Computational Technology Resources - CCP

Keywords: parallel libraries, Python interfaces, model reduction, dynamical linear time-invariant systems.

Summary

Scripting languages have gained popularity in the computational sciences community, in part because of the complexity of codes, limited computational resources, large volumes of data produced and setup of large simulation runs. Python is one of these scripting languages, and it provides reduced development times with a high integration of the fairly large individual codes. In this paper we present a Python based high level interface to a parallel library for model reduction of dynamical linear time-invariant (LTI) systems, called PLiCMR [1]. The parallel model reduction addressed by the PLiCMR library provides absolute error methods, based on similarity transformations. The functionality and naming convention of the parallel routines closely follow analogous routines from SLICOT [2]. These routines have been implemented in Fortran using the kernels in ScaLAPACK [3].

The proposed interfaces, named PyPLiC, reduce the time users spend prototyping and deploying high-end software tools like PLiCMR. Implementations details and capabilities of the developed interfaces are given. PyPLiC reduces the number of input parameters to only those that modify the behaviour of the PLiCMR routines. The rest of parameters, such as the desired order of the resulting reduced-order system, working arrays or ScaLAPACK descriptors of matrices, are initialized to values-by-default, or are automatically computed by the interface. Nevertheless, these default values can still be modified by the user. Also, PyPLiC offers access to all three PLiCMR driver routines under the umbrella of a single routine, so that the PLiCMR routine to be used is selected via one additional parameter of the interface. Another improvement of PyPLiC is how it manages the input data matrices and the data distribution across the processors. PyPLiC has three different options to store the data matrices: in Matrix Market format files, in a Numerical Python array, (using a two-dimensional array), and stored as a 2D block-cyclic ScaLAPACK distributed matrix (using a one-dimensional array). On the other hand, PyPLiC has four options to perform the data distribution: PLiCMR.so, PyPLiC, Num2PyACTS and Not distribute option.

The results show not only the many advantages of using the simplified interfaces but also that there are not major performance degradations to pay for the use of the PyPLiC interfaces. The high level interfaces enable the possibility of adding further improvements, specially in the management and distribution of matrices.

References

1: P. Benner, E.S. Quintana-Ortí, G. Quintana-Ortí, "State-space truncation methods for parallel model reduction of large-scale systems", Parallel Computing, 29, 1701-1722, 2003. doi:10.1016/j.parco.2003.05.013
2: The Working Group on Software: WGS. "SLICOT Implementation and Documentation Standards 2.1", WGS-report 96-1, 1996.
3: L.S. Blackford, J. Choi, A. Cleary, E. D'Azevedo, J. Demmel, I. Dhillon, J. Dongarra, S. Hammarling, G. Henry, A. Petitet, K. Stanley, D. Walker, and R.C. Whaley, "ScaLAPACK Users' Guide", SIAM, Philadelphia, PA, 1997.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 86 PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING COMPUTING Edited by: B.H.V. Topping Paper 62 PyPLiC: A High-Level Interface to the Parallel Model Reduction Library PLiCMR V. Galiano¹, A. Martín², H. Migallón¹, V. Migallón³, J. Penadés³ and E.S. Quintana-Ortí² ¹Department of Physics and Computer Architectures, University Miguel Hernández, Elche, Alicante, Spain ²Department of Engineering and Science of Computers, University Jaume I, Castellón, Spain ³Department of Computational Science and Artificial Intelligence, University of Alicante, Spain doi:10.4203/ccp.86.62 purchase the full-text of this paper Full Bibliographic Reference for this paper , "PyPLiC: A High-Level Interface to the Parallel Model Reduction Library PLiCMR", in B.H.V. Topping, (Editor), "Proceedings of the Eleventh International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Stirlingshire, UK, Paper 62, 2007. doi:10.4203/ccp.86.62 Keywords: parallel libraries, Python interfaces, model reduction, dynamical linear time-invariant systems. Summary Scripting languages have gained popularity in the computational sciences community, in part because of the complexity of codes, limited computational resources, large volumes of data produced and setup of large simulation runs. Python is one of these scripting languages, and it provides reduced development times with a high integration of the fairly large individual codes. In this paper we present a Python based high level interface to a parallel library for model reduction of dynamical linear time-invariant (LTI) systems, called PLiCMR [1]. The parallel model reduction addressed by the PLiCMR library provides absolute error methods, based on similarity transformations. The functionality and naming convention of the parallel routines closely follow analogous routines from SLICOT [2]. These routines have been implemented in Fortran using the kernels in ScaLAPACK [3]. The proposed interfaces, named PyPLiC, reduce the time users spend prototyping and deploying high-end software tools like PLiCMR. Implementations details and capabilities of the developed interfaces are given. PyPLiC reduces the number of input parameters to only those that modify the behaviour of the PLiCMR routines. The rest of parameters, such as the desired order of the resulting reduced-order system, working arrays or ScaLAPACK descriptors of matrices, are initialized to values-by-default, or are automatically computed by the interface. Nevertheless, these default values can still be modified by the user. Also, PyPLiC offers access to all three PLiCMR driver routines under the umbrella of a single routine, so that the PLiCMR routine to be used is selected via one additional parameter of the interface. Another improvement of PyPLiC is how it manages the input data matrices and the data distribution across the processors. PyPLiC has three different options to store the data matrices: in Matrix Market format files, in a Numerical Python array, (using a two-dimensional array), and stored as a 2D block-cyclic ScaLAPACK distributed matrix (using a one-dimensional array). On the other hand, PyPLiC has four options to perform the data distribution: PLiCMR.so, PyPLiC, Num2PyACTS and Not distribute option. The results show not only the many advantages of using the simplified interfaces but also that there are not major performance degradations to pay for the use of the PyPLiC interfaces. The high level interfaces enable the possibility of adding further improvements, specially in the management and distribution of matrices. References 1 P. Benner, E.S. Quintana-Ortí, G. Quintana-Ortí, "State-space truncation methods for parallel model reduction of large-scale systems", Parallel Computing, 29, 1701-1722, 2003. doi:10.1016/j.parco.2003.05.013 2 The Working Group on Software: WGS. "SLICOT Implementation and Documentation Standards 2.1", WGS-report 96-1, 1996. 3 L.S. Blackford, J. Choi, A. Cleary, E. D'Azevedo, J. Demmel, I. Dhillon, J. Dongarra, S. Hammarling, G. Henry, A. Petitet, K. Stanley, D. Walker, and R.C. Whaley, "ScaLAPACK Users' Guide", SIAM, Philadelphia, PA, 1997. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £120 +P&P)
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