Strongly scalable algorithms for matrix and tensor computations
Title: Strongly scalable algorithms for matrix and tensor computations
SNIC Project: SNIC 2013/1-256
Project Type: SNAC Medium
Principal Investigator: Lars Karlsson <>
Affiliation: UmeƄ University
Duration: 2013-10-01 – 2014-10-01
Classification: 10105


The number of processor cores available to researchers and engineers is rapidly growing. To effectively utilize this increased processing power, codes must be parallel and scalable. However, many existing codes are scalable only in the weak sense, that is, they maintain efficiency when scaling to a larger parallel system only if also the problem size is scaled up. We are primarily interested in dense matrix and tensor algorithms and here it is evident that very large problems are necessary to obtain high efficiency using standard algorithms. Much of the current research in the area focus on developing algorithms that are scalable in the strong sense, that is, algorithms that maintain high efficiency when the problem size remains fixed. We propose to continue the development of strongly scalable matrix and tensor algorithms as well as constructing generic techniques that facilitate the development of such algorithms. For example, we intend to develop algorithms and software for dense eigenvalue problems, low-rank tensor computations, and two-sided matrix decompositions.