rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: ParU: A Task Based Parallel Multifrontal and Unsymmetric Sparse LU Factorization
  name:
    name part: Mahmoudi Aznaveh, Mohsen
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Davis, Tim
    role:
      role term: thesis advisor.
  name:
    name part: Texas A&M University
    role:
      role term: degree granting institution.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2023
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: We are introducing a new sparse direct solver with a parallel multifrontal algorithm. The algorithm is designed by borrowing the symbolic analysis phase and some ideas from UMFPACK. UMFPACK is a part of the SuiteSparse package and appears as a built-in routine in MATLAB. Although the general idea of the algorithm is the same, we designed a new algorithm from scratch that is more amenable to parallelism. The new algorithm is right-looking multifrontal with rectangular fronts and uses the sparsest pivot like UMFPACK; however, its data structures are different, and the factorization is more coarse-grained. One of the most significant contributions of this work is the algorithm that cuts various dependencies of the data structure. The only dependency comes from the matrix's pattern from the elimination tree. Independent tasks can start working in different computing cores using OpenMP tasking. Each task can call BLAS kernels, specifically matrix multiplication and triangular solve. Therefore, there is nested parallelism in this algorithm. To better manage hardware resources, we can exploit parallel BLAS only if we have more computing cores than tasks. In practice, the performance depends on the BLAS library and the input matrix. It is better to have a mixed strategy to have both parallel BLAS with parallel fronts. Therefore, our algorithm is less affected by parallel BLAS and shows good performance compared to UMFPACK. Data structure and memory management are different in ParU, so it needs less memory to solve a system. The current algorithm is implemented for a shared memory environment. However, the algorithm can be an excellent candidate to be implemented in a distributed environment. Moreover, while there are parallel BLAS calls (typically larger than what UMFPACK has), it is also an excel-lent candidate for using hardware accelerators like GPUs. The electronic version of this dissertation is accessible from https://hdl.handle.net/1969.1/198633
  note: by Mohsen Mahmoudi Aznaveh.
  note: "Major Subject: Computer Engineering"
  note: Includes vita.
  note: Doctor of Philosophy Texas A&M University 2022 https://hdl.handle.net/1969.1/198633
  note: Includes bibliographical references.
  note: Text (Dissertation)
  subject:
    topic: Major Computer Engineering
  subject:
    topic: Sparse linear algebra
  subject:
    topic: Matrix computation
  identifier: https://hdl.handle.net/1969.1/198633
  record info:
    record content source: TXA
    record creation date: 241022
    record change date: 20250326141434.6
    record identifier: in00005621353
    language of cataloging:
      language term: eng