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Scientific Programming
Volume 6, Issue 1, Pages 59-72

Performance Issues in High Performance Fortran Implementations of Sensor-Based Applications

David R. O'hallaron, Jon Webb, and Jaspal Subhlok

School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA

Received 26 June 1995; Accepted 26 February 1996

Copyright © 1997 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Applications that get their inputs from sensors are an important and often overlooked application domain for High Performance Fortran (HPF). Such sensor-based applications typically perform regular operations on dense arrays, and often have latency and through put requirements that can only be achieved with parallel machines. This article describes a study of sensor-based applications, including the fast Fourier transform, synthetic aperture radar imaging, narrowband tracking radar processing, multibaseline stereo imaging, and medical magnetic resonance imaging. The applications are written in a dialect of HPF developed at Carnegie Mellon, and are compiled by the Fx compiler for the Intel Paragon. The main results of the study are that (1) it is possible to realize good performance for realistic sensor-based applications written in HPF and (2) the performance of the applications is determined by the performance of three core operations: independent loops (i.e., loops with no dependences between iterations), reductions, and index permutations. The article discusses the implications for HPF implementations and introduces some simple tests that implementers and users can use to measure the efficiency of the loops, reductions, and index permutations generated by an HPF compiler.