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@INPROCEEDINGS{DiNapoli:811971,
      author       = {Di Napoli, Edoardo},
      title        = {{T}he {C}h{ASE} library on distributed and heterogeneous
                      platforms},
      reportid     = {FZJ-2016-04275},
      year         = {2016},
      abstract     = {We propose to step away from the black-box approach and
                      allow the eigensolver to accept as much information as it is
                      available from the application. Such a strategy implies that
                      the resulting library is tailored to the specific
                      application, or class of applications, and loose generality
                      of usage. On the other hand, the resulting eigensolver
                      maximally exploits knowledge from the application and become
                      very efficient. With this general strategy in mind, we
                      present here a version of a Chebyshev Accelerated Subspace
                      iteration Eigensolver (ChASE) which targets extremal
                      eigenpairs of dense eigenproblems. In particular, ChASE
                      focuses of on a class of applications resulting in having to
                      solve sequences of eigenvalue problems where adjacent
                      problems possess a certain degree of correlation. A typical
                      example of such applications is Density Functional Theory
                      where the solution to a non-linear partial differential
                      equation is worked out by generating and solving dozens of
                      algebraic eigenvalue problems in a self- consistent fashion
                      over dozens of iterations. Similarly, any non-linear
                      eigenvalue problem, which can be solved by the method of
                      successive linearization, gives rise to sequences of
                      correlated algebraic eigenproblems that are the target of
                      ChASE. We re-design the eigensolver so as to minimize its
                      complexity and have better control of its numerical
                      features. Following the algorithm optimizations, we strive
                      to adopt a strategy leading to an implementation that would
                      lends itself to high-performance parallel computing and
                      avoid, at the same time, issues related to portability to
                      heterogeneous architectures. We achieve such a goal by
                      implementing parallel kernels for the modular tasks of the
                      eigensolver using programming strategies out of MPI, OpenMP,
                      and CUDA.},
      month         = {Jul},
      date          = {2016-07-06},
      organization  = {Parallel Matrix Algorithms and
                       Applications, Bordeaux (France), 6 Jul
                       2016 - 8 Jul 2016},
      subtyp        = {After Call},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511) / Simulation and Data Laboratory Quantum
                      Materials (SDLQM) (SDLQM)},
      pid          = {G:(DE-HGF)POF3-511 / G:(DE-Juel1)SDLQM},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/811971},
}