TY - GEN
T1 - Toward an End-to-End Auto-tuning Framework in HPC PowerStack
AU - Wu, Xingfu
AU - Marathe, Aniruddha
AU - Jana, Siddhartha
AU - Vysocky, Ondrej
AU - John, Jophin
AU - Bartolini, Andrea
AU - Riha, Lubomir
AU - Gerndt, Michael
AU - Taylor, Valerie
AU - Bhalachandra, Sridutt
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9
Y1 - 2020/9
N2 - Efficiently utilizing procured power and optimizing performance of scientific applications under power and energy constraints are challenging. The HPC PowerStack defines a software stack to manage power and energy of high-performance computing systems and standardizes the interfaces between different components of the stack. This survey paper presents the findings of a working group focused on the end-to-end tuning of the PowerStack. First, we provide a background on the PowerStack layer-specific tuning efforts in terms of their high-level objectives, the constraints and optimization goals, layer-specific telemetry, and control parameters, and we list the existing software solutions that address those challenges. Second, we propose the PowerStack end-to-end auto-tuning framework, identify the opportunities in co-tuning different layers in the PowerStack, and present specific use cases and solutions. Third, we discuss the research opportunities and challenges for collective auto-tuning of two or more management layers (or domains) in the PowerStack. This paper takes the first steps in identifying and aggregating the important RD challenges in streamlining the optimization efforts across the layers of the PowerStack.
AB - Efficiently utilizing procured power and optimizing performance of scientific applications under power and energy constraints are challenging. The HPC PowerStack defines a software stack to manage power and energy of high-performance computing systems and standardizes the interfaces between different components of the stack. This survey paper presents the findings of a working group focused on the end-to-end tuning of the PowerStack. First, we provide a background on the PowerStack layer-specific tuning efforts in terms of their high-level objectives, the constraints and optimization goals, layer-specific telemetry, and control parameters, and we list the existing software solutions that address those challenges. Second, we propose the PowerStack end-to-end auto-tuning framework, identify the opportunities in co-tuning different layers in the PowerStack, and present specific use cases and solutions. Third, we discuss the research opportunities and challenges for collective auto-tuning of two or more management layers (or domains) in the PowerStack. This paper takes the first steps in identifying and aggregating the important RD challenges in streamlining the optimization efforts across the layers of the PowerStack.
UR - http://www.scopus.com/inward/record.url?scp=85096225732&partnerID=8YFLogxK
U2 - 10.1109/CLUSTER49012.2020.00068
DO - 10.1109/CLUSTER49012.2020.00068
M3 - Conference contribution
AN - SCOPUS:85096225732
T3 - Proceedings - IEEE International Conference on Cluster Computing, ICCC
SP - 473
EP - 483
BT - Proceedings - 2020 IEEE International Conference on Cluster Computing, CLUSTER 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd IEEE International Conference on Cluster Computing, CLUSTER 2020
Y2 - 14 September 2020 through 17 September 2020
ER -