[Paper] LLM-Based Porting of Optimized C++ to CUDA Through Deoptimization and Reoptimization
Source: arXiv - 2606.06063v1
Overview
When porting high-performance computing (HPC) code from CPU to GPU, CPU-oriented optimizations may obstruct LLM-based CUDA translation. We design and evaluate a Deopt-Reopt workflow that first simplifies the input C++ code and then retranslates and reoptimizes it for CUDA, comparing it against direct translation (Direct) on twelve HPC kernels with two LLMs (gpt-oss-120b (O120) and qwen-3-235b-a22b-instruct-2507 (Q235)) in Single-shot (one pass) and Iterative (repeated refinement) settings. In Single-shot, among 18 testable cases Deopt-Reopt was significantly faster among successful trials (after BH-FDR correction) in five - most clearly for conv2d, where CPU- and GPU-oriented designs diverge - but Direct was faster in three, so removing CPU-specific optimizations is not universally beneficial. An exploratory Direct-3 control that equalizes the LLM-call count left Deopt-Reopt ahead in only four of nineteen testable cases, with Direct-3 ahead in four others. In Iterative, repeated generation and repair narrow the mode gap - markedly so for O120 - while Q235 retains large Deopt-Reopt advantages on conv2d, ddgemm, and bgemm. Deopt-Reopt’s effect on feasibility is also mixed - sharply higher for some kernels Direct rarely compiles, lower for others. Because performance is conditioned on successful trials, the benefit is conditional rather than a guaranteed end-to-end gain. Overall, Deopt-Reopt is an effective but non-universal technique for LLM-based GPU porting, with gains that depend on the kernel, the model, the search budget, and the success rate.
Key Contributions
This paper presents research in the following areas:
- cs.DC
Methodology
Please refer to the full paper for detailed methodology.
Practical Implications
This research contributes to the advancement of cs.DC.
Authors
- Daichi Mukunoki
- Ryo Mikasa
- Shunichiro Hayashi
- Tetsuya Hoshino
- Takahiro Katagiri
Paper Information
- arXiv ID: 2606.06063v1
- Categories: cs.DC
- Published: June 4, 2026
- PDF: Download PDF