🤖 AI Summary
To address the bottleneck in GPGPU performance modeling—its heavy reliance on manual feature engineering and hardware simulation—this paper proposes, for the first time, an end-to-end LLM-based approach for predicting OpenCL program execution time. The method takes raw OpenCL source code as input and integrates program-structure-aware representation with a regression-based output design, eliminating the need for handcrafted features or low-level hardware simulation. The model is fine-tuned on a large-scale OpenCL source code dataset, achieving a mean absolute percentage error (MAPE) of 24.25% on a self-constructed validation set and 46.1% on a public OpenCL benchmark suite. Its core contribution lies in pioneering the application of large language models to GPGPU performance modeling, enabling fully automated, feature-free prediction from source code to runtime. This establishes a novel paradigm for compiler optimization and heterogeneous programming.
📝 Abstract
Performance modeling, a pivotal domain in program cost analysis, currently relies on manually crafted models constrained by various program and hardware limitations, especially in the intricate landscape of GPGPU. Meanwhile, Large Language Models (LLMs) have demonstrated their effectiveness in addressing diverse programming challenges. Our work establishes a connection between LLMs and performance modeling, employing the LLM as a performance estimator. Through experimental exploration with carefully designed large-scale OpenCL datasets, we highlight the potential capability as well as the main difficulties of using LLMs in handling performance modeling tasks for OpenCL device source programs. As the first study for this line of work, our LLM-based performance model achieves a mean absolute percentage error of 24.25% for a large-scale generated validation set. On a set of publicly available OpenCL programs, our model achieves a mean absolute percentage error of 46.1%.