SCHEDULE: NOV 10-16, 2012
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GPU Accelerated Ultrasonic Tomography Using Propagation and Backpropagation Method
SESSION: Research Poster Reception
EVENT TYPE: Posters and Electronic Posters
TIME: 5:15PM - 7:00PM
SESSION CHAIR: Torsten Hoefler
AUTHOR(S):Pedro Bello Maldonado, Yuanwei Jin, Enyue Lu
ROOM:East Entrance
ABSTRACT:
This paper develops implementation strategy and method to accelerate the propagation and backpropagation (PBP) tomographic imaging algorithm using Graphic Processing Units (GPUs). The Compute Unified Device Architecture (CUDA) programming model is used to develop our parallelized algorithm since the CUDA model allows the user to interact with the GPU resources more efficiently than traditional Shader methods. The results show an improvement of more than 80x when compared to the C/C++ version of the algorithm, and 515x when compared to the MATLAB version while achieving high quality imaging for both cases. We test different CUDA kernel configurations in order to measure changes in the processing-time of our application. By examining the acceleration rate and the image quality, we develop an optimal kernel configuration that maximizes the throughput of CUDA implementation for the PBP method.
Chair/Author Details:
Torsten Hoefler (Chair) - ETH Zurich
Pedro Bello Maldonado - Florida International University
Yuanwei Jin - University of Maryland Eastern Shore
Enyue Lu - Salisbury University
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GPU Accelerated Ultrasonic Tomography Using Propagation and Backpropagation Method
SESSION: Research Poster Reception
EVENT TYPE:
TIME: 5:15PM - 7:00PM
SESSION CHAIR: Torsten Hoefler
AUTHOR(S):Pedro Bello Maldonado, Yuanwei Jin, Enyue Lu
ROOM:East Entrance
ABSTRACT:
This paper develops implementation strategy and method to accelerate the propagation and backpropagation (PBP) tomographic imaging algorithm using Graphic Processing Units (GPUs). The Compute Unified Device Architecture (CUDA) programming model is used to develop our parallelized algorithm since the CUDA model allows the user to interact with the GPU resources more efficiently than traditional Shader methods. The results show an improvement of more than 80x when compared to the C/C++ version of the algorithm, and 515x when compared to the MATLAB version while achieving high quality imaging for both cases. We test different CUDA kernel configurations in order to measure changes in the processing-time of our application. By examining the acceleration rate and the image quality, we develop an optimal kernel configuration that maximizes the throughput of CUDA implementation for the PBP method.
Chair/Author Details:
Torsten Hoefler (Chair) - ETH Zurich
Pedro Bello Maldonado - Florida International University
Yuanwei Jin - University of Maryland Eastern Shore
Enyue Lu - Salisbury University
Click here to download .ics calendar file