Ricerc@Sapienza

A very common and relevant concern in the most disparate applications in Computer Science, scientific and engineering modelling, is to have robust, reliable tools for fast computations. Two main factors contribute to this purpose: algorithms having low computational and communication costs, and the availability of architectures for high performance computing. A wide set of problems relies more or less explicitly on computational Linear Algebra in order to simplify problems whose symbolic solution would be prohibitively time requiring for computers. For this reason computational Linear Algebra is continuously evoked in numerical Calculus in order to solve scientific and engineering problems as fast as possible. At the same time, the algorithms involved need to be adapted frequently to the growing industry of computers and clusters architectures.
This project focuses on both aspects: devising novel computational Linear Algebra algorithms to be implemented in avant-garde parallel architectures that include clusters of multi-threaded processors and GPUs. In particular, we will concentrate on those fields of computational Linear Algebra that concern basic matrix operations and linear solvers, as they appear as intermediate steps within more complex frameworks in systems modelling and simulation. Depending on the nature of the input data, we aim to investigate the best parallel paradigms to use, also integrating state-of-the-art libraries for Linear Algebra.