In this paper, we present two new parallel algorithms to solve large instances of the scheduling of independent tasks problem. First, we describe a parallel version of the Min-min heuristic. Second, we present GraphCell, an advanced parallel cellular genetic algorithm (CGA) for the GPU. Two new generic recombination operators that take advantage of the massive parallelism of the GPU are proposed for GraphCell. A speedup study shows the high performance of the parallel Min-min algorithm in the GPU versus several CPU versions of the algorithm (both sequential and parallel using multiple threads). GraphCell improves state-of-the-art solutions, especially for larger problems, and it provides an alternative to our GPU Min-min heuristic when more accurate solutions are needed, at the expense of an increased runtime.
Melab et al. [12] REF presented GPU implementations on the Min-Min heuristic and the GraphCell, an advanced parallel cellular genetic algorithm, for solving large instances of the scheduling of independent tasks problem.
Solving very large instances of the scheduling of independent tasks problem on the GPU
J. Parallel Distributed Comput.
J. Parallel Distributed Comput.
Computer Science