Year: 2023
Type: report
Abstract: This manual describes the use of the Portable, Extensible Toolkit for Scientific Computation (PETSc) and the Toolkit for Advanced Optimization (TAO) for the numerical solution of partial differential equations and related problems on high-performance computers. PETSc/TAO is a suite of data structures and routines that provide the building blocks for the implementation of large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all distributed memory communication. PETSc/TAO includes a large suite of parallel linear solvers, nonlinear solvers, time integrators, and opti mization that may be used in application codes written in Fortran, C, C++, and Python (via petsc4py; see Getting Started). PETSc provides many of the mechanisms needed within parallel application codes, such as parallel matrix and vector assembly routines. The library is organized hierarchically, enabling users to employ the level of abstraction that is most appropriate for a particular problem. By using techniques of object-oriented programming, PETSc provides enormous flexibility for users. PETSc is a sophisticated set of software tools; as such, for some users it initially has a much steeper learning curve than packages such as MATLAB or a simple subroutine library. In particular, for individuals without some computer science background, experience programming in C, C++, python, or Fortran and experience using a debugger such as gdb or lldb, it may require a significant amount of time to take full advantage of the features that enable efficient software use. However, the power of the PETSc design and the algorithms it incorporates may make the efficient implementation of many application codes simpler than “rolling them” yourself. For many tasks a package such as MATLAB is often the best tool; PETSc is not intended for the classes of problems for which effective MATLAB code can be written. There are several packages, built on PETSc, that may satisfy your needs without requiring directly using PETSc. We recommend reviewing these packages functionality before starting to code directly with PETSc. PETSc can be used to provide a “MPI parallel linear solver” in an otherwise sequential, or OpenMP parallel code. This approach cannot provide extremely large improvements in the application time by utilizing large numbers of MPI processes but can still improve the performance. Certainly all parts of a previously sequential code need not be parallelized but the matrix generation portion must be parallelized to expect true scalability to large numbers of MPI processes. See PCMPI for details on how to utilize the PETSc MPI linear solver server. Since PETSc is under continued development, small changes in usage and calling sequences of routines will occur. PETSc has been supported for twenty-five years; see mailing list information on our website for information on contacting support.
DOI: 10.2172/1968587
Authors: S. Balay, S. Abhyankar, Mark Adams, S. Benson, J. Brown, P. Brune, K. Buschelman, E. Constantinescu, L. Dalcin, A. Dener, V. Eijkhout, J. Faibussowitsch, W. Gropp, V. Hapla, T. Isaac, P. Jolivet, D. Karpeev, D. Kaushik, M. Knepley, F. Kong, S. Kruger, D. May, L. McInnes, R. Mills, L. Mitchell, T. Munson, J. Roman, K. Rupp, P. Sanan, J. Sarich, B. Smith, S. Zampini, H. Zhang, H. Zhang, J. Zhang
Institutions: Argonne National Laboratory, Pacific Northwest National Laboratory, Lawrence Berkeley National Laboratory, University of Colorado Boulder, University of Colorado System, United States Department of Energy, Office of Scientific and Technical Information, University of Chicago, National Technical Information Service, King Abdullah University of Science and Technology, Texas Advanced Computing Center, The University of Texas at Austin, University of Illinois Urbana-Champaign, ETH Zurich, Georgia Institute of Technology, Sorbonne Université, Institut de Recherche en Informatique de Toulouse, University at Buffalo, State University of New York, Idaho National Laboratory, Tech-X Corporation (United States), Scripps Institution of Oceanography, University of California San Diego, University of Oxford, Durham University, Nvidia (United States), Consejo Superior de Investigaciones Científicas, Universitat Politècnica de València, TU Wien, Institute of Geophysics, Simons Foundation, Flatiron Institute, Illinois Institute of Technology
Language: English
Topic: Matrix Theory and Algorithms
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4362717258