Recent research has focused on accelerating stencil computations by exploiting emerging hardware like Tensor Cores. To leverage these accelerators, the stencil operation must be transformed to matrix multiplications. However, this transformation introduces undesired sparsity into the kernel matrix, leading to significant redundant computation.

In this paper, we present SPIDER, the first system to turn this unresolved sparsity into an optimization opportunity by exploring the potential of Sparse Tensor Cores (SpTCs) for stencil acceleration. Specifically, SPIDER introduces an efficient and elegant transformation method that integrates two cooperative techniques: an ahead-of-time strided swapping transformation for kernel matrices and an on-the-fly row-swapping mechanism for inputs. This rule-based approach effectively transforms stencil computation into operations compatible with SpTCs, introducing only slight compile-time overhead and zero runtime overhead. Additionally, SPIDER incorporates multiple optimizations to maximize computational efficiency. Experimental evaluations demonstrate that SPIDER outperforms vendor library cuDNN by 6.23$\times$ and state-of-the-art (SOTA) Tensor Core-based approaches (ConvStencil, FlashFFTStencil, etc.) by 1.98$\times$ on average.