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: dis- tributed data management, process management, inter-process commu- nication, and load balancing. This encapsulation of data management, together with the prescribed semantics of a typical fundamental compo- nent 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 trans- formations suitable for certain classes of numerical algorithms, one for re- ducing 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.