ESC: Effective Submanifold Convolution using Tensor Cores

Submanifold convolution is an effective method to process 3D point cloud data, playing a significant role in fields such as robotics, autonomous driving, and AR/VR. However, due to the high sparsity and irregularity of point cloud data, it is challenging to accelerate submanifold convolution on modern GPUs, especially using tensor cores. Previous works have proposed implicit GEMM methods to accelerate submanifold convolution on GPU. However, the performance of such methods is limited by massive redundant computation and suboptimal parameter configurations. In this paper, we propose ESC, a new method to leverage GPU tensor cores for accelerating submanifold convolution with improved performance. Firstly, we propose an online similarity-aware reordering method to increase the point cloud data locality and yield more opportunities for eliminating redundancy. Secondly, we propose TC-aware redundancy elimination to reduce the redundant computation at the fine TC-tile granularity. Moreover, we propose an adaptive configuration selector to select the optimal configuration based on offline profiling results and online input data. Experimental results demonstrate that ESC outperforms the state-of-the-art works on representative datasets.