Events

HPC Architectures for AI and Dedicated Applications

Proteins and their intricate network of interactions are the mainstay of any cellular process. Dissecting these networks at atomic detail is invaluable, as this paves the route to a mechanistic understanding of biological function and provides the first essential step toward the development of new drugs. Because of the challenges associated with experimental techniques, structural biologists have to resort more and more these days to complementary computational methods.

During the last years, the computing ecosystem is becoming more and more heterogeneous. On the one hand, trends in computer architectures focus on providing different computing devices (CPUs, GPUs and FPGAs) and memories in a single chip or computing node, with the aim of providing better computing devices for the different types of algorithms and applications. On the other hand, distributed platforms such as Clusters, Grids and Clouds, which have been traditionally composed by many homogeneous nodes, are starting to be composed of heterogeneous nodes including processors with different cores, accelerators and memory capacities. This heterogeneity is required to achieve better computing performance with lower energy consumption and must be managed at different levels.

Big Data systems are computer systems that are based on similar design plans to all the others. We can therefore talk about the management of data in Big Data systems (Big Data Management) and using these data to extract knowledge relevant to the organization with Data Mining and Machine Learning algorithms (Big Data Analytics). Unlike traditional systems, however, there is not so much justification to separate the data exploitation management part, as there is no universal solution for storing data and exploiting them in a Big Data environment. Instead, the architectural solution depends on the specific case of use (exploitation) being considered. This Postgraduate programme provides an overview of Big Data ecosystem and considers both aspects in depth: management (Big Data Management) and exploitation of data (Big Data Analytics), while providing applicability and a business vision within this system.