A Methodology for Generating Efficient Disk-Based Algorithms from Tensor Product Formulas

In this paper, we address the issue of automatic generation of disk-based algorithms from tensor product formulas. Disk-based algorithms are required in scientiic applications which work with large data sets that do not t entirely into main memory. Tensor products have been used for designing and implementing block recursive algorithms on shared-memory, vector and distributed-memory multiproces-sors. We extend this theory to generate disk-based code from tensor product formulas. The methodology is based on generating algebraically equivalent tensor product formulas which have better disk performance. We demonstrate this methodology by generating disk-based code for the fast Fourier transform.