Compiling Parallel Sparse Code for User-Defined Data Structures.

Abstract

We describe how various sparse matrix and distribution formats can be handled using the relational approach to sparse matrix code compilation. This approach allows for the development of compilation techniques that are independent of the storage formats by viewing the data structures as relations and abstracting the implementation details as access methods. 1 Introduction Sparse matrix computations are at the core of many computational science algorithms. A typical application can often be separated into the discretization module, which translates a continuous problem (such as a system of differential equations) into a sequence of sparse matrix problems, and into the solver module, which solves the matrix problems. Typically, the solver is the most timeand space-intensive part of an application and, quite naturally, much effort both in the numerical analysis and compilers communities has been devoted to producing efficient parallel and sequential code for sparse matrix solvers. There...