Synthesizing parallel graph programs via automated planning

Abstract

We describe a system that uses automated planning to synthesize correct and efficient parallel graph programs from high-level algorithmic specifications. Automated planning allows us to use constraints to declaratively encode program transformations such as scheduling, implementation selection, and insertion of synchronization. Each plan emitted by the planner satisfies all constraints simultaneously, and corresponds to a composition of these transformations. In this way, we obtain an integrated compilation approach for a very challenging problem domain. We have used this system to synthesize parallel programs for four graph problems: triangle counting, maximal independent set computation, preflow-push maxflow, and connected components. Experiments on a variety of inputs show that the synthesized implementations perform competitively with hand-written, highly-tuned code.