Hoppa direkt till innehållet

Information till studenter och medarbetare med anledning av covid-19 (Uppdaterad: 4 december 2020)

printicon

StarNEig library

Forskningsprojekt StarNEig is a new task-based parallel library for solving nonsymmetric standard and generalized eigenvalue problems.

Projektöversikt

Projektperiod:

Startdatum: 2015-11-01

Medverkande institutioner och enheter vid Umeå universitet

Institutionen för datavetenskap

Forskningsämne

Datavetenskap

Projektbeskrivning

StarNEig is either comparable to LAPACK and ScaLAPACK or significantly faster depending on the computational step. Moreover, StarNEig realizes new parallel and blocked algorithms for computing eigenvectors without suffering from floating point overflow. In LAPACK the corresponding solvers are sequential scalar codes which compute eigenvectors one by one. In ScaLAPACK the corresponding solvers are vulnerable to overflow.

Eigenvalue problems can be found in every field of natural science. Clear examples are supplied by the analysis of systems of ordinary differential equations. The stability analysis of first order systems produces standard eigenvalue problems which are not necessarily symmetric and the analysis of second order systems produce quadratic eigenvalue problems which are equivalent to nonsymmetric generalized eigenvalue problems. Without the ability to solve eigenvalue problems rapidly and accurately, we would be unable to complete the calculations needed to maintain and advance our civilization. Therefore, it is important that we continue to develop new algorithms and software which maximizes both the performance and the accuracy using existing and emerging hardware

StarNEig is one of very few libraries to offer support for nonsymmetric eigenvalue problems. It is built on top of the runtime system StarPU which is used to schedule the tasks. Currently, StarNEig applies to real problems which have real or complex eigenvalues and eigenvectors. By design, StarNEig applies to both shared and distributed memory machines and it has experimental support for GPU accelerators.