Speculative computation in multilisp

Abstract

We demonstrate by experiments that performìng computations in parallel before their results are known to be required can yield performance improvements over conventional approaches to parallel computing. We call such eager computation of expressions speculative computation, as opposed to conventional mandatory computation that is used in almost all contemporary parallel programming languages and systems. The two major requirements for speculative computation are: 1) a means to control computation to favor the most promising computations and 2) a means to abort computation and reclaim computation resources. We discuss these requirements in the parallel symbolic language Multilisp and present a sponsor model for speculative computation in Multilisp which handles control and reclamation of computation in a single, elegant framework. We describe an implementation of this sponsor model and present performance results for several applications of speculative computation. The results demonstrate that our support for speculative computation adds expressive and computational power to Multilisp, with observed performance improvement as great as 26 times over conventional approaches to parallel computation.