Abstract
The numerical analysis and solution of many physics and engineering problems is based on lattice-oriented algorithms. The Cellular Neural Network (CNN) computational paradigm embodies a wide set of grid problems characterized by locality of information exchanges among lattice points. Performance analysis tests using CNN-based algorithms may provide insights into the performance achievable by a given parallel architecture, with respect to a wide class of lattice problems. In this paper a message passing version of a general CNN-based algorithm is implemented and optimized for three general purpose parallel architectures: Connection Machine CM-5, Cray T3D, and IBM SP2. Separate measurements on computations and communications of the algorithm allow us to evaluate processing node and network communication performance of the machines. Moreover. the overall performance of the full application is analyzed, in order to understand the scalability and the range of applicability of this prototype of lattice problem.
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