Faster and more accurate techniques for finding statements of Java Programs that run in parallel

What is it about?

In order to exploit the performance benefits of today's multi-core processors, programs are written in a way that several program statements can be executed by threads, in parallel on multipe cores. Such programs are called multithreaded programs. May Happen in Parallel (MHP) analysis is a technique of analyzing multithreaded programs to identify which statements can execute in parallel - without actually running the program. MHP analysis works by first converting the program to a graph that represents the execution flow of the statements and threads in the program. Then the analysis, which is a collection of data operations and rules, runs on this graph to provide the results. The analysis is quite sensitive to the size of the input graph. Java is a popular programming language for writing multithreaded programs. But historically, applying MHP analysis to real-world Java applications has been impractical. Program graphs created by previous works were bloated, making the computationally intensive analysis too slow to run in a reasonable time. This paper introduces GRIP-MHP, a novel technique that aggressively reduces the size of these program graphs. Our technique is far more effective at detect and only retaining the statements that actually affect MHP information, compared to previous methods. This enables us to drastically cut down the overall analysis time. Furthermore, we identified that previous works fail to produce precise results for common programming patterns, such as starting and joining threads inside loops. They also lack support for modern Java parallelism features. GRIP-MHP provides concrete solutions to these issues, thereby delivering a fast and precise MHP analysis

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Photo by orbtal media on Unsplash

Photo by orbtal media on Unsplash

Why is it important?

To the best of our knowledge, no existing May Happen in Parallel (MHP) analysis could handle large, real-world Java applications without severe limitations. Previous methods struggled to execute within a reasonable time-frame, and their outputs were often overly conservative and imprecise—falsely reporting that statements could run in parallel when they actually could not. Our research resolves these core issues through GRIP-MHP, massively reducing analysis times and significantly improving precision, especially for commonly used Java concurrency patterns. By providing a scalable and precise MHP analysis, this development enables far better analysis of concurrent programs. Writing bug-free concurrent programs is hard. However, with our highly accurate MHP output, developers and tool builders can now more reliably identify complex bugs, such as data races or accidental serialization. Furthermore, a highly precise MHP analysis inherently improves the quality and performance of downstream static analyses.

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