Cover Image
QR Code

IN-MEMORY PARALLEL PROCESSING FOR MATRIX TRANSPOSE.

Efficient implementation of parallel algorithms for matrix transposition requires careful consideration of data distribution, synchronization, communication overheads, and load balancing to achieve optimal performance in-memory parallel processing. We propose an algorithm and its implementation for...

Full description

Published in:InSight: Rivier Academic Journal Vol. 18; no. 1; pp. 1 - 15
Main Authors: Malita, Mihaela, Ştefan, Gheorghe M.
Format: Article
Published: Rivier College (InSight: Rivier Academic Journal) Fall2023
Subjects:
Data distribution
Linear algebra
Online Access:View this record in EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=175785491&site=ehost-live
header:
  @attributes:
    shortDbName: hlh
    uiTerm: 175785491
    longDbName: Humanities International Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    jinfo:
      jid:
        19469233
        B2BS
      jtl: InSight: Rivier Academic Journal
      issn: 19469233
      maglogo: N
    pubinfo:
      dt: Fall2023
      vid: 18
      iid: 1
      pid: 59866
      pub: Rivier College (InSight: Rivier Academic Journal)
    artinfo:
      ui: 175785491
      ppf: 1
      ppct: 14
      formats:
      tig:
        atl: IN-MEMORY PARALLEL PROCESSING FOR MATRIX TRANSPOSE.
      aug:
        au:
          Malita, Mihaela
          Ştefan, Gheorghe M.
        affil:
          Associate Professor, Department of Mathematics and Computer Science, Rivier University
          Professor, National University of Science and Technology Politehnica Bucharest, Romania
      su:
        Data distribution
        Linear algebra
      sug:
        subj:
          Data distribution
          Linear algebra
      keyword:
        heterogenous systems
        in-memory processing
        linear algebra
        matrix transpose
        parallelism
      ab: Efficient implementation of parallel algorithms for matrix transposition requires careful consideration of data distribution, synchronization, communication overheads, and load balancing to achieve optimal performance in-memory parallel processing. We propose an algorithm and its implementation for execution on a p-cell parallel in-memory cellular processor (CP). Two versions of CP are considered, one with size in O(p) and another with size in O(p×log p).
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
    refInfo:
    copyright:
      @attributes:
        flag: Y
      dt:
        @attributes:
          year: 2023
    holdings:
      @attributes:
        islocal: N