Abstract
High Performance Computing systems typically require performant hardware infrastructures, in most cases managed and operated on-premises by single organizations. However, the computing power demand often fluctuates in time, resulting into periods where allocated resources can be underused. The pay-as-you-go and resources-on-demand approach provided by Cloud Computing can surely ease such problem, consequently reducing the upfront investment on enterprise infrastructures. However, a common approach to support software migration to the Cloud is still missing. Here we propose a methodology to recognize design and algorithmic characteristics in sequential source code and, thanks parallel Compilers and Skeletons, to support the parallelization and migration of existing software to the Cloud, guided in the process by the parallel programming paradigm represented by MapReduce. In addition, by leveraging the virtualization capabilities of the Cloud, it is possible to further parallelize specific sections of code by means of virtual GPUs, which can take advantage of the parallel data transmission capabilities offered by multiple Cloud nodes.
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