Avaliação de clusters baseados em sistemas em um chip para a computação de alto desempenho: uma revisão

High-performance computing systems are the maximum expression in the field of processing for large amounts of data. However, their energy consumption is an aspect of great importance, which was not considered decades ago. Hence, software developers and hardware providers are obligated to approach new challenges to address energy consumption, and costs. Constructing a computational cluster with a large amount of systems on a chip can result in a powerful, ecologic platform, with the capacity to offer sufficient performance for different applications, as long as low costs and minimum energy consumption can be maintained. As a result, energy efficient hardware has an opportunity to impact upon the area of high-performance computing. This article presents a systematic review of the evaluations conducted on clusters of  ystems on a Chip for High-Performance computing in the research setting.Los sistemas de computación de alto desempeño son la máxima expresión en el campo de procesamiento para grandes cantidades de datos. Sin embargo, su consumo de energía es un aspecto de gran importancia que no era tenido en cuenta en décadas pasadas. Por lo tanto, desarrolladores de software y proveedores de hardware están obligados a enfocarse en nuevos retos para abordar el consumo de energía y costos. Construir un clúster informático con una gran cantidad de sistemas en un chip puede dar como resultado una plataforma poderosa, ecológica y capaz de ofrecer el rendimiento suficiente para diferentes aplicaciones, siempre y cuando se puedan mantener bajos costos y el menor consumo de energía posible. Como resultado, el hardware eficiente en el consumo de energía tiene la oportunidad de tener un impacto en el área de la computación de alto desempeño. En este artículo se presenta una revisión sistemática para conocer las evaluaciones realizadas a clústeres de sistemas en un chip para computación de alto desempeño en el ámbito investigativo. Os sistemas de computação de alto desempenho são a máxima expressão no campo de processamento para grandes quantidades de dados. No entanto, seu consumo de energia é um aspecto de grande importância que não era levado em consideração em décadas passadas. Portanto, desenvolvedores de software e provedores de hardware estão obrigados a focar-se em novos desafios para abordar o consumo de energia e  ustos. Construir um cluster informático com uma grande quantidade de sistemas em um chip pode dar como resultado uma plataforma poderosa, ecológica e capaz de oferecer o rendimento suficiente para diferentes aplicações, desde que possam ser mantidos baixos custos e o menor consumo de energia possível. Como resultado, o hardware eficiente no consumo de energia tem a oportunidade de ter um impacto na área da computação de alto desempenho. Neste artigo, apresenta-se uma revisão sistemática para conhecer as avaliações realizadas a clusters de sistemas em um chip para computação de alto desempenho no âmbito investigativo.&nbsp

Improving the performance of physics applications in atom-based clusters with rCUDA

[EN] Traditionally, High-Performance Computing (HPC) has been associated with large power requirements. The reason was that chip makers of the processors typically employed in HPC deployments have always focused on getting the highest performance from their designs, regardless of the energy their processors may consume. Actually, for many years only heat dissipation was the real barrier for achieving higher performance, at the cost of higher energy consumption. However, a new trend has recently appeared consisting on the use of low-power processors for HPC purposes. The MontBlanc and Isambard projects are good examples of this trend. These proposals, however, do not consider the use of GPUs. In this paper we propose to use GPUs in this kind of low-power processor based HPC deployments by making use of the remote GPU virtualization mechanism. To that end, we leverage the rCUDA middleware in a hybrid cluster composed of low-power Atom-based nodes and regular Xeon-based nodes equipped with GPUs. Our experiments show that, by making use of rCUDA, the execution time of applications belonging to the physics domain is noticeably reduced, achieving a speed up of up to 140x with just one remote NVIDIA V100 GPU with respect to the execution of the same applications using 8 Atom-based nodes. Additionally, a rough energy consumption estimation reports improvements in energy demands of up to 37x. (C) 2019 Elsevier Inc. All rights reserved.This work was funded by the Generalitat Valenciana, Spain under Grant PROMETEO/2017/077. Authors are also grateful for the generous support provided by Mellanox Technologies Inc.Silla, F.; Prades, J.; Baydal Cardona, ME.; Reaño, C. (2020). Improving the performance of physics applications in atom-based clusters with rCUDA. 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