A highly optimized skeleton for unbalanced and deep divide-and-conquer algorithms on multi-core clusters

被引:0
|
作者
Millán A. Martínez
Basilio B. Fraguela
José C. Cabaleiro
机构
[1] Universidade da Coruña,Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Dpto. Electrónica e Computación
[2] CITIC,undefined
[3] Computer Architecture Group,undefined
[4] Universidade de Santiago de Compostela,undefined
来源
关键词
Algorithmic skeletons; Divide-and-conquer; Template metaprogramming; Load balancing; Multi-core clusters; Hybrid parallelism;
D O I
暂无
中图分类号
学科分类号
摘要
Efficiently implementing the divide-and-conquer pattern of parallelism in distributed memory systems is very relevant, given its ubiquity, and difficult, given its recursive nature and the need to exchange tasks and data among the processors. This task is noticeably further complicated in the presence of multi-core systems, where hybrid parallelism must be exploited to attain the best performance, and when unbalanced and deep workloads are considered, as additional measures must be taken to load balance and avoid deep recursion problems. In this manuscript a parallel skeleton that fulfills all these requirements while providing high levels of usability is presented. In fact, the evaluation shows that our proposal is on average 415.32% faster than MPI codes and 229.18% faster than MPI + OpenMP benchmarks, while offering an average improvement in the programmability metrics of 131.04% over MPI alternatives and 155.18% over MPI + OpenMP solutions.
引用
收藏
页码:10434 / 10454
页数:20
相关论文
共 21 条
  • [21] MPI Collectives for Multi-core Clusters: Optimized Performance of the Hybrid MPI plus MPI Parallel Codes
    Zhou, Huan
    Gracia, Jose
    Schneider, Ralf
    PROCEEDINGS OF THE 48TH INTERNATIONAL CONFERENCE ON PARALLEL PROCESSING WORKSHOPS (ICPP 2019), 2019,