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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The randomized singular value decomposition (RSVD) is by now a wellestablished technique for efficiently computing an approximate singular valuedecomposition of a matrix. Building on the ideas that underpin the RSVD, therecently proposed algorithm "randUTV" computes a FULL factorization of a givenmatrix that provides low-rank approximations with near-optimal error. Becausethe bulk of randUTV is cast in terms of communication-efficient operations likematrix-matrix multiplication and unpivoted QR factorizations, it is faster thancompeting rank-revealing factorization methods like column pivoted QR in mosthigh performance computational settings. In this article, optimized randUTVimplementations are presented for both shared memory and distributed memorycomputing environments. For shared memory, randUTV is redesigned in terms of an"algorithm-by-blocks" that, together with a runtime task scheduler, eliminatesbottlenecks from data synchronization points to achieve acceleration over thestandard "blocked algorithm", based on a purely fork-join approach. Thedistributed memory implementation is based on the ScaLAPACK library. Theperformances of our new codes compare favorably with competing factorizationsavailable on both shared memory and distributed memory architectures.

Efficient algorithms for computing a rank-revealing UTV factorization on parallel computing architectures