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HPSC
  • Fall 2019
Logistics
Syllabus
Lecture Notes
  • 2019-08-28 Architecture
  • 2019-08-30 Vectorization
  • 2019-09-04 Modeling
  • 2019-09-06 Parallel Scaling
  • 2019-09-11 OpenMP Basics
  • 2019-09-13 More OpenMP
  • 2019-09-16 OpenMP Tasks
  • 2019-09-18 Reductions and Scans
  • 2019-09-23 More bitonic sorting, graphs
  • 2019-09-24 Introduction to MPI
  • 2019-09-30 Dense Linear Algebra
  • 2019-10-02 Dense Linear Algebra and Orthogonality
  • 2019-10-04 Orthogonality and Conditioning
  • 2019-10-07 Elemental
  • 2019-10-09 Sparse and Iterative
  • 2019-10-11 Preconditioning
  • 2019-10-14 DD Preconditioning
  • 2019-10-16 DD Preconditioning 2
  • 2019-10-18 Multilevel Preconditioning
  • 2019-10-21 Nonlinear
  • 2019-10-23 Transient
  • 2019-10-25 libCEED
  • 2019-10-28 Coprocessors
  • 2019-10-30 GPUs and CUDA
  • 2019-11-01 Practical CUDA
  • 2019-11-04 ISP/OpenMP/OpenACC
  • 2019-11-06 HPC I/O
  • 2019-11-08 MPI-IO
  • 2019-11-11 Data-intensive
  • 2019-11-13 Data and Probability
  • 2019-11-15 Dynamic/Interactive
  • 2019-11-18 Intro N-body
  • 2019-11-20 Long-range N-body
  • 2019-11-22 Molecular Dynamics
  • 2019-12-02 Git Workflows
  • 2019-12-04 Fourier
  • 2019-12-06 Multigrid Intro
  • 2019-12-09 Algebraic Multigrid
Resources
  • Trends
  • Contents
    • FLAME diagram for Cholesky
    • $A[M_C, M_R]$ distribution
    • The $A[,]$ distribution
    • References

Elemental for distributed dense linear algebra

FLAME diagram for Cholesky

$A[M_C, M_R]$ distribution

The $A[,]$ distribution

References

  • Poulson et al. (2013) Elemental: A New Framework for Distributed Memory Dense Matrix Computations doi:10.1145⁄2427023.2427030
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Sparse and iterative linear algebra
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Orthogonality and Conditioning

Last updated on Oct 8, 2019

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