We study the performance of algebraic operations on large sparse matrices stored on secondary storage and show how the traditional algorithms can be fine-tuned in order to minimize the number of page accesses. We develop cost equations for performing multiplication, transposition, and Gaussian elimination on various secondary storage schemes for sparse matrices and show how these can be incorporated into a selection model which chooses the optimal sequence of storage schemes for a given mix of operations. Furthermore, we present the results of a number of experiments and compare our analytical results with experimental results obtained on synthetically generated data.
ASJC Scopus subject areas
- Modeling and Simulation
- Computer Science Applications