Fine grained parallel processing for soft computing

Abstract

This paper considers an approach to fine grained parallel processing for soft computing that mainly deals with large-scale stochastic optimization problems. In the detailed steps of the computation, there are a lot of useless calculations that has no influence upon final results. Removing such a wasted process must be effective to reduce the computational cost. The key is asynchronization of data processing by using redundancy of variables and priority-based processing. A typical system architecture to support this approach is presented and discussed for its application. © 2009 Springer Berlin Heidelberg.