Talks - Henrik Barthels

  1. Parallelism in Linnea
    20th Workshop on Compilers for Parallel Computing.
    Dublin, Ireland, 18 April 2018.
    Linnea is an experimental tool for the automatic translation of linear algebra expressions to efficient programs consisting of a sequence of calls to BLAS and LAPACK kernels. Linnea generates programs by constructing a search graph, where each path in the graph represents one program. We introduce two problems related to parallelism that arise in Linnea. Those problems consist in 1) parallelizing the construction of the search graph and 2) generating parallel programs.
  2. The Generalized Matrix Chain Algorithm
    2018 IEEE/ACM International Symposium on Code Generation and Optimization.
    Vienna, Austria, 27 February 2018.
    In this paper, we present a generalized version of the matrix chain algorithm to generate efficient code for linear algebra problems, a task for which human experts often invest days or even weeks of works. The standard matrix chain problem consists in finding the parenthesization of a matrix product $M := A_1 A_2 \cdots A_n$​ that minimizes the number of scalar operations. In practical applications, however, one frequently encounters more complicated expressions, involving transposition, inversion, and matrix properties. Indeed, the computation of such expressions relies on a set of computational kernels that offer functionality well beyond the simple matrix product. The challenge then shifts from finding an optimal parenthesization to finding an optimal mapping of the input expression to the available kernels. Furthermore, it is often the case that a solution based on the minimization of scalar operations does not result in the optimal solution in terms of execution time. In our experiments, the generated code outperforms other libraries and languages on average by a factor of about 5. The motivation for this work comes from the fact that---despite great advances in the development of compilers---the task of mapping linear algebra problems to optimized kernels is still to be done manually. In order to relieve the user from this complex task, new techniques for the compilation of linear algebra expressions have to be developed.
  3. Linnea: Automatic Generation of Efficient Linear Algebra Programs
    Umeå University, 12 January 2018.
    The evaluation of linear algebra expressions is a central part of both languages for scientific computing such as Julia and Matlab, and libraries such as Eigen, Blaze, and NumPy. However, the existing strategies are still rather primitive. At present, the only way to achieve high performance is by handcoding algorithms using libraries such as BLAS and LAPACK, a task that requires extensive knowledge in linear algebra, numerical linear algebra and high-performance computing. We present Linnea, the prototype of a compiler that automates the translation of the mathematical description of a linear algebra problem to an efficient sequence of calls to BLAS and LAPACK kernels. The main idea of Linnea is to construct a search graph that represents a large number of programs, taking into account knowledge about linear algebra. The algebraic nature of the domain is used to reduce the size of the search graph, without reducing the size of the search space that is explored. Experiments show that 1) the code generated by Linnea outperforms standard linear algebra languages and libraries, and 2) in contrast to the development time of human experts, the generation takes only few seconds.
  4. Efficient Pattern Matching in Python
    Manuel Krebber, Henrik Barthels and Paolo Bientinesi
    7th Workshop on Python for High-Performance and Scientific Computing.
    Denver, Colorado, 12 November 2017.
  5. Compiling Linear Algebra Expressions to High-Performance Code
    8th International Workshop on Parallel Symbolic Computation (PASCO).
    Kaiserslautern, July 2017.
    Vectors, matrices and tensors are the mathematical objects universally used to describe scientific phenomena, engineering processes, and numerical algorithms. By contrast, processors only operate with scalars and small arrays, and do not understand the language and the rules of linear algebra. Because of this mismatch, any linear algebra expression has to be translated in terms of the instructions supported by the specific target processor. Over the course of many years, the linear algebra community has put tremendous effort in the identification, standardization, and optimization of a rich set of relatively simple computational kernels--such as those included in the BLAS and LAPACK libraries--that provide the necessary building blocks for just about any linear algebra computation. The initial--daunting--task has thus been reduced to the decomposition of a target linear algebra expression in terms of said building blocks; we refer to this task as the "Linear Algebra Mapping Problem" (LAMP). However, LAMP is itself an especially challenging problem, requiring knowledge in high-performance computing, compilers, and numerical linear algebra. In this talk we present the problem, we give an overview of the solutions provided by several programming languages and computing environments (such as Julia, Matlab, R, ...), and introduce Linnea, a compiler to solve the general form of LAMP. As shown through a set of test cases, Linnea's results are comparable with those obtained by a human expert.
  6. Linnea: Automatic Generation of Efficient Linear Algebra Programs
    • Massachusetts Institute of Technology, 24 May 2017.
    • University of Nevada, Las Vegas, 17 May 2017.
  7. A Compiler for Linear Algebra Operations
    ACM Student Research Competition at SPLASH 2016.
    Amsterdam, Netherlands, 3 November 2016.
  8. The Matrix Chain Algorithm to Compile Linear Algebra Expressions
    4th Workshop on Domain Specific Language Design and Implementation (DSLDI).
    Amsterdam, Netherlands, 31 October 2016.
  9. Knowledge-Based Linear Algebra Compiler
    AICES Annual Retreat 2015.
    15 October 2015.
  10. Systematic Generation of Algorithms for Iterative Methods
    RWTH Aachen University, April 2015.
    Master Thesis Presentation.
  11. Automata-Based Detection of Hypergraph Embeddings
    RWTH Aachen University, October 2011.
    Bachelor's Thesis Presentation.