An approach for parallel job scheduling using nimble algorithm

High performance computing offers an excellent vehicle to accelerate computational needs of scientific and engineering applications. These High performance computing applications consists of several processes that communicate frequently. Because of their synchronization needs, these applications suffer from performance penalties if the processes of the applications are not all co scheduled together. Effective Scheduling Strategies to improve response time, throughput, utilization, mean response time, mean reaction time, mean slowdown are an important considerations in parallel systems. Traditionally used many space and time sharing strategies to accommodate multiple jobs at the same time in to a parallel system. However these approaches suffer from low system utilization and large job wait time. The paper concentrates on the detailed frequency granularity of the processes in the application and schedules accordingly. The paper discusses the various algorithms used for the grains sizes and results are compared with the traditional approaches like First Come First Served, Gang Scheduling, Flexible Co scheduling and the results are compared with the help of various performance metrics and analyzed in a detail manner.

[1]  Dror G. Feitelson,et al.  Probabilistic Backfilling , 2007, JSSPP.

[2]  P. Sadayappan,et al.  Selective Reservation Strategies for Backfill Job Scheduling , 2002, JSSPP.

[3]  Rajkumar Kettimuthu,et al.  Selective preemption strategies for parallel job scheduling , 2002, Proceedings International Conference on Parallel Processing.

[4]  Larry Rudolph,et al.  Metrics and Benchmarking for Parallel Job Scheduling , 1998, JSSPP.

[5]  Dimitrios S. Nikolopoulos,et al.  Informing algorithms for efficient scheduling of synchronizing threads on multiprogrammed SMPs , 2001, International Conference on Parallel Processing, 2001..

[6]  Dror G. Feitelson,et al.  Parallel Job Scheduling under Dynamic Workloads , 2003, JSSPP.

[7]  Dimitrios S. Nikolopoulos,et al.  Adaptive scheduling under memory constraints on non-dedicated computationalfarms , 2003, Future Gener. Comput. Syst..

[8]  Dror G. Feitelson,et al.  Adaptive parallel job scheduling with flexible coscheduling , 2005, IEEE Transactions on Parallel and Distributed Systems.

[9]  Scott Pakin,et al.  STORM: Lightning-Fast Resource Management , 2002, ACM/IEEE SC 2002 Conference (SC'02).

[10]  Dan Tsafrir,et al.  Backfilling Using System-Generated Predictions Rather than User Runtime Estimates , 2007, IEEE Transactions on Parallel and Distributed Systems.

[11]  Dror G. Feitelson,et al.  The workload on parallel supercomputers: modeling the characteristics of rigid jobs , 2003, J. Parallel Distributed Comput..

[12]  Victor Lee,et al.  Implications of I/O for Gang Scheduled Workloads , 1997, JSSPP.

[13]  Cosimo Anglano.  A comparative evaluation of implicit coscheduling strategies for networks of workstations , 2000, Proceedings the Ninth International Symposium on High-Performance Distributed Computing.