X-TED: Massive Parallelization of Tree Edit Distance
Summary: X-TED: GPU framework that massive-parallelizes tree edit distance by preprocessing to expose dependencies among millions of DP tables and by dynamic, stage-adaptive scheduling to optimize GPU core and memory use. Outperforms AP‑TED by up to 42× and MC‑TED by ~31×. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Dayi Fan
- 2. Rubao Lee
- 3. Xiaodong Zhang
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,271 | The Yin and Yang of Processing Data Warehousing Queries on GPU Devices | 2013 | VLDB | 0.00012900735 |
| 2,753 | Mega-KV: A Case for GPUs to Maximize the Throughput of In-Memory Key-Value Stores | 2015 | VLDB | 8.1722714e-05 |
| 2,786 | Approximate XML Joins | 2002 | SIGMOD | 8.1223413e-05 |
| 3,206 | Similarity Evaluation on Tree-structured Data | 2005 | SIGMOD | 7.3855924e-05 |
| 3,302 | RTED: A Robust Algorithm for the Tree Edit Distance | 2012 | VLDB | 7.2445358e-05 |
| 5,129 | The Art of Balance: A RateupDBTM Experience of Building a CPU/GPU Hybrid Database Product | 2021 | VLDB | 5.6724875e-05 |
| 7,215 | SyncSignature: A Simple, Efficient, Parallelizable Framework for Tree Similarity Joins | 2023 | VLDB | 4.7939964e-05 |
| 8,352 | Accelerating Pathology Image Data Cross-Comparison on CPU-GPU Hybrid Systems | 2012 | VLDB | 4.5333481e-05 |
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