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Programming Models for Massively Parallel Computers (PMMP '95)   p. 142
Distributed memory implementation of elliptic partial differential equations in a dataparallel functional language
H. Kuchen, Lehrstuhl fur Inf. II, Tech. Hochschule Aachen, Germany
H. Stoltze, Lehrstuhl fur Inf. II, Tech. Hochschule Aachen, Germany
I. Dimov, Lehrstuhl fur Inf. II, Tech. Hochschule Aachen, Germany
A. Karaivanova, Lehrstuhl fur Inf. II, Tech. Hochschule Aachen, Germany

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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/PMMPC.1995.504352
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Abstract
We show that the numerical solution of partial differential equations can be elegantly and efficiently addressed in a functional language. Two statistical numerical methods are considered. We discuss why current parallel imperative languages are difficult to use and why general (expression parallel) functional languages are not efficient enough. The key point of our approach is to offer "unique" arrays and some operations on them which allow to handle their elements in parallel, including operations which exchange the partitions of an array between the processors. These operations constitute a deadlock-free high-level way of communication.
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Index Terms- partial differential equations; elliptic equations; parallel algorithms; distributed memory systems; elliptic partial differential equations; dataparallel functional language; distributed memory; parallel imperative languages; functional languages; deadlock-free Citation:  H. Kuchen, H. Stoltze, I. Dimov, A. Karaivanova, "Distributed memory implementation of elliptic partial differential equations in a dataparallel functional language," pmmp, p. 142,  Programming Models for Massively Parallel Computers (PMMP '95),  1995