Abstract
PyTrilinos is a collection of Python modules targeting serial and parallel sparse linear algebra, direct and iterative linear solution techniques, domain decomposition and multilevel preconditioners, nonlinear solvers and continuation algorithms. Also included are a variety of related utility functions and classes, including distributed I/O, coloring algorithms and matrix generation. PyTrilinos vector objects are integrated with the popular NumPy module, gathering together a variety of high-level distributed computing operations with serial vector operations.
PyTrilinos uses a hybrid development approach, with a front-end in Python, and a back-end, computational engine in compiled libraries. As such, PyTrilinos makes it easy to take advantage of both the flexibility and ease of use of Python, and the efficiency of the underlying C++, C and FORTRAN numerical kernels. The presented numerical results show that, for many important problem classes, the overhead required by the Python interpreter is negligible.
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© 2007 Springer-Verlag Berlin Heidelberg
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Sala, M., Spotz, W.F., Heroux, M.A. (2007). PyTrilinos: High-Performance Distributed-Memory Solvers for Python. In: Kågström, B., Elmroth, E., Dongarra, J., Waśniewski, J. (eds) Applied Parallel Computing. State of the Art in Scientific Computing. PARA 2006. Lecture Notes in Computer Science, vol 4699. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75755-9_114
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DOI: https://doi.org/10.1007/978-3-540-75755-9_114
Publisher Name: Springer, Berlin, Heidelberg
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