Università della Svizzera italiana

High-performance interior point methods : application to power grid problems

Kardoš, Juraj ; Schenk, Olaf (Dir.)

Thèse de doctorat : Università della Svizzera italiana, 2020 ; 2020INFO003.

A software library for the solution of large-scale structured nonconvex optimization problems is presented in this work, with the purpose of accelerating the solution on single- core, multicore, or massively parallel high-performance distributed memory computing infrastructures. A large class of industrial and engineering problems possesses a particular structure, motivating the development of...

Università della Svizzera italiana

High performance selected inversion methods for sparse matrices : direct and stochastic approaches to selected inversion

Verbosio, Fabio ; Schenk, Olaf (Dir.)

Thèse de doctorat : Università della Svizzera italiana, 2019 ; 2019INFO002.

The explicit evaluation of selected entries of the inverse of a given sparse matrix is an important process in various application fields and is gaining visibility in recent years. While a standard inversion process would require the computation of the whole inverse who is, in general, a dense matrix, state-of-the-art solvers perform a selected inversion process instead. Such approach allows...

Università della Svizzera italiana

Local time stepping on high performance computing architectures : mitigating CFL bottlenecks for large-scale wave propagation

Rietmann, Max ; Schenk, Olaf (Dir.)

Thèse de doctorat : Università della Svizzera italiana, 2015 ; 2015INFO006.

Modeling problems that require the simulation of hyperbolic PDEs (wave equations) on large heterogeneous domains have potentially many bottlenecks. We attack this problem through two techniques: the massively parallel capabilities of graphics processors (GPUs) and local time stepping (LTS) to mitigate any CFL bottlenecks on a multiscale mesh. Many modern supercomputing centers are installing...