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 |
|---|---|---|---|---|
| 2,010 | VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning | 2021 | SIGMOD | 9.1945893e-05 |
| 3,242 | Towards Demystifying Serverless Machine Learning Training | 2021 | SIGMOD | 7.4967268e-05 |
| 3,312 | BlindFL: Vertical Federated Machine Learning without Peeking into Your Data | 2022 | SIGMOD | 7.4357331e-05 |
| 5,437 | PS2: Parameter Server on Spark | 2019 | SIGMOD | 6.1279659e-05 |
| 5,724 | An Experimental Evaluation of Large Scale GBDT Systems | 2019 | VLDB | 6.0150027e-05 |
| 5,877 | BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees | 2019 | SIGMOD | 5.9599042e-05 |
| 5,999 | BAGUA: Scaling up Distributed Learning with System Relaxations | 2022 | VLDB | 5.9170896e-05 |
| 6,678 | Reliable Data Distillation on Graph Convolutional Network | 2020 | SIGMOD | 5.7098648e-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 |
|---|---|---|---|---|
| 83 | Space-Efficient Online Computation of Quantile Summaries | 2001 | SIGMOD | 0.00035962466 |
| 123 | Schism: a Workload-Driven Approach to Database Replication and Partitioning | 2010 | VLDB | 0.00030749898 |
| 195 | Integrating Vertical and Horizontal Partitioning into Automated Physical Database Design | 2004 | SIGMOD | 0.00025619089 |
| 243 | Automating Physical Database Design in a Parallel Database | 2002 | SIGMOD | 0.00023349603 |
| 521 | Learning Linear Regression Models over Factorized Joins | 2016 | SIGMOD | 0.00016923519 |
| 851 | Scaling Factorization Machines to Relational Data | 2013 | VLDB | 0.00013453278 |
| 1,288 | Hybrid-Range Partitioning Strategy: A New Declustering Strategy for Multiprocessor Database Machines | 1990 | VLDB | 0.00011178484 |
| 2,186 | Heterogeneity-aware Distributed Parameter Servers | 2017 | SIGMOD | 8.8916253e-05 |
| 3,524 | MLog: Towards Declarative In-Database Machine Learning | 2017 | VLDB | 7.2303094e-05 |
| 12,302 | LDA*: A Robust and Large-scale Topic Modeling System | 2017 | VLDB | 4.9769913e-05 |
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