English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We present a sparse linear system solver that is based on a multifrontalvariant of Gaussian elimination, and exploits low-rank approximation of theresulting dense frontal matrices. We use hierarchically semiseparable (HSS)matrices, which have low-rank off-diagonal blocks, to approximate the frontalmatrices. For HSS matrix construction, a randomized sampling algorithm is usedtogether with interpolative decompositions. The combination of the randomizedcompression with a fast ULV HSS factorization leads to a solver with lowercomputational complexity than the standard multifrontal method for manyapplications, resulting in speedups up to 7 fold for problems in our testsuite. The implementation targets many-core systems by using task parallelismwith dynamic runtime scheduling. Numerical experiments show performanceimprovements over state-of-the-art sparse direct solvers. The implementationachieves high performance and good scalability on a range of modern sharedmemory parallel systems, including the Intel Xeon Phi (MIC). The code is partof a software package called STRUMPACK -- STRUctured Matrices PACKage, whichalso has a distributed memory component for dense rank-structured matrices.

An efficient multi-core implementation of a novel HSS-structured multifrontal solver using randomized sampling