A New Approach to Parallelization of Serial Nested Loops Using Genetic Algorithms

Abstract

Loop parallelization is an important issue in the acceleration of the execution of scientific programs. To exploit parallelism in loops a system of equations representing the dependencies between the loop iterations and a system of non-equations indicating the loop boundary conditions has to be solved. This is a NP-Complete problem. Our major contribution in this paper has been to apply genetic algorithm to solve system of equation and non-equation resulted from loop dependency analysis techniques to find two dependent loop iterations. We use distance vector to find the rest of dependencies.

Using of Cuckoo Search Algorithm and Wave-Fronts Approach with Changing Angle for Tiled Iteration Space Scheduling of Two-Level Nested Loops

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