The semantics and complexity of parallel programs for vector computations. Part I: A case study using ADA

Recent research in parallel numerical computation has tended to focus on the algorithmic level. Less attention has been given to the programming level where algorithm is matched, to some extent, to computer architecture. This two-part paper presents a three-level approach to parallel programming which distinguishes between mathematical algorithm, program and computer architecture. In part I, we motivate our approach by a case study using the Ada language. In part II, a mathematical concept of parallel algorithm is introduced in terms of partial orders. This serves as the basis of a theory of parallel computation which makes possible a precise semantics and a precise criterion of complexity of parallel programs. It also suggests some notation for specifying parallel numerical algorithms. To illustrate the ideas presented in part II, we concentrate here on parallel numerical computations which have vector spaces as their central data type and which are intended to be executed on a multi-processor system. The Ada language, with its task constructs, allows one to program computer algorithms to be executed on multi-processor systems, rather than on vector (pipelined) architectures. To provide a concrete example of the general problem of programming parallel numerical algorithms for multi-processor computers, we do a case study of how Ada can be used to program the solution of a system of linear equations on such computers. The case study includes an analysis of complexity which addresses the cost of data movement and process control/synchronization as well as the usual arithmetic complexity.Dedicated to Peter Naur on the occasion of his 60th birthdayThis research was partially supported by NSF Grants DCR-8406290 & CCR-8712192.