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Math. Softw."],"published-print":{"date-parts":[[2022,6,30]]},"abstract":"<jats:p>\n            Randomized singular value decomposition (RSVD) is by now a well-established technique for efficiently computing an approximate singular value decomposition of a matrix. Building on the ideas that underpin RSVD, the recently proposed algorithm \u201crandUTV\u201d computes a\n            <jats:italic>full<\/jats:italic>\n            factorization of a given matrix that provides low-rank approximations with near-optimal error. Because the bulk of\n            <jats:sc>randUTV<\/jats:sc>\n            is cast in terms of communication-efficient operations such as matrix-matrix multiplication and unpivoted QR factorizations, it is faster than competing rank-revealing factorization methods such as column-pivoted QR in most high-performance computational settings. In this article, optimized\n            <jats:sc>randUTV<\/jats:sc>\n            implementations are presented for both shared-memory and distributed-memory computing environments. For shared memory,\n            <jats:sc>randUTV<\/jats:sc>\n            is redesigned in terms of an\n            <jats:italic>algorithm-by-blocks<\/jats:italic>\n            that, together with a runtime task scheduler, eliminates bottlenecks from data synchronization points to achieve acceleration over the standard\n            <jats:italic>blocked algorithm<\/jats:italic>\n            based on a purely fork-join approach. The distributed-memory implementation is based on the ScaLAPACK library. The performance of our new codes compares favorably with competing factorizations available on both shared-memory and distributed-memory architectures.\n          <\/jats:p>","DOI":"10.1145\/3507466","type":"journal-article","created":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T13:08:43Z","timestamp":1648213723000},"page":"1-42","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Algorithm\u00a01022: Efficient Algorithms for Computing a Rank-Revealing UTV Factorization on Parallel Computing Architectures"],"prefix":"10.1145","volume":"48","author":[{"given":"N.","family":"Heavner","sequence":"first","affiliation":[{"name":"University of Colorado at Boulder, Boulder, CO, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4480-9517","authenticated-orcid":false,"given":"F. 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