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Cited by 67Matrix Theory and AlgorithmsParallel Computing and Optimization TechniquesAdvanced Numerical Methods in Computational Mathematics

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PETSc/TAO Users Manual (Rev. 3.19)

Open access
Argonne National Laboratory (ANL), Argonne, IL (United States), Balay, S., USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR), Abhyankar, S., Adams, Mark, Benson, S., Brown, J., Brune, P., Buschelman, K., Constantinescu, E., Dalcin, L., Dener, A., Eijkhout, V., Faibussowitsch, J., Gropp, W., Hapla, V., Isaac, T., Jolivet, P., Karpeev, D., Kaushik, D., Knepley, M., Kong, F., Kruger, S., May, D., McInnes, L., Mills, R., Mitchell, L., Munson, T., Roman, J., Rupp, K., Sanan, P., Sarich, J., Smith, B., Zampini, S., Zhang, H., Zhang, H., Zhang, J.·2023
DOI 10.2172/1968587
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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.

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Metadata

Authors
Argonne National Laboratory (ANL), A., Balay, S., USDOE Office of Science (SC), A. S. C. R. (., Abhyankar, S., Adams, M., Benson, S., Brown, J., Brune, P., Buschelman, K., Constantinescu, E., Dalcin, L., Dener, A., Eijkhout, V., Faibussowitsch, J., Gropp, W., Hapla, V., Isaac, T., Jolivet, P., Karpeev, D., Kaushik, D., Knepley, M., Kong, F., Kruger, S., May, D., McInnes, L., Mills, R., Mitchell, L., Munson, T., Roman, J., Rupp, K., Sanan, P., Sarich, J., Smith, B., Zampini, S., Zhang, H., Zhang, H., & Zhang, J.
Affiliations
  • Argonne National Laboratory (US)
  • Lawrence Berkeley National Laboratory (US)
  • King Abdullah University of Science and Technology (KAUST), Thuwal (Saudi Arabia)
  • Texas Advanced Computing Center (US)
  • ETH Zurich (CH)
  • Sorbonne Université (FR)
  • Tech-X Corporation (United States) (US)
  • Scripps Institution of Oceanography (US)
  • Durham University (GB)
  • Spanish National Research Council and Polytechnic Univ. of Valencia, Valencia (Spain)
  • King Abdullah University of Science and Technology (SA)
ORCID
Publisher
Office of Scientific and Technical Information (OSTI)
Published
2023-03-30
DOI
10.2172/1968587
OpenAlex
W4362717258
Cited by
67
References
1
Citations by year
2026: 5 · 2025: 9 · 2024: 28 · 2023: 23
Type
report
Language
en
Open access
Locations
OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
Funders
  • USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
  • DOE (AC02-06CH11357)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Matrix Theory and Algorithms
  • Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
  • Physical Sciences › Engineering › Computational Mechanics › Advanced Numerical Methods in Computational Mathematics
Keywords
PETSc, Tao, parallel computing, partial differential equations, parallel linear solvers, nonlinear solvers, time integrators, optimization, MPI, object-oriented programming, high-performance computing, scientific libraries, parallel preconditioning, parallel multigrid, parallel domain decomposition

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