Coscheduling techniques and monitoring tools for non-dedicated cluster computing

Our efforts are directed towards the understanding of the coscheduling mechanism in a NOW system when a parallel job is executed jointly with local workloads, balancing parallel perfor-mance against the local interactive response. Explicit and implicit coscheduling techniques in a PVM-Linux NOW (or cluster) have been implemented. Furthermore, dynamic coscheduling remains an open question when parallel jobs are executed in a non-dedicated Cluster. A basis model for dynamic coscheduling in Cluster systems is presented in this paper. Also, one dynamic coscheduling algorithm for this model is proposed. The applicability of this algorithm has been proved and its performance ana-lyzed by simulation. Finally, a new tool (named Monito) for monitoring the different queues of messages in such an environments is presented. The main aim of implementing this facility is to provide a mean of capturing the bottlenecks and overheads of the communication system in a PVM-Linux cluster.Facultad de Informátic

E.: Building MPI for multi-programming systems using implicit information

Abstract. With the growing importance of fast system area networks in the parallel community, it is becoming common for message passing programs to run in multi-programming environments. Competing sequential and parallel jobs can distort the global coordination of communicating processes. In this paper, we describe our implementation of MPI using implicit information for global coscheduling. Our results show that MPI program performance is, indeed, sensitive to local scheduling variations. Further, the integration of implicit co-scheduling with the MPI runtime system achieves robust performance in a multi-programming environment, without compromising performance in dedicated use.