Parallelization of Loops with Variable Distance Data Dependences

Abstract: The extent of parallelization of a loop is largely determined by the dependences between its statements. While dependence free loops are fully parallelizable, those with loop carried dependences are not. Dependence distance is a measure of absolute difference between a pair of dependent iterations. Loops with constant distance data dependence, because of uniform distance between the dependent iterations, lead to easy partitioning of the iteration space and hence they have been successfully dealt with. Parallelization of loops with variable distance data dependences is a considerably difficult problem. It is our belief that partitioning the iteration space in such loops cannot be done without examining solutions of the corresponding Linear Diophantine Equations. Focus of this work is to study variable distance data dependences and examine the relation between dependent iterations. Our analysis based on parametric solution leads to a mathematical formulation capturing dependence between iterations. Our approach shows the existence of reasonable exploitable parallelism in variable distance data dependences loops with multiple LDEs.