Distributed dynamic processor allocation for multicomputers

Current processor allocation techniques for highly parallel systems use centralized front-end based algorithms which restrict applied strategies to static allocation, low parallelism, and weak fault tolerance. To lift these restrictions, we are investigating a distributed approach to processor allocation in multicomputers where currently no centralized data structure with information about the state of all processors exists. This approach will allow the implementation of more complex allocation schemes and possibly the consideration of dynamic allocation, where parallel applications would be able to adapt the allocated processor partition to its actual demand at running time, resulting in a more efficient utilization of system resources. Noncontiguous versions of a distributed dynamic processor allocation scheme are proposed and studied in this paper as an alternative for parallel programming models to allow dynamic creation and task deletion. Simulations compare the performance of the proposed dynamic strategies with static counterparts and also with well-known centralized algorithms in an environment with growing and shrinking processor demands. To demonstrate dynamic allocation is feasible with current technologies, results of the experiments are presented for a 96 nodes SCI hpcLine Primergy Server cluster.