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.
[1]
Ken Kennedy,et al.
Compiling Fortran D for MIMD distributed-memory machines
,
1992,
CACM.
[2]
Peter Steenkiste,et al.
Parallel and distributed application of an urban-to-regional multiscale model
,
1997
.
[3]
David R. O'Hallaron,et al.
An architecture for optimal all-to-all personalized communication
,
1994,
SPAA '94.
[4]
John R. Gilbert,et al.
Generating Local Address and Communication Sets for Data-Parallel Programs
,
1995,
J. Parallel Distributed Comput..
[5]
Allan L. Fisher,et al.
Flattening and parallelizing irregular, recurrent loop nests
,
1995,
PPOPP '95.
[6]
Thomas R. Gross,et al.
Optimizing memory system performance for communication in parallel computers
,
1995,
Proceedings 22nd Annual International Symposium on Computer Architecture.
[7]
Barbara M. Chapman,et al.
A Software Architecture for Multidisciplinary Applications: Integrating Task and Data Parallelism
,
1994,
CONPAR.