Optimas: An Intelligent Analytics-Informed Generative AI Framework for Performance Optimization
For developers and HPC practitioners, Optimas automates the time-intensive manual performance optimization process, making it accessible to non-experts.
Optimas is a generative AI framework that automates code optimization by mapping performance diagnostics to code transformations, achieving 100% correct code and performance gains of 8.02%-79.09% across 3,410 experiments on NVIDIA GPUs.
Large language models (LLMs) show promise for automated code optimization. However, without performance context, they struggle to produce correct and effective code transformations. Existing performance tools can identify bottlenecks but stop short of generating actionable code changes. Consequently, performance optimization continues to be a time-intensive and manual endeavor, typically undertaken only by experts with detailed architectural understanding. To bridge this gap, we introduce Optimas, a modular, fully automated, end-to-end generative AI framework built on a multi-agent workflow. Optimas uses LLMs to map performance diagnostics from multiple reports to established, literature-backed code transformations, while unifying insight extraction, code generation, execution, and validation within a single pipeline. Across 3,410 real-world experiments on 10 benchmarks and two HPC mini-applications, Optimas generates 100% correct code and improves performance in over 98.82% of those experiments, achieving average gains of 8.02%-79.09% on NVIDIA GPUs.