Abstract Compile‐time optimizations involve a number of transformations such as loop permutation, fusion, tiling, array contraction etc. The selection of the appropriate transformation to minimize the execution time is a challenging task. We address this problem in the context of tensor contraction expressions involving arrays too large to fit in main memory. Domain‐specific features of the computation are exploited to develop an integrated framework that facilitates the exploration of the entire search space of optimizations. In this paper, we discuss the exploration of the space of loop fusion and tiling transformations in order to minimize the disk I/O cost. These two transformations are integrated and pruning strategies are presented that significantly reduce the number of loop structures to be evaluated for subsequent transformations. The evaluation of the framework using representative contraction expressions from quantum chemistry shows a dramatic reduction in the size of the search space using the strategies presented. Copyright © 2007 John Wiley & Sons, Ltd.

The Tensor Contraction Engine (TCE) is a domain-specific compiler for implementing complex tensor contraction expressions arising in quantum chemistry applications modeling electronic structure. This paper develops a performance model for tensor contractions, considering both disk I/O as well as inter-processor communication costs, to facilitate performance-model driven loop optimization for this domain. Experimental results are provided that demonstrate the accuracy and effectiveness of the model.

This paper provides an overview of a program synthesis system for a class of quantum chemistry computations. These computations are expressible as a set of tensor contractions and arise in electronic structure modeling. The input to the system is a a high-level specification of the computation, from which the system can synthesize high-performance parallel code tailored to the characteristics of the target architecture. Several components of the synthesis system are described, focusing on performance optimization issues that they address.