Speculative evaluation, including leniency and futures, is often used to produce high degrees of parallelism. Existing speculative implementations, however, may serialize computation because of their implementation of queues of suspended threads. We give a provably efficient parallel implementation of a speculative functional language on various machine models. The implementation includes proper parallelization of the necessary queuing operations on suspended threads. Our target machine models are a butterfly network, hypercube, and PRAM. To prove the efficiency of our implementation, we provide a cost model using a profiling semantics and relate the cost model to implementations on the parallel machine models. 1 Introduction Futures, lenient languages, and several implementations of graph reduction for lazy languages all use speculative evaluation (call-by-speculation [15]) to expose parallelism. The basic idea of speculative evaluation, in this context, is that the evaluation of a...

Dynamic granularity estimation is a new technique for automatically identifying expressions in functional languages for parallel evaluation. Expressions with little computation relative to thread-creation costs should evaluate sequentially for maximum performance. Static identification of such threads is however difficult. Therefore, dynamic granularity estimation has compile-time and run-time components: Abstract interpretation statically identifies functions whose complexity depends on data structure sizes; the run-time system maintains approximations to these sizes. Compiler-inserted checks consult this size information to make thread creation decisions dynamically. We describe dynamic granularity estimation for a list-based functional language. Extension to general recursive data structures and imperative operations is possible. Performance measurements of dynamic granularity estimation in a parallel ML implementation on a shared-memory machine demonstrate the possibility of large...

This research is an attempt to reason about the control of parallel computation in the world of applicative programming languages. Applicative languages, in which computation is performed through function application and in which functions are treated as first-class objects, have the benefits of elegance, expressiveness and having clean semantics. Parallel computation and real-world concurrent activities are much harder to reason about than the sequential counterparts. Many parallel applicative languages have thus hidden most control details with their declarative programming styles, but they are not expressive enough to characterize many real world concurrent activities that can be easily explained with concepts such as message passing, pipelining and so on. Ease of programming should not come at the expense of expressiveness. Therefore, we design a parallel applicative language Pscheme such that programmers can express explicitly the control of parallel computation while maintaining ...

This paper surveys sequential and parallel implementation techniques for functional programming languages, as well as optimizations that can improve their performance. Sequential implementations have evolved from simple interpreters to sophisticated super-combinator-based compilers, while most parallel implementations have explored a broad range of techniques. We analyze the purpose and function of each implementation technique and discuss the current state-of-the-art in functional language implementation.