Machine Learning and High-Performance Computing Hybrid Systems, a New Way of Performance Acceleration in Engineering and Scientific Applications
Citation: Proceedings of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 25, pages 27–36 (2021)
Abstract. Machine learning is one of the hottest topics in IT industry as well as in academia. Some of the IT leaders and scientists believe that this is going to totally revolutionise the industry. This transformation is happening on both fronts, one is the application and software paradigm, the other is at the hardware and system level. At the same time, the High-Performance Computing segment is striving to achieve the level of Exascale performance. It is not debatable that to meet such level of performance and keep the cost of system and power consumption on reasonable level is not a trivial task. In this article, we try to look at a potential solution to these problems and discuss a new approach to building systems and software to meet these challenges and the growing needs of the computing power for HPC systems on the one hand, but also be ready for a new type of workload including Artificial Intelligence type of applications
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