An Efficient and Scalable Coscheduling Technique for Large Symmetric Multiprocessor Clusters

Abstract

Coscheduling is essential for obtaining good performance in a time-shared symmetric multiprocessor (SMP) cluster environment. The most common technique, gang scheduling, has limitations such as poor scalability and vulnerability to faults mainly due to explicit synchronization between its components. A decentralized approach called dynamic coscheduling (DCS) has been shown to be effective for network of workstations (NOW), but this technique may not be suitable for the workloads on a very large SMP-cluster with thousands of processors. Furthermore, its implementation can be prohibitively expensive for such a large-scale machine. In this paper, we propose a novel coscheduling technique which can achieve coscheduling on very large SMP-clusters in a scalable, effcient, and cost-effective way. In the proposed technique, each local scheduler achieves coscheduling based upon message trafic between the components of parallel jobs. Message trapping is carried out at the user-level, eliminating the need for unsupported hardware or device-level programming. A sending process attaches its status to outgoing messages so local schedulers on remote nodes can make more intelligent scheduling decisions. Once scheduled, processes are guaranteed some minimum period of time to execute. This provides an opportunity to synchronize the parallel job’s components across all nodes and achieve good program performance. The results from a performance study reveal that the proposed technique is a promising approach that can reduce response time significantly over uncoordinated time-sharing and batch scheduling.

This work was performed under the auspices of the U.S. Department of Energy by University of California Lawrence Livermore National Laboratory under contract No. W-7405-Eng-48.