EXPERIMENTAL COMPARISON OF MATRIX ALGORITHMS FOR DATAFLOW COMPUTER ARCHITECTURE

In this paper we draw our attention to several algorithms for the dataflow computer paradigm, where the dataflow computation is used to augment the classical control-flow computation and, hence, strives to obtain an accelerated algorithm. Our main goal is to experimentally explore various dataflow techniques and features, which enable such an acceleration. Our focus is to resolve one of the most important challenges when designing a dataflow algorithm, which is to determine the best possible data choreography in the given context. In order to mitigate this challenge, we systematically enumerate and present possible techniques of various data choreographies. In particular, we focus our interest on the algorithms that use matrices and vectors as the underlaying data structure. We begin with simple algorithms such as matrix and vector multiplication, evaluation of polynomials as well as more advanced ones such as the simplex algorithm for solving linear programs. To evaluate the algorithms we compare their running-times as well as the dataflow resource consumption