High-Performance Numerical Optimization on Multicore Clusters

Abstract

This paper presents a software infrastructure for high performance numerical optimization on clusters of multicore systems. At the core, a runtime system implements a programming and execution environment for irregular and adaptive task-based parallelism. Building on this, we extract and exploit the parallelism of a global optimization application at multiple levels, which include Hessian calculations and Newton-based local optimizations. We discuss parallel implementations details and task distribution schemes for managing nested parallelism. Finally, we report experimental performance results for all the components of our software system on a multicore cluster.

A parallel hybrid optimization algorithm for fitting interatomic potentials

“…In order to exploit multicore clusters, we implemented the parallel GO algorithm with TORC [5,33], a custom task-parallel library that offers a programming and runtime environment where parallel programs can be executed unaltered on both shared and distributed memory platforms.…”