Maximus: A Modular Accelerated Query Engine for Data Analytics on Heterogeneous Systems
Summary: Maximus is a modular query engine for heterogeneous systems, enabling cross-engine ops and flexible I/O. Operator-level integration via Substrait with third-party engines enables cross-CPU/GPU pipelines and overlapped compute/communication for TPC-H. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Marko Kabić (ETH Zurich)
- 2. Shriram Chandran (ETH Zurich)
- 3. Gustavo Alonso (ETH Zurich)
BibTeX Citation
@inproceedings{kabic_sigmod25,
title = {{Maximus: A Modular Accelerated Query Engine for Data Analytics on Heterogeneous Systems}},
author = {Kabić, Marko and Chandran, Shriram and Alonso, Gustavo},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725324},
url = {https://dl.acm.org/doi/10.1145/3725324},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,431 | Powerful GPUs or Fast Interconnects: Analyzing Relational Workloads on Modern GPUs | 2025 | VLDB | 5.6198965e-05 |
| 7,904 | Terabyte-Scale Analytics in the Blink of an Eye | 2026 | VLDB | 5.5180253e-05 |
| 10,118 | Rethinking Analytical Processing in the GPU Era | 2026 | CIDR | 5.0935618e-05 |
| 10,121 | End-to-End Declarative Data Analytics: Co-designing Engines, Interfaces, and Cloud Infrastructure | 2026 | CIDR | 5.0935618e-05 |
| 10,375 | GraphMatch: Subgraph Query Processing on Steroids | 2026 | SIGMOD | 5.0935618e-05 |
| 10,957 | Towards Designing Future-Proof Data Processing Systems | 2025 | VLDB | 5.0935618e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 24 of 24 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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|---|---|---|---|---|
| 1 | 8,887 | A Model for Query Execution Over Heterogeneous Instances | 2024 | CIDR |
| 2 | 1,076 | High-Speed Query Processing over High-Speed Networks | 2016 | VLDB |
| 3 | 1,977 | HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines | 2019 | VLDB |
| 4 | 10,118 | Rethinking Analytical Processing in the GPU Era | 2026 | CIDR |
| 5 | 4,243 | Orchestrating Data Placement and Query Execution in Heterogeneous CPU-GPU DBMS | 2022 | VLDB |
| 6 | 6,826 | Demonstrating Efficient Query Processing in Heterogeneous Environments | 2014 | SIGMOD |
| 7 | 2,714 | Robust Query Processing in Co-Processor-accelerated Databases | 2016 | SIGMOD |
| 8 | 3,638 | Fast Queries Over Heterogeneous Data Through Engine Customization | 2016 | VLDB |
| 9 | 7,899 | Modularis: Modular Relational Analytics over Heterogeneous Distributed Platforms | 2021 | VLDB |
| 10 | 3,791 | Hardware-conscious Query Processing in GPU-accelerated Analytical Engines | 2019 | CIDR |