CuTeGen: An LLM-Based Agentic Framework for Generation and Optimization of High-Performance GPU Kernels using CuTe
arXiv:2604.01489v1 Announce Type: new Abstract: High-performance GPU kernels are critical to modern machine learning systems, yet developing efficient implementations remains a challenging, expert-driven process due to the tight coupling between algorithmic structure, memory hierarchy usage, and hardware-specific optimizations. Recent work has explored using large language models (LLMs) to generate GPU kernels automatically, but generated implementations often struggle to maintain correctness and achieve competitive performance across iterative refinements. We present CuTeGen, an agentic framework for automated generation and optimization of GPU kernels that treats kernel development as a structured generate--test--refine workflow. Unlike approaches that rely on one-shot generation or large-scale search over candidate implementations, CuTeGen focuses on progressive refinement of a single evolving kernel through execution-based validation, structured debugging, and staged optimization.
arXiv:2604.01489v1 Announce Type: new Abstract: High-performance GPU kernels are critical to modern machine learning systems, yet developing efficient implementations remains a challenging, expert-driven process due to the tight coupling between algorithmic structure, memory hierarchy usage, and hardware-specific optimizations. Recent work has explored using large language models (LLMs) to generate GPU kernels automatically, but generated implementations often struggle to maintain correctness and achieve competitive performance across iterative refinements. We present CuTeGen, an agentic framework for automated generation and optimization of GPU kernels that treats kernel development as a structured generate--test--refine workflow. Unlike approaches that rely on one-shot generation or large-scale search over candidate implementations, CuTeGen focuses on progressive refinement of a single evolving kernel through execution-based validation, structured debugging, and staged optimization. A key design choice is to generate kernels using the CuTe abstraction layer, which exposes performance-critical structures such as tiling and data movement while providing a more stable representation for iterative modification. To guide performance improvement, CuTeGen incorporates workload-aware optimization prompts and delayed integration of profiling feedback. Experimental results on matrix multiplication and activation workloads demonstrate that the framework produces functionally correct kernels and achieves competitive performance relative to optimized library implementations.
Executive Summary
CuTeGen introduces an agentic framework leveraging large language models (LLMs) to automate the generation and optimization of high-performance GPU kernels using the CuTe abstraction layer. Unlike prior one-shot or exhaustive search-based approaches, CuTeGen employs a structured generate--test--refine workflow, iteratively validating and debugging kernels through execution-based feedback. By focusing on progressive refinement and workload-aware prompts, the framework achieves functional correctness and competitive performance on matrix multiplication and activation workloads, rivaling optimized library implementations. This work bridges the gap between automated code generation and hardware-aware optimization, offering a scalable solution for GPU kernel development in machine learning systems.
Key Points
- ▸ CuTeGen treats GPU kernel development as an iterative generate--test--refine process, departing from one-shot generation or brute-force search paradigms.
- ▸ The framework utilizes the CuTe abstraction layer to expose performance-critical structures (e.g., tiling, data movement) while providing a stable representation for iterative modifications.
- ▸ Experimental validation on matrix multiplication and activation workloads demonstrates functional correctness and performance parity with optimized library implementations, highlighting the framework's efficacy in real-world scenarios.
Merits
Innovative Workflow Design
CuTeGen's iterative refinement approach, combined with execution-based validation, ensures robustness and adaptability, addressing the brittleness of one-shot LLM generation by systematically integrating feedback.
Hardware-Aware Abstraction
The use of CuTe as an intermediary abstraction layer enables the framework to decouple algorithmic logic from hardware-specific optimizations, facilitating targeted and efficient performance tuning.
Performance Competitiveness
Empirical results demonstrate that CuTeGen produces kernels with performance comparable to hand-optimized libraries, validating the framework's practical utility in high-stakes applications like machine learning.
Demerits
Limited Generalizability
The framework's reliance on CuTe and specific workloads (e.g., matrix multiplication, activation) may restrict its applicability to other GPU kernel types or hardware architectures not covered by the abstraction layer.
Dependency on Profiling Feedback
Delayed integration of profiling data introduces latency into the refinement cycle, potentially limiting the framework's efficiency in real-time or latency-sensitive environments.
LLM-Dependent Variability
The quality of generated kernels is contingent on the underlying LLM's capabilities, which may introduce variability or suboptimal solutions in edge cases not well-represented in training data.
Expert Commentary
CuTeGen represents a significant advancement in the intersection of AI-driven automation and high-performance computing. By framing kernel development as an iterative, feedback-driven process, the authors address a longstanding challenge in GPU programming: the tension between expressiveness and performance. The use of CuTe as an abstraction layer is particularly insightful, as it provides a stable yet flexible interface for iterative refinement, a critical feature for frameworks relying on LLMs. However, the framework's dependence on the underlying LLM's capabilities introduces a layer of unpredictability, which may necessitate hybrid approaches combining LLM-generated code with traditional compiler optimizations. Additionally, the delayed integration of profiling feedback, while likely necessary for stability, could be a bottleneck in real-time applications. Future work could explore adaptive refinement strategies that dynamically adjust the balance between exploration and exploitation based on intermediate performance metrics. This work underscores the potential of AI to augment, rather than replace, the expertise of performance engineers, heralding a new era of collaboration between human insight and machine automation in scientific computing.
Recommendations
- ✓ Explore hybrid frameworks that integrate CuTeGen's iterative refinement with traditional compiler optimizations to mitigate LLM variability and enhance robustness.
- ✓ Expand the scope of experimental validation to include a broader range of GPU kernels and hardware architectures, ensuring the framework's generalizability and scalability.
- ✓ Develop standardized benchmarks and evaluation metrics for AI-driven kernel generation tools to facilitate fair comparisons and foster industry adoption.
- ✓ Investigate techniques for real-time profiling integration to reduce latency in the refinement cycle, particularly for latency-sensitive applications.
- ✓ Establish ethical guidelines and technical standards for the use of LLMs in generating high-performance computing kernels, addressing concerns about correctness, reproducibility, and potential biases.
Sources
Original: arXiv - cs.LG
