# TCUDB: Accelerating Database with Tensor Processors

arXiv: https://arxiv.org/abs/2112.07552 · HTML: https://mlsanity.com/paper/2112.07552/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2021.

## Built on by (most cited first, 4 of 4)

- [Query Processing on Tensor Computation Runtimes](https://mlsanity.com/paper/2203.01877/) (2022)
- [TC-GS: A Faster Gaussian Splatting Module Utilizing Tensor Cores](https://mlsanity.com/paper/2505.24796/) (2025)
- [JoinBoost: Grow Trees Over Normalized Data Using Only SQL](https://mlsanity.com/paper/2307.00422/) (2023)
- [AMULET: Adaptive Matrix-Multiplication-Like Tasks](https://mlsanity.com/paper/2305.08872/) (2023)

## Abstract (arXiv)

The emergence of novel hardware accelerators has powered the tremendous growth of machine learning in recent years. These accelerators deliver incomparable performance gains in processing high-volume matrix operators, particularly matrix multiplication, a core component of neural network training and inference. In this work, we explored opportunities of accelerating database systems using NVIDIA's Tensor Core Units (TCUs). We present TCUDB, a TCU-accelerated query engine processing a set of query operators including natural joins and group-by aggregates as matrix operators within TCUs. Matrix multiplication was considered inefficient in the past; however, this strategy has remained largely unexplored in conventional GPU-based databases, which primarily rely on vector or scalar processing. We demonstrate the significant performance gain of TCUDB in a range of real-world applications including entity matching, graph query processing, and matrix-based data analytics. TCUDB achieves up to 288x speedup compared to a baseline GPU-based query engine.