Via Phys.org:
Rubing Duan and Xiaorong Li at the A*STAR Institute of High Performance Computing in Singapore and co-workers have now developed a scheme to address the scheduling problem in two large-scale applications: the ASTRO program from the field of cosmology, which simulates the movements and interactions of galaxy clusters, and the WIEK2k program from the field of theoretical chemistry, which calculates the electronic structure of solids1. The researchers' new scheme relies on three game-theory-based scheduling algorithms: one to minimize the execution time; one to reduce the economic cost; and one to limit the storage requirements.
The researchers performed calculations wherein they stopped the competition for resources when the iteration reached the upper limit of optimization. They compared their simulation results with those from related algorithms—namely, Minimum Execution Time, Minimum Completion Time, Opportunistic Load Balancing, Max-min, Min-min and Sufferage. The new approach showed improvements in terms of speed, cost, scheduling results and fairness. Furthermore, the researchers found that the execution time improved as the scale of the experiment increased. In one case, their approach delivered results within 0.3 seconds while other algorithms needed several hours.