Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomy

Recent Large Language Models (LLMs) can produce and optimize complex code. We investigate the use of LLMs to generate and optimize code for large-scale sciences, focusing on radio astronomy and sustainability. The LOFAR telescope is currently being upgraded, significantly increasing the sky area observed, while simultaneously processing more data faster. However, this is expected to increase the computational requirements 40-fold. This upgrade thus critically depends on rigorous performance optimization of existing software and widespread adoption of accelerators. The code base is very large, making this a daunting task. We therefore investigate and demonstrate an AI-driven approach meant to assist developers in evaluating and optimizing their code, including porting to hardware accelerators. The LOFAR community is committed to sustainable solutions, and needs to achieve these improvements without increasing the energy budget. We thus need to optimize existing codes or port them to accelerators, while making sure that the optimization process itself is also energy efficient. This poses a challenge, since LLMs are energy-intensive. We therefore propose to use Small Language Models (SLMs) instead to limit environmental impact. In this paper, we show how to enhance SLMs through the use of agentic AI. We extend the SLMs in two ways to improve code generation quality and performance: first with a multi-sampling generation strategy and second with incorporating compiler feedback. We demonstrate that multi-sampling SLMs can match or surpass larger single-generation models with fewer computational resources and that feeding compiler output back into the SLMs leads to consistent improvements across all tested models. Our approach is generic, and can also use Retrieval Augmented Generation (RAG) as well as static and dynamic analysis tools in the code generation pipeline.

Beyond the Need for Speed: Energy-Aware Code Generation via Simulation-Guided Reinforcement Learning

Code models strictly prioritize functional correctness, leaving software energy efficiency as an unoptimized byproduct. Training models to generate energy-efficient code requires reproducible feedback at scale, which physical hardware measurement cannot reliably provide due to variance. In this paper, we replace hardware profiling with a deterministic architectural simulation harness to build Green Tea, a corpus of $3.5$ million evaluations across $1{,}474$ C++ problems. We train an energy-aware code model via supervised fine-tuning on energy-contrastive pairs, followed by closed-loop reinforcement learning (GRPO) using simulation-in-the-loop feedback. To rigorously evaluate deployment readiness, we introduce the Correctness-Adjusted Reduction in Energy Total (CARET), a metric that explicitly penalizes code that sacrifices functionality for efficiency. On $143$ held-out problems, our simulation-in-the-loop pipeline achieves $12.63\%$ CARET, nearly tripling the gain of fine-tuning alone, and successfully beats the energy efficiency of human-expert references on $58.4\%$ of its valid outputs. Furthermore, our analysis exposes the IPC trap: standard throughput proxies like Instructions-Per-Cycle (IPC) actively misrank true energy efficiency on $67.8\%$ of problems, proving the absolute necessity of direct energy simulation. By releasing our dataset and infrastructure, we bypass the $263{,}000$ CPU-hours required for reproduction, structurally empowering the community to deploy inherently energy-efficient code generation models.