very coarse. Other profilers use instrumentation tools to gather temporal traces such as path profiles and hot memory streams, but they have high overhead. Runtime and feedback-directed compilation systems need detailed information to aggressively optimize, but the cost of gathering profiles can outweigh the benefits. Shadow profiling is a novel method for sampling long traces of instrumented code in parallel with normal execution, taking advantage of the trend of increasing numbers of cores. Each instrumented sample can be many millions of instructions in length. The primary goal is to incur negligible overhead, yet attain profile information that is nearly as accurate as a perfect profile. The profiler requires no modifications to the operating system or hardware, and is tunable to allow for greater coverage or lower overhead. We evaluate the performance and accuracy of this new profiling technique for two common types of instrumentation-based profiles: interprocedural path profiling and value profiling. Overall, profiles collected using the shadow profiling framework are 94% accurate versus perfect value profiles, while incurring less than 1% overhead. Consequently, this technique increases the viability of dynamic and continuous optimization systems by hiding the high overhead of instrumentation and enabling the online collection of many types of profiles that were previously too costly.