An Efficient Load Balancing Scheme for Grid-based High Performance Scientific computing
Abstract: With the emergence of computational grids, there has been a dramatic increase in the number of available processing and storing resources available for parallel execution of large-scale compute and data intensive scientific applications. However, large computing power in itself is not sufficient for high performance computing (HPC). In this context, (application) partitioning and load balancing strategies play a critical role in meeting the high performance requirements and in achieving high processor utilization. In HPC applications such as molecular simulations, protein synthesis, drug design et cetera parallel loops constitute the greatest percentage of program parallelism. The degree to which parallelism can be exploited during parallel execution of a nested loop directly depends on partitioning and load balance, i.e., the number of iterations mapped onto each processor, between the different processors. Thus, partitioning of parallel loops is of key importance for grid-based high performance scientific computing.
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