Toward an End-to-End Auto-tuning Framework in HPC PowerStack

Xingfu Wu; Aniruddha Marathe; Siddhartha Jana; Ondrej Vysocky; Jophin John; Andrea Bartolini; Lubomir Riha; Michael Gerndt; Valerie Taylor; Sridutt Bhalachandra

arXiv · 2008.06571

Toward an End-to-End Auto-tuning Framework in HPC PowerStack

Xingfu Wu·Aniruddha Marathe·Siddhartha Jana·Ondrej Vysocky·Jophin John·Andrea Bartolini·Lubomir Riha·Michael Gerndt·Valerie Taylor·Sridutt Bhalachandra

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Abstract

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 R&D challenges in streamlining the optimization efforts across the layers of the PowerStack.

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Xingfu Wu, Aniruddha Marathe, Siddhartha Jana, Ondrej Vysocky, Jophin John, Andrea Bartolini, Lubomir Riha, Michael Gerndt, Valerie Taylor, Sridutt Bhalachandra. 2020-08-14. Toward an End-to-End Auto-tuning Framework in HPC PowerStack. https://arxiv.org/abs/2008.06571

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