ASE 2017

2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE 2017), October 30 – November 3, 2017, Urbana-Champaign, IL, USA

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Technical Research
Understanding and Overcoming Parallelism Bottlenecks in ForkJoin Applications
Gustavo Pinto, Anthony Canino, Fernando Castor, Guoqing Xu, and Yu David Liu
(Federal University of Paríç, Brazil; SUNY Binghamton, USA; Federal University of Pernambuco, Brazil; University of California at Irvine, USA)
Abstract: ForkJoin framework is a widely used parallel programming framework upon which both core concurrency libraries and real-world applications are built. Beneath its simple and user-friendly APIs, ForkJoin is a sophisticated managed parallel runtime unfamiliar to many application programmers: the framework core is a *work-stealing* scheduler, handles *fine-grained* tasks, and sustains the pressure from *automatic* memory management. ForkJoin poses a unique gap in the compute stack between high-level software engineering and low-level system optimization. Understanding and bridging this gap is crucial for the future of parallelism support in JVM-supported applications. This paper describes a comprehensive study on parallelism bottlenecks in ForkJoin applications, with a unique focus on how they interact with underlying system-level features, such as work stealing and memory management. We identify 6 bottlenecks, and found that refactoring them can significantly improve performance and energy efficiency. Our field study includes an in-depth analysis of Akka --- a real-world actor framework --- and 30 additional open-source ForkJoin projects. We sent our patches to the developers of 15 projects, and 7 out of the 9 projects that replied to our patches have accepted them.


Time stamp: 2019-06-25T06:08:38+02:00