The growing demand for high-performance tensor programs on GPUs, especially for large language models (LLMs), necessitates advanced compilation and optimization techniques. However, the critical task of analyzing optimized, low-level PTX code for performance tuning or understanding poses significant challenges. While LLMs hold promise for PTX-to-CUDA decompilation to improve code intelligibility, their effectiveness is severely limited by the scarcity of aligned training data and the inherent complexity of highly optimized, unrolled PTX code.

In this work, we explore methodologies to significantly enhance LLM capabilities for accurate and readable PTX-to-CUDA decompilation and present PtxDec, a decompilation prototype implementing our approach. To overcome the critical barrier of data scarcity, we develop a compiler-based data augmentation framework coupled with rigorous post-processing, enabling the creation of a large-scale, high-quality dataset of 400K aligned CUDA-PTX kernel pairs for effective LLM training. Furthermore, to empower LLMs to handle the complexity of optimized PTX, we introduce Rolled-PTX—an intermediate representation generated through heuristic loop rerolling during preprocessing. Rolled-PTX condenses unrolled patterns, drastically simplifying the input structure presented to the LLM and aligning it better with higher-level loop constructs.

Comprehensive evaluation demonstrates that PtxDec achieves substantial performance gains: our approach yields a 2.3×–3.1× improvement in functional accuracy over baseline methods, alongside significant enhancements in generated code readability and scheduling consistency with the original optimized kernels. Ablation studies further validate the contribution of each proposed component to the overall performance.

To the best of our knowledge, this is the first work tackling PTX-to-CUDA decompilation, specifically focusing on and demonstrating effective strategies for augmenting LLMs to overcome the key challenges in this domain.