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

TL;DR

This paper discusses the development of an end-to-end auto-tuning framework for the HPC PowerStack to optimize power and performance in high-performance computing systems under energy constraints.

Contribution

It introduces a novel auto-tuning framework for the entire PowerStack, highlighting opportunities for co-tuning layers and outlining future research challenges.

Findings

Proposed a comprehensive end-to-end auto-tuning framework

Identified opportunities for co-tuning across PowerStack layers

Outlined research challenges for multi-layer auto-tuning

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.