TY - GEN
T1 - GPU Based Quarter Spectral Correlation Density Function
AU - Marshall, Scott
AU - Vanhoy, Garrett
AU - Akoglu, Ali
AU - Bose, Tamal
AU - Ryu, Bo
N1 - Funding Information:
ACKNOWLEDGMENT Research reported in this publication was supported in part by Office of the Naval Research under the contract N00014-15-C-5173. The content is solely the responsibil-ityof theauthorsand doesnotnecessarily representthe official views of the Office of Naval Research.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/31
Y1 - 2018/12/31
N2 - In this study we investigate the parallelization of a key feature extraction method called spectral correlation density (SCD) function, which is used in signal classification systems particularly under low signal-to-noise ratio conditions for classifying numerous signals. In order to reduce the computation complexity of the SCD function, we introduce a method called Quarter SCD (QSCD) that allows extracting features of a given signal by processing only quarter of the input signal data. We then parallelize the QSCD by targeting general purpose graphics processing unit (GPU) through architecture specific optimization strategies. We present experimental evaluations on identifying the parallelization configuration for maximizing the efficiency of the program architecture in utilizing the threading power of the GPU architecture. We show that algorithmic and architecture specific optimization strategies result with improving the throughput of the state of the art GPU based Full SCD from 120 signals/second to 2719 signals/second.
AB - In this study we investigate the parallelization of a key feature extraction method called spectral correlation density (SCD) function, which is used in signal classification systems particularly under low signal-to-noise ratio conditions for classifying numerous signals. In order to reduce the computation complexity of the SCD function, we introduce a method called Quarter SCD (QSCD) that allows extracting features of a given signal by processing only quarter of the input signal data. We then parallelize the QSCD by targeting general purpose graphics processing unit (GPU) through architecture specific optimization strategies. We present experimental evaluations on identifying the parallelization configuration for maximizing the efficiency of the program architecture in utilizing the threading power of the GPU architecture. We show that algorithmic and architecture specific optimization strategies result with improving the throughput of the state of the art GPU based Full SCD from 120 signals/second to 2719 signals/second.
KW - GPGPU
KW - signal classification
KW - spectral correlation density
UR - http://www.scopus.com/inward/record.url?scp=85061378419&partnerID=8YFLogxK
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U2 - 10.1109/DASIP.2018.8596977
DO - 10.1109/DASIP.2018.8596977
M3 - Conference contribution
AN - SCOPUS:85061378419
T3 - Conference on Design and Architectures for Signal and Image Processing, DASIP
SP - 88
EP - 93
BT - 2018 Conference on Design and Architectures for Signal and Image Processing, DASIP 2018
PB - IEEE Computer Society
T2 - 12th Conference on Design and Architectures for Signal and Image Processing, DASIP 2018
Y2 - 10 October 2018 through 12 October 2018
ER -