Session

DescriptionNumerical simulation is a very common tool in a wide range of applications, ranging from structural mechanics to computational fluid-dynamics, underground processes, electromagnetism, and many others. Nowadays, the industrial world is constantly pushing for increasingly large and accurate, real-time solutions and modern engineers need efficient tools to address numerical simulations that require the solution of sparse linear systems of millions or even billions of unknowns. A common bottleneck in large size simulations is the cost for solving a linear systems of equations which tend to grow superlinearly with the problem size. Thus, the use of High-Performance Computing (HPC) is increasingly necessary, and the development of efficient and scalable sparse linear solvers is an important research topic. From a hardware point of view, the trend in HPC is towards massively parallel computing through smart interconnected nodes equipped with accelerators featuring thousands of simple computing units (cores). The availability of these novel computational platforms boosts both a theoretical renovation of well-established algorithms and the development of brand-new ideas. The algorithms must be designed and optimized having as target platforms multiple nodes with thousands of simple computing units and a memory hierarchy that is much more exposed to the developer's control.