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Changing Approaches to HPC Systems
Prof.dr.sc. Mario Kovac, European Processor Initiative, Chief Communication Officer
The need to collect and efficiently and timely process that vast amounts of data comes at a price. The existing approach to HPC systems design is no longer sustainable for the exascale era (exascale systems being defined as those capable of executing 1018 calculations per second). Energy efficiency is of enormous importance for sustainability of future exascale HPC systems.
The design of a novel HPC processor family cannot be sustainable without thinking about possible additional markets that could support such long term activities
The European Processor Initiative (EPI) is one of the cornerstones of this EU HPC strategic plan. EPI gets together 23 partners from 10 European countries, with the aim to bring to the market a low power microprocessor and will ensure that the key competence of high-end chip design remains in Europe, a critical point for many application areas. Thanks to such new European technologies, European scientists and industry will be able to access exceptional levels of energy-efficient computing performance. This EPI efforts will benefit Europe's scientific leadership, industrial competitiveness, engineering skills and know-how and the society as whole, as recognized by high EU officials.
The design of a novel HPC processor family cannot be sustainable without thinking about possible additional markets that could support such long term activities. Thus, EPI will cover other areas such as the automotive sector, ensuring the overall economic viability of the initiative.
Current main trends driving the innovations in automotive industry include introduction of autonomous driving (class 4 and 5) and the ‘Connected Car’ infrastructure. New autonomous vehicle E/E-architectures require computing platforms able to execute complex vehicle perception algorithms that include modelling of the surrounding environment, sensor/imaging processing, data fusion, low-latency deep machine learning for object classification and behavior prediction with seamless, dependable and secure interaction between mobile high performance embedded computing and stationary server-based high performance computing.