RODIS: Accelerating Sparse Matrix Multiplication on GPU via Row-Orchestration and Dynamic Instruction Scheduling
Following the Scaling Law, Deep Neural Networks face immense parameter scales and costs, making high-compression unstructured pruning attractive. Additionally, rising edge-side deployment demands have revived interest in activation sparsity. Consequently, unstructured sparse-dense and sparse-sparse matrix multiplications are becoming prevalent in LLM training and inference. However, as the most common general-purpose AI accelerators, GPUs do not provide efficient support for unstructured sparse matrices. Although existing works have proposed GPU architectural enhancements for unstructured sparsity, they are often limited to optimizing Tensor Core computation patterns through dataflows such as outer-product and row-by-row. These methods fail to adequately resolve the poor load balancing and low Multiply-Accumulate utilization in unstructured sparse workloads, especially lacking targeted optimizations for the latest GPU architectural features. To address these issues, we propose RODIS, a two-level acceleration scheme. It utilizes row-orchestration at the data loading level to achieve inter-block load balancing and employs dynamic instruction scheduling at the underlying computation level to enhance MAC utilization. Experimental results show that with minimal area and power overhead, RODIS achieves an average 1.35 × performance improvement compared to other state-of-the-art sparse Tensor Core schemes.