Workshops Archive

Invited Talk 2: Automating Energy-Aware HPC Code Development for Performance and Portability in Heterogeneous Computing

Workshop: ExHetAI: Extreme Heterogeneity and AI Convergence in HPC

Authors: Matthew Dearing (Argonne National Laboratory (ANL))

Abstract: The future of sustainable HPC depends on reducing energy consumption without sacrificing performance, especially with today’s diversification of accelerators. While advancements in hardware, workload management, and system implementation will dramatically influence energy savings, software must also play a key role. Recent advances with large language models (LLMs) show promise for automated code generation. However, most efforts prioritize functionality over energy efficiency, portability, and architecture-specific optimization. We discuss our latest progress in LASSI, an automated LLM-driven refactoring framework that generates translations and energy-efficient code on target parallel systems for given parallel code as input. Through multi-stage iterative refinement incorporating self-prompting, domain-specific context, and self-correcting feedback loops, LASSI demonstrates effectiveness across multiple device architectures as evaluated through functional equivalence metrics and expected energy reductions in generated codes. Such capabilities are paving the way toward AI-driven automation in heterogeneous HPC code development with a focus on performant and portable parallelized scientific software.

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