Parallel language constructs for tensor product computations on loosely coupled architectures

Distributed memory architectures offer high levels of performance and flexibility, but have proven awkward to program. Current languages for nonshared memory architectures provide a relatively low-level programming environment, and are poorly suited to modular programming, and to the construction of libraries. This paper describes a set of language primitives designed to allow the specification of parallel numerical algorithms at a higher level. We focus here on tensor product array computations, a simple but important class of numerical algorithms. We consider first the problem of programming one dimensional “kernel” routines, such as parallel tridiagonal solvers, and after that look at how such parallel kernels can be combined to form parallel tensor product algorithms.