Optimizing Transformations of Stencil Operations for Parallel Object-Oriented Scientific Frameworks on Cache-Based Architectures

Abstract

High-performance scientific computing relies increasingly on high-level, large-scale, object-oriented software frameworks to manage both algorithmic complexity and the complexities of parallelism: distributed data management, process management, inter-process communication, and load balancing. This encapsulation of data management, together with the prescribed semantics of a typical fundamental component of such object-oriented frameworks—a parallel or serial array class library—provides an opportunity for increasingly sophisticated compile-time optimization techniques. This paper describes two optimizing transformations suitable for certain classes of numerical algorithms, one for reducing the cost of inter-processor communication, and one for improving cache utilization; demonstrates and analyzes the resulting performance gains; and indicates how these transformations are being automated.

This work was performed under the auspices of the U.S. Department of Energy by Los Alamos National Laboratory under Contract No. W-7405-Eng-36.