h1

% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@PHDTHESIS{Barthels:918229,
      author       = {Barthels, Henrik},
      othercontributors = {Bientinesi, Paolo and Naumann, Uwe and Püschel, Markus},
      title        = {{L}innea: a compiler for mapping linear algebra problems
                      onto high-performance kernel libraries},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2023-01193},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2022},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University 2023; Dissertation, RWTH Aachen University, 2022},
      abstract     = {The translation of linear algebra computations to efficient
                      code consisting of kernels as provided by libraries such as
                      BLAS and LAPACK is a frequently encountered problem in
                      different areas of science and engineering. Since the manual
                      translation is a tedious, error-prone and time consuming
                      process that requires a lot of expertise, several high-level
                      languages and libraries have been developed that solve this
                      problem automatically. Example include Matlab, Julia, Eigen,
                      and Armadillo. These languages and libraries allow users to
                      describe linear algebra computations in code which closely
                      resembles the mathematical description of the problem; this
                      high-level code is then internally translated to sequences
                      of kernel calls. Unfortunately, while those languages and
                      libraries increase the productivity of the user, it has been
                      shown that this automatic translation frequently results in
                      suboptimal code. In this thesis, we present Linnea, a
                      domain-specific compiler that automatically translates the
                      high-level description of linear algebra problems to
                      efficient sequences of kernel calls. Linnea aims to combine
                      the ease-of-use of high-level languages with the performance
                      of low-level code written by an expert. Unlike other
                      languages and libraries, Linnea extensively makes use of
                      knowledge about linear algebra: Algebraic identities such as
                      associativity, commutativity, and distributivity are used to
                      rewrite the input problem. In addition, Linnea is able to
                      infer matrix properties and detect common subexpressions. To
                      explore the large space of candidate solutions, Linnea uses
                      a custom best-first search algorithm that quickly finds a
                      first solution, and increasingly better solutions when given
                      more time. This thesis concludes with an extensive
                      experimental evaluation of Linnea on 25 application and 100
                      synthetic test problems, both with sequential and parallel
                      execution. The results show that Linnea almost always
                      outperforms Matlab, Julia, Eigen and Armadillo, with
                      speedups up to and exceeding 10x. For all test problems,
                      Linnea finds a first solution in less than one second;
                      finding a close to optimal solution rarely takes longer than
                      a few minutes.},
      cin          = {123120 / 120000},
      ddc          = {004},
      cid          = {$I:(DE-82)123120_20140620$ / $I:(DE-82)120000_20140620$},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2023-01193},
      url          = {https://publications.rwth-aachen.de/record/918229},
}