SuperCode: Sustainability PER AI-driven CO-DEsign

TL;DR

This paper proposes an AI-driven co-design approach using Large Language Models to generate energy-efficient code for emerging hardware, aiming to enhance sustainability in scientific computing.

Contribution

It introduces a novel methodology leveraging AI for co-design of software and hardware to improve energy efficiency in scientific applications.

Findings

Validation planned with radio astronomy applications

Focus on sustainability as key performance indicator

Promotes open science and collaboration

Abstract

Currently, data-intensive scientific applications require vast amounts of compute resources to deliver world-leading science. The climate emergency has made it clear that unlimited use of resources (e.g., energy) for scientific discovery is no longer acceptable. Future computing hardware promises to be much more energy efficient, but without better optimized software this cannot reach its full potential. In this vision paper, we propose a generic AI-driven co-design methodology, using specialized Large Language Models (like ChatGPT), to effectively generate efficient code for emerging computing hardware. We describe how we will validate our methodology with two radio astronomy applications, with sustainability as the key performance indicator. This paper is a modified version of our accepted SuperCode project proposal. We present it here in this form to introduce the vision behind this project and to disseminate the work in the spirit of Open Science and transparency. An additional aim is to collect feedback, invite potential collaboration partners and use-cases to join the project.