Abstracts Computer Science

Improving Implicit Parallelism

by Jose Manuel Calderon

We propose a new technique for exploiting the inherent parallelism in lazy functional programs. Known as implicit parallelism, the goal of writing a sequential program and having the compiler improve its performance by determining what can be executed in parallel has been studied for many years. Our technique abandons the idea that a compiler should accomplish this feat in ‘one shot’ with static analysis and instead allow the compiler to improve upon the static analysis using iterative feedback. We demonstrate that iterative feedback can be relatively simple when the source language is a lazy purely functional programming language. We present three main contributions to the field: the auto- matic derivation of parallel strategies from a demand on a structure, and two new methods of feedback-directed auto-parallelisation. The first method treats the runtime of the program as a black box and uses the ‘wall-clock’ time as a fitness function to guide a heuristic search on bitstrings representing the parallel setting of the program. The second feedback approach is profile directed. This allows the compiler to use profile data that is gathered by the runtime system as the pro- gram executes. This allows the compiler to determine which threads are not worth the overhead of creating them. Our results show that the use of feedback-directed compilation can be a good source of refinement for the static analysis techniques that struggle to account for the cost of a computation. This lifts the burden of ‘is this parallelism worthwhile?’ away from the static phase of compilation and to the runtime, which is better equipped to answer the question.