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The growing power and number of high performance computing resources made available through computational grids present major opportunities as well as a number of challenges to the user.
Efficient utilization of these high performance computing resources can help solve the problem of computation bottleneck and expedite the experimental turnaround time.
We also discuss the application and use of novel computational steering techniques for the more efficient utilization of high performance computing resources.
This 9 billion grid cell database is possible using available high performance computing resources.
But it often remains difficult to easily integrate environmental applications with high performance computing resources.
HPC (high performance computing) resources are exploited in "urgent computing" mode.
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In this case reliable high performance compute resources are preferred.
Phylogenetic analyses were performed using high-performance computing resources of the National Computational Infrastructure NCI National Facilityty at the Australian National University.
The computations were mainly performed on the high-performance computing resources at the University of Missouri Bioinformatics Consortium.
We performed experiments on high-performance computing resources and clusters with different numbers of nodes and memory capacities.
Although other MapReduce implementations, such as Spark [36] and Hadoop [37], can provide scalability and fault tolerance, they are tailored to commodity hardware and cannot perform efficient execution on high-performance computing resources.
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