Adaptation of algorithms for underwater sonar data processing to GPU-based systems
Department of Computer and Information Science, The Institute of Technology, Linkoping University
Linkoping University, 2013
@phdthesis{sundin2013adaptation,
title={Adaptation of algorithms for underwater sonar data processing to GPU-based systems},
author={Sundin, Patricia},
year={2013},
school={Link{"o}ping}
}
In this master thesis, algorithms for acoustic simulations in underwater environments are ported for GPU processing. The GPU parallel computing platforms used are CUDA, OpenCL and SkePU. The purpose of this master thesis is to adapt and evaluate the ported algorithms’ performance on two modern NVIDIA GPUs, Tesla K20 and Quadro K5000. Several optimizations, described in existing literature for GPU processing (e.g. usage of shared memory, coalesced memory accesses), are implemented and multiple versions of each algorithm are created to study their trade-offs. Evaluation on two GPUs showed that different versions of the same algorithm have different performance characteristic and execution with the best performing version can give better performance than the original algorithm executing on 8 CPUs. A performance comparison between CUDA, OpenCL and SkePU versions of one algorithm is also made.
June 29, 2013 by hgpu
