SAGE: Semantic-Aware GPU Kernel Embedding for Efficient Performance Simulation

Graphics processing units (GPUs) underpin high-performance computing, artificial intelligence, and scientific simulation, yet rising architectural complexity complicates timely and accurate performance evaluation. Cycle-accurate simulation is precise but prohibitively slow for design exploration; sampled-based approaches improve efficiency, but BBV-based characterizations capture only execution frequencies and insufficiently reflect instruction semantics and control-flow dynamics, especially in multi-kernel workloads.To address these limitations, this paper introduces SAGE, a semantic-aware GPU kernel representation method leveraging pretrained language models. SAGE semantically encodes warp-level instruction sequences with localized context and aggregates them hierarchically to produce kernel-level embeddings. Compared with conventional BBV-based approaches, these embeddings better capture program semantics, improving prediction accuracy and generalization on complex applications. Experimental results show that SAGE achieves approximation accuracy comparable to Photon—with relative errors of 19.70% and 19.88%, respectively, while delivering an average simulation speedup of 3.31×, representing an improvement over Photon’s 2.69×.