No abstract

We describe verification and coverage methods for multicore software that uses message passing libraries for communication. Specifically, we provide techniques to improve reliability of software using the new industry standard MCAPI by the Multicore Association. We develop dynamic predictive verification techniques that allow us to find actual and potential errors in a multicore software. Some of these error types are deadlocks, race conditions, and violation of temporal assertions. We complement our verification techniques with a mutation-testing-based coverage metric. Coverage metrics enable measuring the quality of verification tests. We implemented our techniques in tools and validated them on several multicore programs that use the MCAPI standard. We implement our techniques in tools and experimentally show the effectiveness of our approach. We find errors that are not found using traditional dynamic verification techniques and we can potentially explore execution schedules different than the original program with our coverage tool. This is the first time such predictive verification and coverage metrics have been developed for MCAPI. ACM Reference Format:Deniz, E., Sen, A., and Holt, J. 2012. Verification and coverage of message passing multicore applications.

Abstract-Benchmarks capture the essence of many important real-world applications and allow performance, and power analysis while developing new systems. Synthetic benchmarks are a miniaturized form of benchmarks that allow high simulation speeds and act as proxies to proprietary applications. Software architecture principles guide the development of new applications and benchmarks. We leverage software architectural patterns in developing synthetic benchmarks for embedded multicore systems. We developed an automated framework complete with characterization and synthesis components and performed experiments on PARSEC and Rodinia benchmarks. Our benchmarks can be run on any given infrastructure, that is, SMP or message passing, unlike previously developed benchmarks. Hence, this allows us to target heterogeneous embedded multicore systems. Our results show that the synthetic benchmarks and the real applications are similar with respect to various micro-architecture dependent as well as independent metrics.

Multicore hardware and software are becoming increasingly more complex. The programmability problem of multicore software has led to the use of parallel patterns. Parallel patterns reduce the effort and time required to develop multicore software by effectively capturing its thread communication and data sharing characteristics. Hence, detecting the parallel pattern used in a multi-threaded application is crucial for performance improvements and enables many architectural optimizations; however, this topic has not been widely studied. We apply machine learning techniques in a novel approach to automatically detect parallel patterns and compare these techniques in terms of accuracy and speed. We experimentally validate the detection ability of our techniques on benchmarks including PARSEC and Rodinia. Our experiments show that the k-nearest neighbor, decision trees, and naive Bayes classifier are the most accurate techniques. Overall, decision trees are the fastest technique with the lowest characterization overhead producing the best combination of detection results. We also show the usefulness of the proposed techniques on synthetic benchmark generation.

No abstract