Functional parallel algorithms

Abstract

Functional programming presents several important advantages in the design, analysis and implementation of parallel algorithms: It discourages iteration and encourages decomposition. It supports persistence and hence easy speculation. It encourages higher-order aggregate operations. It is well suited for defining cost models tied to the programming language rather than the machine. Implementations can avoid false sharing. Implementations can use cheaper weak consistency models. And most importantly, it supports safe deterministic parallelism. In fact functional programming supports a level of abstraction in which parallel algorithms are often as easy to design and analyze as sequential algorithms. The recent widespread advent of parallel machines therefore presents a great opportunity for functional programming languages. However, any changes will require significant education at all levels and involvement of the functional programming community. In this talk I will discuss an approach to designing and analyzing parallel algorithms in a strict functional and fully deterministic setting. Key ideas include a cost model defined in term of analyzing work and span, the use of divide-and-conquer and contraction, the need for arrays (immutable) to achieve asymptotic efficiency, and the power of (deterministic) randomized algorithms. These are all ideas I believe can be taught at any level.