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)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Jingzhi Fang
- 2. Yanyan Shen
- 3. Yue Wang
- 4. Lei Chen
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,942 | Efficient Training of Graph Neural Networks on Large Graphs | 2024 | VLDB | 4.8922884e-05 |
| 9,677 | Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving | 2025 | SIGMOD | 4.3047774e-05 |
| 10,233 | Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling | 2026 | VLDB | 4.1945683e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next