Parallel implementation of the irregular terrain model (ITM) for radio transmission loss prediction using GPU and cell BE processors

Yang Song, Ali Akoglu

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

The Irregular Terrain Model (ITM), also known as the Longley-Rice model, predicts long-range average transmission loss of a radio signal based on atmospheric and geographic conditions. Due to variable terrain effects and constantly changing atmospheric conditions which can dramatically influence radio wave propagation, there is a pressing need for computational resources capable of running hundreds of thousands of transmission loss calculations per second. Multicore processors, like the NVIDIA Graphics Processing Unit (GPU) and IBM Cell Broadband Engine (BE), offer improved performance over mainstream microprocessors for ITM. We study architectural features of the Tesla C870 GPU and Cell BE and evaluate the effectiveness of architecture-specific optimizations and parallelization strategies for ITM on these platforms. We assess the GPU implementations that utilize both global and shared memories along with fine-grained parallelism. We assess the Cell BE implementations that utilize direct memory access, double buffering, and SIMDization. With these optimization strategies, we achieve less than a second of computation time on each platform which is not feasible with a general purpose processor, and we observe that the GPU delivers better performance than Cell BE in terms of total execution time and performance per watt metrics by a factor of 2.3x and 1.6x, respectively.

Original languageEnglish (US)
Article number5680900
Pages (from-to)1276-1283
Number of pages8
JournalIEEE Transactions on Parallel and Distributed Systems
Volume22
Issue number8
DOIs
StatePublished - 2011

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Radio transmission
Engines
Data storage equipment
Radio waves
Wave propagation
Microprocessor chips
Graphics processing unit

Keywords

  • IBM cell broadband engine
  • Longley-Rice model
  • multicore
  • NVIDIA GPU
  • parallel computing

ASJC Scopus subject areas

  • Hardware and Architecture
  • Signal Processing
  • Computational Theory and Mathematics

Cite this

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