STile: Searching Hybrid Sparse Formats for Sparse Deep Learning Operators Automatically
Summary: STile enlarges the sparse-format search space with flexible tensor transforms and multi-level decomposition; formalizes the NP-hard multi-level sparse-format decomposition problem. Greedy, cost-driven search yields 2.1–18.0x speedups for SpMM (cuSPARSE) and 1.5–6.9x for SDDMM (DGL), with sub-hour search time amortized. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Jingzhi Fang (Hong Kong University of Science and Technology)
- 2. Yanyan Shen (Shanghai Jiao Tong University)
- 3. Yue Wang (Shenzhen University)
- 4. Lei Chen (Hong Kong University of Science and Technology)
BibTeX Citation
@inproceedings{fang_sigmod24,
title = {{STile: Searching Hybrid Sparse Formats for Sparse Deep Learning Operators Automatically}},
author = {Fang, Jingzhi and Shen, Yanyan and Wang, Yue and Chen, Lei},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3639323},
url = {https://dl.acm.org/doi/10.1145/3639323},
year = {2024}
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| Rank | Citing Paper | Year | Venue | Pagerank |
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| 6,630 | Efficient Training of Graph Neural Networks on Large Graphs | 2024 | VLDB | 5.8192133e-05 |
| 6,900 | Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving | 2025 | SIGMOD | 5.7430032e-05 |
| 10,521 | Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling | 2026 | VLDB | 5.093636e-05 |
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