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The contributions of this paper are the following.1. We introduce a new variety of generic programming in which algorithm implementors use a different API than data structure designers, the gap between the API's being bridged by restructuring compilers. One view of this approach is that it exploits restructuring compiler technology to perform a novel kind of template instantiation. 2. We demonstrate the usefulness of this new generic programming technology by deploying it in a system that generates efficient sparse codes from high-level algorithms and specifications of sparse matrix formats. 3. We argue that sparse matrix formats should be viewed as indexed-sequential access data structures (in the database sense), and show that appropriate abstractions of the index structure of common formats can be conveyed to a restructuring compiler through the type system of a modern language that supports inheritance and templates.

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.