Chaco

Before a calculation can be performed on a parallel computer, it must first be decomposed into tasks which are assigned to different processors. Efficient use of the machine requires that each processor have about the same amount of work to do and that the quantity of interprocessor communication is kept small. Finding an optimal decomposition is provably hard, but due to its practical importance, a great deal of effort has been devoted to developing heuristics for this problem. The decomposition problem can be addressed in terms of graph partitioning. Rob Leland and I have developed a variety of algorithms for graph partitioning and implemented them into a package we call Chaco. The code is being used at most of the major parallel computing centers around the world to simplify the development of parallel applications, and to ensure that high performance is obtained. Chaco has contributed to a wide variety of computational studies including investigation of the molecular structure of liquid crystals, evaluating the design of a chemical vapor deposition Reactor and modeling AutoMobile collisions. The algorithms developed for Chaco have also been successfully applied to a number of problems which have nothing to do with parallel computing. These include the determination of genomic sequences (a critical part of the Human Genome Project), the design of space-efficient circuit placements, organization of databases for efficient retrieval, and ordering of sparse matrices for efficient factorization. The code can also be used more GeneRally to identify data locality.