DimBoost: Boosting Gradient Boosting Decision Tree to Higher Dimensions
Summary: DimBoost is a scalable GBDT trainer for ultra-high dimensional data (330K features), with a performance model revealing collective-communication bottlenecks. Key innovations: scheduler, two-phase split finding, sparsity-aware histograms with parallel indexing, and low-precision gradients; 2–9x speedups over existing systems. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jiawei Jiang (Peking University; Tencent)
- 2. Bin Cui (Peking University)
- 3. Ce Zhang (ETH Zurich)
- 4. Fangcheng Fu (Peking University)
BibTeX Citation
@inproceedings{jiang_sigmod18,
title = {{DimBoost: Boosting Gradient Boosting Decision Tree to Higher Dimensions}},
author = {Jiang, Jiawei and Cui, Bin and Zhang, Ce and Fu, Fangcheng},
series = {{SIGMOD} '18},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3183713.3196892},
url = {https://dl.acm.org/doi/10.1145/3183713.3196892},
year = {2018}
}Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,959 | VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning | 2021 | SIGMOD | 9.4090198e-05 |
| 3,169 | Towards Demystifying Serverless Machine Learning Training | 2021 | SIGMOD | 7.6715222e-05 |
| 3,237 | BlindFL: Vertical Federated Machine Learning without Peeking into Your Data | 2022 | SIGMOD | 7.6089416e-05 |
| 5,345 | PS2: Parameter Server on Spark | 2019 | SIGMOD | 6.2586047e-05 |
| 5,589 | An Experimental Evaluation of Large Scale GBDT Systems | 2019 | VLDB | 6.1559057e-05 |
| 5,785 | BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees | 2019 | SIGMOD | 6.0892672e-05 |
| 5,876 | BAGUA: Scaling up Distributed Learning with System Relaxations | 2022 | VLDB | 6.0557672e-05 |
| 6,550 | Reliable Data Distillation on Graph Convolutional Network | 2020 | SIGMOD | 5.8430465e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 82 | Space-Efficient Online Computation of Quantile Summaries | 2001 | SIGMOD | 0.00036378991 |
| 126 | Schism: a Workload-Driven Approach to Database Replication and Partitioning | 2010 | VLDB | 0.00030779127 |
| 199 | Integrating Vertical and Horizontal Partitioning into Automated Physical Database Design | 2004 | SIGMOD | 0.00025612088 |
| 246 | Automating Physical Database Design in a Parallel Database | 2002 | SIGMOD | 0.00023457421 |
| 536 | Learning Linear Regression Models over Factorized Joins | 2016 | SIGMOD | 0.0001693369 |
| 835 | Scaling Factorization Machines to Relational Data | 2013 | VLDB | 0.00013721583 |
| 1,271 | Hybrid-Range Partitioning Strategy: A New Declustering Strategy for Multiprocessor Database Machines | 1990 | VLDB | 0.0001138443 |
| 2,162 | Heterogeneity-aware Distributed Parameter Servers | 2017 | SIGMOD | 9.0581831e-05 |
| 3,490 | MLog: Towards Declarative In-Database Machine Learning | 2017 | VLDB | 7.3667971e-05 |
| 11,999 | LDA*: A Robust and Large-scale Topic Modeling System | 2017 | VLDB | 5.093636e-05 |
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| 9 | 11,104 | Datamap-Driven Tabular Coreset Selection for Classifier Training | 2025 | VLDB |
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