Cost Optimality And Predictability Of Parallel Programming With Skeletons

Abstract

Skeletons are reusable, parameterized components with well-defined semantics and pre-packaged efficient parallel implementation. This paper develops a new, provably cost-optimal implementation of the DS (double-scan) skeleton for the divide-and-conquer paradigm. Our implementation is based on a novel data structure called plist (pointed list); implementation’s performance is estimated using an analytical model. We demonstrate the use of the DS skeleton for parallelizing a tridiagonal system solver and report experimental results for its MPI implementation on a Cray T3E and a Linux cluster: they confirm the performance improvement achieved by the cost-optimal implementation and demonstrate its good predictability by our performance model.