Computer Science – Distributed – Parallel – and Cluster Computing
Scientific paper
2007-06-14
Computer Science
Distributed, Parallel, and Cluster Computing
18 pages, 12 figures, 2 tables. A shorter version of this paper is available in the proceedings of the The Fifth International
Scientific paper
Traditional parallel schedulers running on cluster supercomputers support only static scheduling, where the number of processors allocated to an application remains fixed throughout the execution of the job. This results in under-utilization of idle system resources thereby decreasing overall system throughput. In our research, we have developed a prototype framework called ReSHAPE, which supports dynamic resizing of parallel MPI applications executing on distributed memory platforms. The resizing library in ReSHAPE includes support for releasing and acquiring processors and efficiently redistributing application state to a new set of processors. In this paper, we derive an algorithm for redistributing two-dimensional block-cyclic arrays from $P$ to $Q$ processors, organized as 2-D processor grids. The algorithm ensures a contention-free communication schedule for data redistribution if $P_r \leq Q_r$ and $P_c \leq Q_c$. In other cases, the algorithm implements circular row and column shifts on the communication schedule to minimize node contention.
Ribbens Calvin J.
Sudarsan Rajesh
No associations
LandOfFree
Efficient Multidimensional Data Redistribution for Resizable Parallel Computations does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Efficient Multidimensional Data Redistribution for Resizable Parallel Computations, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Efficient Multidimensional Data Redistribution for Resizable Parallel Computations will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-331397