Performance Issues for Sensor-Based HPF Programs


Sensor-based computations are an important and often overlooked application domain for HPF. These applications typically perform regular operations on dense arrays, and often have latency and throughput requirements that can only be achieved with parallel machines. We have written a number of sensor-based applications using a dialect of subset HPF that was developed at Carnegie Mellon. The applications include FFT, synthetic aperture radar, narrowband tracking radar, multi-baseline stereo, and magnetic resonance imaging. We have found that good performance is possible for these applications on commercial machines such as the Intel Paragon. In the paper we identify three core operations that are key to achieving good performance for sensor--based computations: parallel loops, index permutations, and reductions and we discuss the implications for HPF compilers. We also introduce some simple tests that HPF programmers and implementors can use to measure the efficiency of the loops, reduc..

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