Abstract
We are developing a lazy, self-optimising parallel library of vector-matrix routines. The aim is to allow users to parallelise certain computationally expensive parts of numerical programs by simply linking with a parallel rather than sequential library of subroutines. The library performs interprocedural data placement optimisation at runtime, which requires the optimiser itself to be very efficient. We achieve this firstly by working from aggregate loop nests which have been optimised in isolation, and secondly by using a carefully constructed mathematical formulation for data distributions and the distribution requirements of library operators, which allows us largely to replace searching with calculation in our algorithm.
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Keywords
- Conjugate Gradient Method
- Distribution Requirement
- Lazy Evaluation
- Parallel Library
- Conjugate Gradient Solver
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
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Olav Beckmann. A lazy, self-optimising parallel matrix library. Master's thesis, Department of Computing, Imperial College, London SW7 2AZ, U.K., 1996.
Paul Feautrier. Toward automatic distribution. Parallel Processing Letters, 4(3):233–244, 1994.
Simon Govier and Paul H. J. Kelly. A lazy, self-optimising parallel matrix library. In David N. Turner et al., editor, Glasgow Functional Programming Workshop, Ullapool, July 1995. Springer-Verlag.
Mary E. Mace. Storage Patterns in Parallel Processing. Kluwer Academic Press, 1987.
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© 1997 Springer-Verlag Berlin Heidelberg
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Beckmann, O., Kelly, P.H.J. (1997). Runtime interprocedural data placement optimisation for lazy parallel libraries (extended abstract). In: Lengauer, C., Griebl, M., Gorlatch, S. (eds) Euro-Par'97 Parallel Processing. Euro-Par 1997. Lecture Notes in Computer Science, vol 1300. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0002749
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DOI: https://doi.org/10.1007/BFb0002749
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