Memory Distance Measurement for Concurrent Programs.

Memory distance analysis, the number of unique memory references made between two accesses to the same memory location, is an effective method to measure data locality and predict memory behavior. Many existing methods on memory distance measurement and analysis consider sequential programs only. With the trend towards concurrent programming, it is necessary to study the impact of memory distance on the performance of concurrent programs. Unfortunately, accurate measurement of concurrent program memory distance is non-trivial. In fact, due to non-determinism, the reuse distance of memory references may differ with the same input set across multiple runs. Since memory distance measurement is fundamental to analysis, we propose a measuring approach that is based on randomized executions. Our approach provides a probabilistic guarantee of observing all possible interleavings without repeated executions. In order to evaluate our approach, we propose a second symbolic execution based approach that is more rigorous but much less scalable than the first approach. We have compared the two approaches on small programs and evaluated the first one on Parsec benchmark suite and a large industrial-size benchmark MySQL. Our experiments confirm that the randomized execution based approach is effective and practical.