Automatic Optimization of Matrix Implementations for Distributed Machine Learning and Linear Algebra
Summary: Proposes automatic optimization of physical data layout for ML/LA, selecting among row/column, tiled, or relational representations to boost performance. Algorithms solve the layout-choice problem; a DBMS prototype yields speedups for ML/LA workloads. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Shangyu Luo
- 2. Dimitrije Jankov
- 3. Binhang Yuan
- 4. Chris Jermaine
Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,084 | In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle | 2022 | SIGMOD | 5.7091191e-05 |
| 8,786 | AWARE: Workload-aware, Redundancy-exploiting Linear Algebra | 2023 | SIGMOD | 4.4521262e-05 |
| 9,391 | Database as Runtime: Compiling LLMs to SQL for In-database Model Serving | 2025 | SIGMOD | 4.3441378e-05 |
| 9,806 | The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format | 2024 | SIGMOD | 4.2805224e-05 |
| 10,122 | TranSQL+: Serving Large Language Models with SQL on Low-Resource Hardware | 2026 | SIGMOD | 4.1945683e-05 |
| 11,363 | Givens QR Decomposition over Relational Databases | 2022 | SIGMOD | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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