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Preliminary Implementation of PETSc Using GPUs

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GPU Solutions to Multi-scale Problems in Science and Engineering

Part of the book series: Lecture Notes in Earth System Sciences ((LNESS))

Abstract

PETSc is a scalable solver library for the solution of algebraic equations arising from the discretization of partial differential equations and related problems. PETSc is organized as a class library with classes for vectors, matrices, Krylov methods, preconditioners, nonlinear solvers, and differential equation integrators. A new subclass of the vector class has been introduced that performs its operations on NVIDIA GPU processors. In addition, a new sparse matrix subclass that performs matrix-vector products on the GPU was introduced. The Krylov methods, nonlinear solvers, and integrators in PETSc run unchanged in parallel using these new subclasses. These can be used transparently from existing PETSc application codes in C, C++, Fortran, or Python. The implementation is done with the Thrust and Cusp C++ packages from NVIDIA.

The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.

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Notes

  1. 1.

    Thrust is a CUDA library of parallel algorithms with an interface resembling the C++ Standard Template Library (STL). Thrust provides a flexible high-level interface for GPU programming that greatly enhances developer productivity.

  2. 2.

    Cusp is a library for sparse linear algebra and graph computations on CUDA that uses Thrust.

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Acknowledgments

We thank Nathan Bell from NVIDIA and Lisandro Dalcin for their assistance with this project. This work was supported by the Office of Advanced Scientific Computing Research, Office of Science, U.S. Department of Energy, under Contract DE-AC02-06CH11357.

Author information

Authors and Affiliations

  1. School of Engineering, Tufts University, Medford, MA, 02155, USA

    Victor Minden

  2. Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, 60439-4844, USA

    Barry Smith

  3. Computation Institute, University of Chicago, Chicago, IL, 60637, USA

    Matthew G. Knepley

Authors
  1. Victor Minden
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  2. Barry Smith
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  3. Matthew G. Knepley
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Corresponding author

Correspondence to Victor Minden .

Editor information

Editors and Affiliations

  1. University of Minnesota, Dep. of Earth Sciences and Minnesota, Supercomputing Institute, Pillsbury Hall 23, Minneapolis, 55455, Minnesota, USA

    David A. Yuen

  2. Network Information Center, Comuter Center and Computer, Zhong Guan Cun 4, Beijing, 100190, China, People's Republic

    Long Wang

  3. Supercomputing Center, Zhong Guan Cun 4, Beijing, 100190, China, People's Republic

    Xuebin Chi

  4. , Computer Science, University of Houston, Calhoun Street 4800, Houston, 77204, Texas, USA

    Lennart Johnsson

  5. Inst. Process Engineering (IPE), Chinese Academy of Sciences, Zhongguancun North Second Street 1, Beijing, 100190, China, People's Republic

    Wei Ge

  6. , Laboratory of Computational Geodynamics,, Chinese Academy of Sciences, Yu Quan Lu 19a, Beijing, 100049, China, People's Republic

    Yaolin Shi

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Minden, V., Smith, B., Knepley, M.G. (2013). Preliminary Implementation of PETSc Using GPUs. In: Yuen, D., Wang, L., Chi, X., Johnsson, L., Ge, W., Shi, Y. (eds) GPU Solutions to Multi-scale Problems in Science and Engineering. Lecture Notes in Earth System Sciences. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16405-7_7

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