An efficient recognition-complete processor allocation strategy for k-ary n-cube multiprocessors

Composed of various topologies, the k-ary n-cube system is desirable for accepting and executing topologically different tasks. To utilize its large amount of processor resources, several allocation strategies have been reported, each with certain restrictions that affect performance. For improvement, we propose a new allocation strategy for the k-ary n-cubes. The proposed strategy is an extension of the TC strategy for hypercubes and is able to recognize all subcubes with different topologies requested by tasks. Complexity analysis and performance comparison between related strategies are provided to demonstrate their advantages and disadvantages. Simulation results show that with full subcube recognition ability and no internal fragmentation, our strategy always exhibits better performance.