TY - GEN
T1 - Comparative Analysis of GPU Stream Processing between Persistent and Non-persistent Kernels
AU - Kim, Suhwan
AU - Jung, Changue
AU - Kim, Younghoon
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Employing GPU to accelerate stream data processing has shown to be a considerable success in recent research. The stream processing engines need to process continuous data. Persistent Thread (PT), where GPU threads remain in a loop throughout executions, rather than non-Persistent Thread (nonPT) kernels can give several advantages. PT provides high-performance low-latency data processing by reducing kernel launch overhead and hiding memory copy overhead to the GPU. In this paper, we comparatively analyze the performances of PT and nonPT in various aspects. We also figure out the cause of advantages and what needs to be considered in order to obtain actual performance gain. The evaluation was done with four distinct application scenarios and the result shows that PT yields at most x4.4 higher performance over nonPT under desirable conditions.
AB - Employing GPU to accelerate stream data processing has shown to be a considerable success in recent research. The stream processing engines need to process continuous data. Persistent Thread (PT), where GPU threads remain in a loop throughout executions, rather than non-Persistent Thread (nonPT) kernels can give several advantages. PT provides high-performance low-latency data processing by reducing kernel launch overhead and hiding memory copy overhead to the GPU. In this paper, we comparatively analyze the performances of PT and nonPT in various aspects. We also figure out the cause of advantages and what needs to be considered in order to obtain actual performance gain. The evaluation was done with four distinct application scenarios and the result shows that PT yields at most x4.4 higher performance over nonPT under desirable conditions.
KW - GPU
KW - Persistent Thread
UR - http://www.scopus.com/inward/record.url?scp=85143254113&partnerID=8YFLogxK
U2 - 10.1109/ICTC55196.2022.9952789
DO - 10.1109/ICTC55196.2022.9952789
M3 - Conference contribution
AN - SCOPUS:85143254113
T3 - International Conference on ICT Convergence
SP - 2330
EP - 2332
BT - ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence
PB - IEEE Computer Society
T2 - 13th International Conference on Information and Communication Technology Convergence, ICTC 2022
Y2 - 19 October 2022 through 21 October 2022
ER -