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TCUDB: Accelerating Database with Tensor Processors

Summary: TCUDB uses NVIDIA Tensor Core Units to accelerate database workloads by treating joins and aggregates as matrix ops on TCUs. Reports up to 288x speedups vs a GPU baseline on entity matching, graph queries, and matrix-based analytics. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6321
Venue
SIGMOD
Year
2022
Pagerank
5.7072189e-05
Overall Rank
5,088 | 64.61%
DOI
10.1145/3514221.3517869

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Incoming Citations (Sorted by Pagerank)

Showing 12 of 12 citing papers.

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Outgoing Citations (Sorted by Pagerank)

Showing 32 of 32 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
35 MonetDB/X100: Hyper-Pipelining Query Execution 2005 CIDR 0.00076197749
239 GPUTeraSort: High Performance Graphics Co-processor Sorting for Large Database Management 2006 SIGMOD 0.00031617428
300 Deep Learning for Entity Matching: A Design Space Exploration 2018 SIGMOD 0.00028441466
398 Big Data Integration 2013 VLDB 0.00024372588
497 Column-Stores vs. Row-Stores: How Different Are They Really? 2008 SIGMOD 0.00021716559
712 Magellan: Toward Building Entity Matching Management Systems 2016 VLDB 0.00017732426
775 Relational Joins on Graphics Processors 2008 SIGMOD 0.00016823862
1,004 Storage Management in the NVRAM Era 2014 VLDB 0.00014695628
1,273 The Yin and Yang of Processing Data Warehousing Queries on GPU Devices 2013 VLDB 0.00012912938
1,338 Query Processing on Smart SSDs: Opportunities and Challenges 2013 SIGMOD 0.00012493384
1,590 Column-oriented Database Systems 2009 VLDB 0.00011233838
1,686 Fast Computation of Database Operations using Graphics Processors 2004 SIGMOD 0.00010917794
2,040 A Study of the Fundamental Performance Characteristics of GPUs and CPUs for Database Analytics 2020 SIGMOD 9.7057698e-05
2,067 HippogriffDB: Balancing I/O and GPU Bandwidth in Big Data Analytics 2016 VLDB 9.6392739e-05
2,287 Pipelined Query Processing in Coprocessor Environments 2018 SIGMOD 9.0972606e-05
2,330 Concurrent Analytical Query Processing with GPUs 2014 VLDB 9.0192228e-05
2,519 Revisiting Co-Processing for Hash Joins on the Coupled CPU-GPU Architecture 2013 VLDB 8.6078505e-05
2,934 AIDA - Abstraction for Advanced In-Database Analytics 2018 VLDB 7.8595778e-05
3,305 Robust Query Processing in Co-Processor-accelerated Databases 2016 SIGMOD 7.2460965e-05
3,327 Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects 2020 SIGMOD 7.2205738e-05
3,465 GPL: A GPU-based Pipelined Query Processing Engine 2016 SIGMOD 7.0695873e-05
3,696 Why it is time for a HyPE: A Hybrid Query Processing Engine for Efficient GPU Coprocessing in DBMS 2013 VLDB 6.834483e-05
3,948 A Comparative Evaluation of Systems for Scalable Linear Algebra-based Analytics 2018 VLDB 6.5959084e-05
4,033 In-RDBMS Hardware Acceleration of Advanced Analytics 2018 VLDB 6.5113267e-05
4,198 Aggregation Support for Modern Graph Analytics in TigerGraph 2020 SIGMOD 6.3677305e-05
4,363 Hardware-conscious Query Processing in GPU-accelerated Analytical Engines 2019 CIDR 6.2552614e-05
6,041 FPGA: What's in it for a Database? 2009 SIGMOD 5.2407055e-05
6,644 A Relational Matrix Algebra and its Implementation in a Column Store 2020 SIGMOD 4.9782839e-05
6,647 Fast Join Project Query Evaluation using Matrix Multiplication 2020 SIGMOD 4.9772122e-05
7,216 FrogWild! – Fast PageRank Approximations on Graph Engines 2015 VLDB 4.7984727e-05
7,416 MILC: Inverted List Compression in Memory 2017 VLDB 4.7355258e-05
8,048 Lowering the Latency of Data Processing Pipelines Through FPGA based Hardware Acceleration 2020 VLDB 4.5977431e-05
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