A METHOD FOR SOLVING GRAPH NP-COMPLETE TASKS ON RECONFIGURABLE COMPUTER SYSTEMS BASED ON THE ITERATION PARALLELIZING PRINCIPLE

Abstract

When we solve graph NP-complete tasks on multiprocessor systems, the growth of hardware resource does not lead to the proportional increase of the system performance, and hence, the task solution time is not always reasonable. The aim of our research, given in the paper, is minimization of the solution time of the task of maximal clique enumeration on reconfigurable computer systems (RCS). When we solve tasks on RCSs with the help of the method of parallelizing by layers, the growth of performance also slows down in spite of better scalability in comparison with multiprocessor implementations. In the paper, we suggest a method of parallel-pipeline application development for reconfigurable computer systems. The method is based on parallelizing bylayers for graph NP-complete tasks. We show that the bit representation of sets, which is used for the method of parallelizing by layers, is not efficient for the method of parallelizing by iterations. The new method has another organization of calculations; it processes unordered sets, whose elements are accessed not by addresses (as in arrays), but by values (names of vertices and names of edges of the graph). We show that the new method, based on parallelizing by iterations, provides ramping of the RCS real performance at much larger computational resource in comparison with the method of parallelizing by layers. Its specific performance is lower, because computing substructures are to process more intermediate data due to symbolic representation of sets.