Scalable Parallel Execution of an Event-based Radio Signal Propagation Model for Cluttered 3D Terrains

Abstract

Radio signal strength estimation is essential in many applications, including the design of military radio communications and industrial wireless installations. While classical approaches such as finite difference methods are well-known, new event-based models of radio signal propagation have been recently shown to deliver such estimates faster (via serial execution) than other methods. For scenarios with large or richly-featured geographical volumes however, parallel processing is required to meet the memory and computation time demands. Here, we present a scalable and efficient parallel execution of a recently-developed event-based radio signal propagation model. We demonstrate its scalability to thousands of processors, with parallel speedups over 1000?. The speed and scale achieved by our parallel execution enable larger scenarios and faster execution than has ever been reported before.

[Pub 104]

http://www.osti.gov/bridge

Kalyan Perumalla
Kalyan Perumalla

As a Federal Program Manager in Advanced Scientific Computing Research at the U.S. Dept. of Energy, Office of Science, Kalyan Perumalla manages a $100-million R&D portfolio covering AI, HPC, Quantum, SciDAC, and Basic Computer Science. In his 25-year R&D leadership experience, he previously led advanced R&D as Distinguished Research Staff Member at the Oak Ridge National Laboratory (ORNL) developing scalable software and applications on the world’s largest supercomputers for 17 years, including as a line manager and a founding group leader. He has held senior faculty and adjunct appointments at UTK, GT, and UNL, and was an IAS Fellow at Durham University.

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