Dynamic Resizing of Parallel Scientific Simulations: A Case Study Using LAMMPS
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
Strategies for Rescheduling Tightly-Coupled Parallel Applications in Multi-Cluster Grids
Journal of Grid Computing
Malleable Model Coupling with Prediction
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
Efficient multiprogramming for multicores with SCAF
Proceedings of the 46th Annual IEEE/ACM International Symposium on Microarchitecture
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A traditional application scheduler running on a parallel cluster only supports static scheduling where the number of processors allocated to an application remains fixed throughout the lifetime of the job. Due to unpredictability in job arrival times and varying resource requirements, static scheduling can result in idle system resources thereby decreasing the overall system throughput. In this paper we present a prototype framework called ReSHAPE, which supports dynamic resizing of parallel MPI applications executed on distributed memory platforms. The framework includes a scheduler that supports resizing of applications, an API to enable applications to interact with the scheduler, and a library that makes resizing viable. Applications executed using the ReSHAPE scheduler framework can expand to take advantage of additional free processors or can shrink to accommodate a high priority application, without getting suspended. Experimental results show that the Re- SHAPE framework can improve individual job turn-around time and overall system throughput.