Compiling Parallel Code for Sparse Matrix Applications

We have developed a framework based on relational algebra for compiling efficient sparse matrix code from dense DO-ANY loops and a specification of the representation of the sparse matrix. In this paper, we show how this framework can be used to generate parallel code, and present experimental data that demonstrates that the code generated by our Bernoulli compiler achieves performance competitive with that of hand-written codes for important computational kernels. Keywords: parallelizing compilers, sparse matrix computations 1 Introduction Sparse matrix computations are ubiquitous in computational science. However, the development of high-performance software for sparse matrix computations is a tedious and error-prone task, for two reasons. First, there is no standard way of storing sparse matrices, since a variety of formats are used to avoid storing zeros, and the best choice for the format is dependent on the problem and the architecture. Second, for most algorithms, it takes a lo..