TY - GEN
T1 - Evaluating Unified Memory Performance in HIP
AU - Jin, Zheming
AU - Vetter, Jeffrey S.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Heterogeneous unified memory management between a CPU and a GPU is a major challenge in GPU computing. Recently, unified memory (UM) has been supported by software and hardware components on AMD computing platforms. The support could simplify the complexities of memory management. In this paper, we attempt to have a better understanding of UM by evaluating the performance of UM programs on an AMD MI100 GPU. More specifically, we evaluate data migration using UM against other data transfer techniques for the overall performance of an application, assess the impacts of three commonly used optimization techniques on the kernel execution time of a vector add sample, and compare the performance and productivity of selected benchmarks with and without UM. The performance overhead associated with UM is not trivial, but it can improve programming productivity by reducing lines of code for scientific applications. We aim to present early results and feedback on the UM performance to the vendor.
AB - Heterogeneous unified memory management between a CPU and a GPU is a major challenge in GPU computing. Recently, unified memory (UM) has been supported by software and hardware components on AMD computing platforms. The support could simplify the complexities of memory management. In this paper, we attempt to have a better understanding of UM by evaluating the performance of UM programs on an AMD MI100 GPU. More specifically, we evaluate data migration using UM against other data transfer techniques for the overall performance of an application, assess the impacts of three commonly used optimization techniques on the kernel execution time of a vector add sample, and compare the performance and productivity of selected benchmarks with and without UM. The performance overhead associated with UM is not trivial, but it can improve programming productivity by reducing lines of code for scientific applications. We aim to present early results and feedback on the UM performance to the vendor.
KW - GPU
KW - Performance evaluation
KW - Unified memory
UR - http://www.scopus.com/inward/record.url?scp=85136175044&partnerID=8YFLogxK
U2 - 10.1109/IPDPSW55747.2022.00096
DO - 10.1109/IPDPSW55747.2022.00096
M3 - Conference contribution
AN - SCOPUS:85136175044
T3 - Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2022
SP - 562
EP - 568
BT - Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 36th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2022
Y2 - 30 May 2022 through 3 June 2022
ER -