Scheduling Parallel Functional Programs

Опубликовано 7 сентября 2016, 17:02

Parallelism abounds! To continue to improve performance, programmers must use parallel algorithms to take advantage of multi-core and other parallel architectures. Functional languages allow programmers to express many parallel algorithms concisely. With a deterministic semantics, a functional language also allows programmers to reason about the correctness of parallel programs independently of the language implementation. Despite this, the performance of these programs still depends heavily on the language implementation and especially on the choice of scheduling policy. In some applications, this choice has asymptotic effects on space use. In this talk, I will present some of my recent work on a parallel functional programming language. At the core of this work is a cost semantics that allows programmers to reason about the performance of parallel programs and in particular about their use of space. This cost semantics is the basis for a suite of prototype profiling tools. These tools enable programmers to simulate and visualize program execution under different scheduling policies. This cost semantics also provides a specification for an implementation of the language. I will describe my implementation, a parallel extension of MLton, and relate some initial results. These results show good parallel speedups for several applications and that program performance reflects the predictions made by my profiler.