The effectiveness of loop self-scheduling schemes has been shown on traditional multiprocessors in the past and computing clusters in the recent years. However, parallel loop scheduling has not been widely applied to computing grids, which are characterized by heterogeneous resources and dynamic environments. In this paper, a performance-based approach, taking the two characteristics above into consideration, is proposed to schedule parallel loop iterations on grid environments. Furthermore, we use a parameter, SWR, to estimate the proportion of the workload which can be scheduled statically, thus alleviating the effect of irregular workloads. Experimental results on a grid testbed show that the proposed approach can reduce the completion time for applications with regular or irregular workloads. Consequently, we claim that parallel loop scheduling can benefit applications on grid environments.
Loop partitioning on parallel and distributed systems has been a critical problem. Furthermore, it becomes more difficult to deal with on the emerging heterogeneous PC cluster environments. In the past, some loop self-scheduling schemes have been proposed to be applicable to heterogeneous cluster environments. In this paper, we propose a performance-based approach, which partitions loop iterations according to the performance ratio of cluster nodes. To verify the proposed approach, a heterogeneous cluster is built, and three types of application programs are implemented to be executed in this testbed. Experimental results show that the proposed approach performs better than traditional schemes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations鈥揷itations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.