Applications of Parallel Computation to Statistical Inference

Abstract

Abstract Recent advances in parallel computation (e.g., Eddy and Schervish 1986; Gardner, Gerard, Mowers, Nemeth, and Schnabel 1986) have made it possible for a network of microcomputers to act together as a parallel processor using data-flow algorithms (see O'Leary and Stewart 1985). A data-flow algorithm is one in which the sequence of computations is not scheduled a priori, but rather is determined by the order in which computations are completed. Many statistical problems can be adapted to dataflow algorithms. One requirement is that the computation can be broken into independent parts, that is, parts that can be performed in any order. For example, calculating the average of a large number of data values requires adding them up, which can be done in any order. Suppose that an application can be broken into independent parts and one has p processors available to perform the computations. Then each of the p processors can work on one of the parts at a time. When one processor finishes, it can work on t...