Optimizing general design objectives in processor-array design

We present an improved search procedure for the General Parameter Method (GPM). Our procedure maps uniform dependence algorithms to application-specific processor arrays (PAs). It can optimize general design objectives with certain nonmonotonicity properties, i.e., those that do not increase monotonically with the parameters. An example of such an objective is the minimization of the total completion time, including load and drain times. In contrast, earlier design methods can only deal with monotonic objectives. We present results for the matrix-product problem using this search technique. We also show that the parameters in GPM can be expressed in terms of the schedule vector Π&oarr; and allocation matrix S in the popular dependence-based method (DM), thereby allowing GPM to be used in DM for finding optimal designs for uniform dependence algorithms