SparSP: Exploiting Communication Sparsity for Sequence-Parallel Video DiTs
Diffusion Transformers have become the dominant architecture for video generation. Their substantial computational cost motivates scaling inference across multi-GPU servers, yet efficient scaling remains challenging on commodity GPUs connected via PCIe, whose bandwidth is limited. Although sparse attention substantially reduces computation, its implications for communication remain underexplored. This paper argues that attention sparsity should be treated as a communication primitive. We present SparSP, an efficient sparse sequence parallel communication system that co-designs token distribution, communication routing, and asynchronous execution for sparse video diffusion models. First, D...
| Authors | Desen Sun, Xinrui Zhong, Yuke Wang et al. |
|---|---|
| Primary category | cs.DC |
| Published | 26 September 2026 |
| Last updated at source | 26 September 2026 |
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