Efficient Parallel Functional Programming with Effects

Abstract

Although functional programming languages simplify writing safe parallel programs by helping programmers to avoid data races, they have traditionally delivered poor performance. Recent work improved performance by using a hierarchical memory architecture that allows processors to allocate and reclaim memory independently without any synchronization, solving thus the key performance challenge afflicting functional programs. The approach, however, restricts mutation, or memory effects, so as to ensure "disentanglement", a low-level memory property that guarantees independence between different heaps in the hierarchy. This paper proposes techniques for supporting entanglement and for allowing functional programs to use mutation at will. Our techniques manage entanglement by distinguishing between disentangled and entangled objects and shielding disentangled objects from the cost of entanglement management. We present a semantics that formalizes entanglement as a property at the granularity of memory objects, and define several cost metrics to reason about and bound the time and space cost of entanglement. We present an implementation of the techniques by extending the MPL compiler for Parallel ML. The extended compiler supports all features of the Parallel ML language, including unrestricted effects. Our experiments using a variety of benchmarks show that MPL incurs a small time and space overhead compared to sequential runs, scales well, and is competitive with languages such as C++, Go, Java, OCaml. These results show that our techniques can marry the safety benefits of functional programming with performance.