TY - GEN
T1 - Parallelizing edge drawing algorithm on CUDA
AU - Ozsen, Ozgur
AU - Topal, Cihan
AU - Akinlar, Cuneyt
PY - 2012
Y1 - 2012
N2 - Parallel computing methods are very useful in speeding up algorithms that can be divided into independent subtasks. Traditional multi-processor architectures have limited use due to their high cost and difficulties of their use. Recently, Graphics Processor Units (GPUs) has opened up a new era for general purpose parallel computation. Among many GPU programming frameworks, Compute Unified Device Architecture (CUDA) seems to be the most widely used GPU architecture due to its low cost and ease of use. In this paper, we show how to implement our recently proposed novel edge segment detector, the Edge Drawing (ED) algorithm, in CUDA, and present performance studies demonstrating the performance gams in the CUDA architecture compared to a uniprocessor CPU implementation. The results show that a CUDA implementation improves the running time of ED by up to 12 and ED runs at an amazing blazing speed of about 1 ms on a 512512 image. ED is run on different CUDA cards and the performance results are presented.
AB - Parallel computing methods are very useful in speeding up algorithms that can be divided into independent subtasks. Traditional multi-processor architectures have limited use due to their high cost and difficulties of their use. Recently, Graphics Processor Units (GPUs) has opened up a new era for general purpose parallel computation. Among many GPU programming frameworks, Compute Unified Device Architecture (CUDA) seems to be the most widely used GPU architecture due to its low cost and ease of use. In this paper, we show how to implement our recently proposed novel edge segment detector, the Edge Drawing (ED) algorithm, in CUDA, and present performance studies demonstrating the performance gams in the CUDA architecture compared to a uniprocessor CPU implementation. The results show that a CUDA implementation improves the running time of ED by up to 12 and ED runs at an amazing blazing speed of about 1 ms on a 512512 image. ED is run on different CUDA cards and the performance results are presented.
KW - CUDA
KW - edge detection
KW - GPU programming
KW - Parallel image processing
KW - real time
UR - http://www.scopus.com/inward/record.url?scp=84858019590&partnerID=8YFLogxK
U2 - 10.1109/ESPA.2012.6152450
DO - 10.1109/ESPA.2012.6152450
M3 - Conference contribution
AN - SCOPUS:84858019590
SN - 9781467308984
T3 - 2012 IEEE International Conference on Emerging Signal Processing Applications, ESPA 2012 - Proceedings
SP - 79
EP - 82
BT - 2012 IEEE International Conference on Emerging Signal Processing Applications, ESPA 2012 - Proceedings
T2 - 2012 IEEE International Conference on Emerging Signal Processing Applications, ESPA 2012
Y2 - 12 January 2011 through 14 January 2011
ER -