Sparse linear algebra is essential for a wide variety of scientific applications. The availability of massively parallel sparse solvers and preconditioners lies at the core of pretty much all multi-physics and multi-scale simulations. Technology is nowadays expanding to target exascale platforms. With this work, we then try to face these challenges by developing both algorithmic and theoretical strategies to make Exascale Computing possible.
The libraries contained here cover both the needs of having both parallel BLAS feature for sparse matrices that are capable of running on machines with thousands of high-performance cores, both the construction of higher-level iterative solvers and preconditioners exploiting these capabilities.