csTuner: Scalable Auto-tuning Framework for Complex Stencil Computation on GPUs
Qingxiao Sun, Yi Liu, Hailong Yang, Zhonghui Jiang, Xiaoyan Liu, Ming Dun, Zhongzhi Luan, Depei Qian · 2021
The computational patterns of stencil operations are commonly used in HPC applications. Many HPC platforms utilize the computation capability of GPUs to accelerate stencil operations. In recent years, stencils have become more complex in terms of stencil order, memory accesses, and operator patterns. To adapt complex stencils to GPUs, various optimization techniques have been proposed such as blocking and unrolling. However, due to the complexity of GPU architecture, no single parameter setting of the optimization techniques fits all stencils. To address this problem, we propose csTuner, a scalable auto-tuning framework that quickly determines the optimal parameter setting for a given combination of optimization techniques. Specifically, csTuner leverages a set of statistics and machine learning methods to generate parameter groups and sampled parameter settings from the search space. In addition, csTuner adopts the genetic algorithm with approximation to reduce the cost of evolutionary search. The experimental results show that csTuner can find better performing settings with higher auto-tuning speed compared to the state-of-the-art works.