Effectively Mapping Linguistic Abstractions for Message-passing Concurrency to Threads on the Java Virtual Machine

By: Ganesha Upadhyaya and Hridesh Rajan

Abstract

Efficient mapping of message passing concurrency (MPC) abstractions to Java Virtual Machine (JVM) threads is critical for performance, scalability, and CPU utilization; but tedious and time consuming to perform manually. In general, this mapping cannot be found in polynomial time, but we show that by exploiting the local characteristics of MPC abstractions and their communication patterns this mapping can be determined effectively. We describe our MPC abstraction to thread mapping technique, its realization in two frameworks (Panini and Akka), and its rigorous evaluation using several benchmarks from representative MPC frameworks. We also compare our technique against four default mapping techniques: thread-all, round-robin-task-all, random-task-all and work-stealing. Our evaluation shows that our mapping technique can improve the performance by 30%-60% over default mapping techniques. These improvements are due to a number of challenges addressed by our technique namely: i) balancing the computations across JVM threads, ii) reducing the communication overheads, iii) utilizing information about cache locality, and iv) mapping MPC abstractions to threads in a way that reduces the contention between JVM threads.

ACM Reference

Upadhyaya, G. and Rajan, H. 2015. Effectively Mapping Linguistic Abstractions for Message-passing Concurrency to Threads on the Java Virtual Machine. OOPSLA’15: The ACM SIGPLAN conference on Systems, Programming, Languages and Applications: Software for Humanity (SPLASH) (Oct. 2015).

BibTeX Reference

@inproceedings{upadhyaya2015effectively,
  author = {Ganesha Upadhyaya and Hridesh Rajan},
  title = {Effectively Mapping Linguistic Abstractions for Message-passing Concurrency to Threads on the Java Virtual Machine},
  booktitle = {OOPSLA'15: The ACM SIGPLAN conference on Systems, Programming, Languages and Applications: Software for Humanity (SPLASH)},
  location = {Pittsburgh, PA, USA},
  month = {October},
  year = {2015},
  entrysubtype = {conference},
  abstract = {
    Efficient mapping of message passing concurrency (MPC) abstractions to Java
    Virtual Machine (JVM) threads is critical for performance, scalability, and
    CPU utilization; but tedious and time consuming to perform manually. In
    general, this mapping cannot be found in polynomial time, but we show that by
    exploiting the local characteristics of MPC abstractions and their
    communication patterns this mapping can be determined effectively. We describe
    our MPC abstraction to thread mapping technique, its realization in two
    frameworks (Panini and Akka), and its rigorous evaluation using several
    benchmarks from representative MPC frameworks. We also compare our technique
    against four default mapping techniques: thread-all, round-robin-task-all,
    random-task-all and work-stealing. Our evaluation shows that our mapping
    technique can improve the performance by 30%-60% over default mapping
    techniques. These improvements are due to a number of challenges addressed by
    our technique namely: i) balancing the computations across JVM threads, ii)
    reducing the communication overheads, iii) utilizing information about cache
    locality, and iv) mapping MPC abstractions to threads in a way that reduces
    the contention between JVM threads.
  }
}