Language and Virtual Machine Support for Efficient Fine-Grained Futures in Java

Lingli Zhang, Chandra Krintz, and Priya Nagpurkar
University of California, Santa Barbara


In this work, we investigate the implementation of futures in Java J2SE v5.0. Java 5.0 provides an interface-based implementation of futures that enables users to encapsulate potentially asynchronous computation and to define their own execution engine for futures. Although this methodology decouples thread scheduling from application logic, for applications with fine-grained parallelism, this model imposes an undue burden on the average user and introduces significant performance overhead.

To address these issues, we investigate the use of lazy futures and offer an alternative implementation to the Java 5.0 approach. In particular, we present a directive-based future that uses annotations in Java 5.0 (as opposed to interfaces) and lazy future support to significantly simplify programmer effort. Our directive-based futures employ novel compilation and runtime techniques that transparently and adaptively split and spawn futures for parallel execution. All such decisions are automatic and guided by dynamically determined future granularity and underlying resource availability. We empirically evaluate our future implementation using different Java Virtual Machine configurations and common Java benchmarks that implement fine-grained parallelism. We compare directive-based lazy futures with lazy and Java 5.0 futures and show that our approach is significantly more scalable.