Many engineering and science problems require a computational effort to solve large sparse
linear systems. Krylov subspace based iterative solvers have been widely used in that …

Finite element analysis involves the solution of linear systems described by large size
sparse matrices. Iterative Krylov methods are well suited for such type of problems. These …

Direct and iterative methods are often used to solve linear systems in engineering. The
matrices involved can be large, which leads to heavy computations on the central …

We present specialized implementations of the preconditioned iterative linear system solver
in ILUPACK for Non-Uniform Memory Access (NUMA) platforms and many-core hardware co …

ILUPACK is a valuable tool for the solution of sparse linear systems via iterative Krylov
subspace-based methods. Its relevance for the solution of real problems has motivated …

We investigate the benefits that an energy-aware implementation of the runtime in charge of
the concurrent execution of ILUPACK—a sophisticated preconditioned iterative solver for …

Exascale computers are expected to have highly hierarchical architectures with nodes
composed by multiple core processors (CPU; central processing unit) and accelerators …

Many engineering and scientific problems need to solve boundary value problems for partial
differential equations or systems of them. For most cases, to obtain the solution with desired …

We propose a fully distributed parallel adaptive generalization of the Ruge–Stuben
algebraic multigrid method. In the adaptive multigrid framework, the coarse space and …

Major microprocessor vendors have integrated functional software-based self-testing in their
manufacturing test flows during the last decade. Functional self-testing is performed by test …