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dc.contributor.authorChowdhury, Rezaul
dc.contributor.authorGanapathi, Pramod
dc.contributor.authorTschudi, Stephen
dc.contributor.authorTithi, Jesmin Jahan
dc.contributor.authorBachmeier, Charles
dc.contributor.authorLeiserson, Charles E
dc.contributor.authorSolar-Lezama, Armando
dc.contributor.authorKuszmaul, Bradley C
dc.contributor.authorTang, Yuan
dc.date.accessioned2021-10-27T20:09:33Z
dc.date.available2021-10-27T20:09:33Z
dc.date.issued2017
dc.identifier.urihttps://hdl.handle.net/1721.1/134866
dc.description.abstract© 2017 ACM. We present Autogen-an algorithm that for a wide class of dynamic programming (DP) problems automatically discovers highly efficient cache-oblivious parallel recursive divide-And-conquer algorithms from inefficient iterative descriptions of DP recurrences. Autogen analyzes the set of DP table locations accessed by the iterative algorithm when run on a DP table of small size and automatically identifies a recursive access pattern and a corresponding provably correct recursive algorithm for solving the DP recurrence.We use Autogen to autodiscover efficient algorithms for several well-known problems. Our experimental results show that several autodiscovered algorithms significantly outperform parallel looping and tiled loop-based algorithms. Also, these algorithms are less sensitive to fluctuations of memory and bandwidth compared with their looping counterparts, and their running times and energy profiles remain relatively more stable. To the best of our knowledge, Autogen is the first algorithm that can automatically discover new nontrivial divide-And-conquer algorithms.
dc.language.isoen
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.isversionof10.1145/3125632
dc.rightsCreative Commons Attribution-Noncommercial-Share Alike
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.sourceMIT web domain
dc.titleAutogen: Automatic Discovery of Efficient Recursive Divide-8-Conquer Algorithms for Solving Dynamic Programming Problems
dc.typeArticle
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.relation.journalACM Transactions on Parallel Computing
dc.eprint.versionAuthor's final manuscript
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
eprint.statushttp://purl.org/eprint/status/NonPeerReviewed
dc.date.updated2019-06-12T15:30:06Z
dspace.orderedauthorsChowdhury, R; Ganapathi, P; Tschudi, S; Tithi, JJ; Bachmeier, C; Leiserson, CE; Solar-Lezama, A; Kuszmaul, BC; Tang, Y
dspace.date.submission2019-06-12T15:30:07Z
mit.journal.volume4
mit.journal.issue1
mit.metadata.statusAuthority Work and Publication Information Needed


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