Low power Systems-on-Chip (SoCs), originally developed in the context of mobile and embedded technologies, are becoming attractive for the scientific community given their increasing computing performances, coupled with relatively low cost and power demand. In this work, we investigate the potential of SoCs for realistic scientific workloads, in particular taken from the bioinformatics and astrophysics domains. We selected a series of parallel, computationally intensive scientific applications and ported them to a cluster of development boards based on low power SoCs. The performance results obtained for the different applications are reported and compared with those obtained on a typical x86 HPC node.
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