Abstract
Several forms of non-HPC clusters named cluster of workstations and cluster of virtual machines have become available in universities and research institutions as cost effective solutions for scientific computing. With the need to characterize the cluster computing systems that are traditionally used to run high-performance computing applications and those that are not, the terms tightly- and loosely- coupled clusters were adopted. However this qualitative characterization of clusters does not provide further characterization of non-HPC systems, and does not reveal real insights into their capacity to tackle many scientific applications. As a consequence, researchers who use these computing systems do not have the tools to make informed decisions about what type of applications better fits the capacity and capabilities of every kind of non-HPC cluster. In this work, we propose the cluster performance profile. This profile enables the quantitative characterization, initially, on non-HPC clusters in order to support decisions in the use and development of these clusters.
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Vivas, A., Castro, H. (2022). Quantitative Characterization of Scientific Computing Clusters. In: Navaux, P., Barrios H., C.J., Osthoff, C., Guerrero, G. (eds) High Performance Computing. CARLA 2022. Communications in Computer and Information Science, vol 1660. Springer, Cham. https://doi.org/10.1007/978-3-031-23821-5_4
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