Supporting unbounded process parallelism in the SPC programming model

In automatic mapping of parallel programs to target parallel machines the efficiency of the compile-time cost estimation needed to steer the optimization process is highly dependent on the choice of programming model. Recently a new parallel programming model, called SPC, has been introduced that specifically aims at the efficient computation of reliable cost estimates, paving the way for automatic mapping. In SPC all algorithm level parallelism is explicitly specified, relying on compile-time transformation of the possibly unbounded algorithm level (data) parallelism to that of the actual target machine. In this paper we present SPC's process-algebraic framework in terms of which we demonstrate that the transformations needed to efficiently support unbounded process parallelism at program level are straightforward