Ryo Mikasa

Daichi Mukunoki, Ryo Mikasa, Shunichiro Hayashi et al.

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.

Ryo Mikasa, Shun-ichiro Hayashi, Daichi Mukunoki et al.

Large language models (LLMs) have demonstrated strong code generation capabilities, yet the runtime performance of generated code is not guaranteed, and there have been few attempts to train LLMs using runtime performance as a reward in the HPC domain. We propose an online reinforcement learning approach that executes LLM-generated code on a supercomputer and directly feeds back the measured runtime performance (GFLOPS) as a reward. We further introduce a Staged Quality-Diversity (SQD) algorithm that progressively varies the permitted optimization techniques on a per-problem basis, enabling the model to learn code optimization from diverse perspectives. We build a distributed system connecting a GPU training cluster with a CPU benchmarking cluster, and train Qwen2.5 Coder 14B on a double-precision matrix multiplication task using Group Relative Policy Optimization (GRPO). Through two experiments, we show that reinforcement learning combining runtime performance feedback with staged optimization can improve the HPC code generation capability of LLMs.