OptiML: An End-to-End Framework for Program Synthesis and CUDA Kernel Optimization
arXiv:2602.12305v1 Announce Type: cross Abstract: Generating high-performance CUDA kernels remains challenging due to the need to navigate a combinatorial space of low-level transformations under noisy and expensive hardware feedback. Although large language models can synthesize functionally correct CUDA code, achieving competitive performance requires systematic exploration and verification of optimization choices. We present OptiML, an end-to-end framework that maps either natural-language intent or input CUDA code to performance-optimized CUDA kernels by formulating kernel optimization as search under verification. OptiML consists of two decoupled stages. When the input is natural language, a Mixture-of-Thoughts generator (OptiML-G) acts as a proposal policy over kernel implementation strategies, producing an initial executable program. A search-based optimizer (OptiML-X) then refines either synthesized or user-provided kernels using Monte Carlo Tree Search over LLM-driven edits,
arXiv:2602.12305v1 Announce Type: cross Abstract: Generating high-performance CUDA kernels remains challenging due to the need to navigate a combinatorial space of low-level transformations under noisy and expensive hardware feedback. Although large language models can synthesize functionally correct CUDA code, achieving competitive performance requires systematic exploration and verification of optimization choices. We present OptiML, an end-to-end framework that maps either natural-language intent or input CUDA code to performance-optimized CUDA kernels by formulating kernel optimization as search under verification. OptiML consists of two decoupled stages. When the input is natural language, a Mixture-of-Thoughts generator (OptiML-G) acts as a proposal policy over kernel implementation strategies, producing an initial executable program. A search-based optimizer (OptiML-X) then refines either synthesized or user-provided kernels using Monte Carlo Tree Search over LLM-driven edits, guided by a hardware-aware reward derived from profiler feedback. Each candidate transformation is compiled, verified, and profiled with Nsight Compute, and evaluated by a composite objective that combines runtime with hardware bottleneck proxies and guardrails against regressions. We evaluate OptiML in both synthesis-and-optimize and optimization-only settings on a diverse suite of CUDA kernels. Results show that OptiML consistently discovers verified performance improvements over strong LLM baselines and produces interpretable optimization trajectories grounded in profiler evidence.
Executive Summary
The article introduces OptiML, an end-to-end framework designed to optimize CUDA kernel performance by leveraging large language models (LLMs) and search-based optimization techniques. OptiML consists of two stages: a Mixture-of-Thoughts generator (OptiML-G) that synthesizes initial CUDA code from natural language or existing code, and a search-based optimizer (OptiML-X) that refines the code using Monte Carlo Tree Search guided by hardware feedback. The framework evaluates candidate transformations through compilation, verification, and profiling, aiming to balance runtime performance with hardware bottlenecks. The study demonstrates that OptiML consistently improves performance over LLM baselines, offering interpretable optimization trajectories supported by profiler evidence.
Key Points
- ▸ OptiML framework combines LLM-based code synthesis with search-based optimization for CUDA kernels.
- ▸ Two-stage process: initial code generation followed by refinement using Monte Carlo Tree Search.
- ▸ Hardware-aware reward system guides optimization based on profiler feedback.
- ▸ Evaluated in both synthesis-and-optimize and optimization-only settings, showing consistent performance improvements.
- ▸ Optimization trajectories are interpretable and grounded in profiler evidence.
Merits
Innovative Framework
OptiML presents a novel approach to CUDA kernel optimization by integrating LLM-based synthesis with search-based refinement, addressing the complexity of low-level transformations.
Performance Improvements
The framework demonstrates significant performance improvements over strong LLM baselines, highlighting its effectiveness in optimizing CUDA kernels.
Interpretable Optimization
The use of profiler evidence to guide optimization makes the process transparent and interpretable, which is crucial for trust and adoption in real-world applications.
Demerits
Computational Overhead
The search-based optimization process, while effective, may introduce computational overhead due to the need for repeated compilation, verification, and profiling.
Dependency on Hardware Feedback
The framework's reliance on hardware feedback for optimization may limit its applicability in environments where such feedback is not readily available or is noisy.
Generalization to Other Domains
The study focuses on CUDA kernels, and the framework's effectiveness in other domains or programming languages remains unexplored.
Expert Commentary
OptiML represents a significant advancement in the field of automated code optimization, particularly for CUDA kernels. By combining the strengths of large language models with search-based optimization techniques, the framework addresses the complex challenge of navigating the combinatorial space of low-level transformations. The use of a hardware-aware reward system ensures that the optimizations are not only performance-driven but also grounded in real-world hardware constraints. The study's demonstration of consistent performance improvements over strong baselines underscores the potential of OptiML to revolutionize the way developers approach CUDA kernel optimization. However, the computational overhead associated with the search-based optimization process and the framework's dependency on hardware feedback are notable limitations that need to be addressed. Future research should explore the generalization of OptiML to other domains and programming languages, as well as strategies to mitigate the computational overhead. Overall, OptiML sets a new benchmark for automated code optimization and paves the way for more efficient and effective software development practices.
Recommendations
- ✓ Further research should investigate the scalability of OptiML to larger and more complex CUDA kernels, as well as its applicability to other high-performance computing environments.
- ✓ Developers should explore hybrid approaches that combine OptiML's automated optimizations with manual tuning to achieve the best possible performance outcomes.
- ✓ Policy makers and industry leaders should consider investing in the development and adoption of AI-driven tools like OptiML to enhance software development practices and stay competitive in the rapidly evolving tech landscape.