TY - GEN
T1 - Towards predicting GPGPU performance for concurrent workloads
AU - Kim, Sunggon
AU - Kim, Dongwhan
AU - Eom, Hyeonsang
AU - Son, Yongseok
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - General-Purpose Graphics Processing Units (GPGPUs) have been widely adapted to the industry due to the high parallelism of Graphics Processing Units (GPUs) compared with Central Processing Units (CPUs). To handle the ever-increasing demand, multiple applications often run concurrently in the GPGPU device. However, the GPGPU device can be under-utilized when various types of GPGPU applications are running concurrently. In this paper, we analyze various types of scientific applications and identify factors that impact the performance during the concurrent execution of the applications in the GPGPU device. Our analysis results show that each application has a distinct characteristic and a certain combination of applications has better performance compared with the others when executed concurrently. Based on the finding of our analysis, we propose a simulator which predicts the performance of GPGPU. Our simulator collects performance metrics during the execution of applications and predicts the performance benefits. The experimental result shows that the best combination of applications can increase the performance by 39.44% and 65.98% compared with the average of combinations and the worst case, respectively.
AB - General-Purpose Graphics Processing Units (GPGPUs) have been widely adapted to the industry due to the high parallelism of Graphics Processing Units (GPUs) compared with Central Processing Units (CPUs). To handle the ever-increasing demand, multiple applications often run concurrently in the GPGPU device. However, the GPGPU device can be under-utilized when various types of GPGPU applications are running concurrently. In this paper, we analyze various types of scientific applications and identify factors that impact the performance during the concurrent execution of the applications in the GPGPU device. Our analysis results show that each application has a distinct characteristic and a certain combination of applications has better performance compared with the others when executed concurrently. Based on the finding of our analysis, we propose a simulator which predicts the performance of GPGPU. Our simulator collects performance metrics during the execution of applications and predicts the performance benefits. The experimental result shows that the best combination of applications can increase the performance by 39.44% and 65.98% compared with the average of combinations and the worst case, respectively.
KW - Distributed Computing
KW - GPGPU
KW - HPC system
KW - Performance Prediction
UR - http://www.scopus.com/inward/record.url?scp=85071513724&partnerID=8YFLogxK
U2 - 10.1109/FAS-W.2019.00048
DO - 10.1109/FAS-W.2019.00048
M3 - Conference contribution
AN - SCOPUS:85071513724
T3 - Proceedings - 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems, FAS*W 2019
SP - 164
EP - 169
BT - Proceedings - 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems, FAS*W 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W 2019
Y2 - 16 June 2019 through 20 June 2019
ER -