SPID-Join: A Skew-resistant Processing-in-DIMM Join Algorithm Exploiting the Bank- and Rank-level Parallelisms of DIMMs

Summary: SPID-Join is a skew-resistant Processing-In-DIMM join algorithm that exploits bank- and rank-level parallelisms in DIMMs. By replicating join keys across banks and ranks, it boosts per-key bandwidth to balance inter-IDP loads under skew, trading replication overhead for faster, more scalable joins. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 7059
Venue: SIGMOD
Year: 2024
Pagerank: 5.093636e-05
Overall Rank: 11,204 | 23.14%
DOI: 10.1145/3698827

Incoming Non-self Citations Over Time

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Authors

1. Suhyun Lee (Yonsei University)
2. Chaemin Lim (Yonsei University)
3. Jinwoo Choi (Yonsei University)
4. Heelim Choi (Yonsei University)
5. Chan Lee (Yonsei University)
6. Yongjun Park (Yonsei University)
7. Kwanghyun Park (Yonsei University)
8. Hanjun Kim (Yonsei University)
9. Youngsok Kim (Yonsei University)

BibTeX Citation

@inproceedings{lee_sigmod24,
        title = {{SPID-Join: A Skew-resistant Processing-in-DIMM Join Algorithm Exploiting the Bank- and Rank-level Parallelisms of DIMMs}},
        author = {Lee, Suhyun and Lim, Chaemin and Choi, Jinwoo and Choi, Heelim and Lee, Chan and Park, Yongjun and Park, Kwanghyun and Kim, Hanjun and Kim, Youngsok},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3698827},
        url = {https://dl.acm.org/doi/10.1145/3698827},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

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Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 19 of 19 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
18	How Good Are Query Optimizers, Really?	2016	VLDB	0.00059284255
29	Database Architecture Optimized for the New Bottleneck: Memory Access	1999	VLDB	0.00052093615
209	Sort vs. Hash Revisited: Fast Join Implementation on Modern Multi-Core CPUs	2009	VLDB	0.00024932174
252	Multi-Core, Main-Memory Joins: Sort vs. Hash Revisited	2014	VLDB	0.00023242719
476	The Making of TPC-DS	2006	VLDB	0.00017860667
481	Practical Skew Handling in Parallel Joins	1992	VLDB	0.00017780716
959	Memory-Efficient Hash Joins	2015	VLDB	0.00012953588
1,188	Bucket Spreading Parallel Hash: A New, Robust, Parallel Hash Join Method for Data Skew in the Super Database Computer (SDC)	1990	VLDB	0.00011756444
1,236	Handling Data Skew in Multiprocessor Database Computers Using Partition Tuning	1991	VLDB	0.00011548179
1,657	Handling Data Skew in Parallel Joins in Shared-Nothing Systems	2008	SIGMOD	0.00010096933
1,664	Two-Level Sampling for Join Size Estimation	2017	SIGMOD	0.00010070362
2,962	To Partition, or Not to Partition, That is the Join Question in a Real System	2021	SIGMOD	7.9170451e-05
3,227	MG-Join: A Scalable Join for Massively Parallel Multi-GPU Architectures	2021	SIGMOD	7.6217889e-05
4,535	Triton Join: Efficiently Scaling to a Large Join State on GPUs with Fast Interconnects	2022	SIGMOD	6.6419266e-05
5,515	FPGA-based Multithreading for In-Memory Hash Joins	2015	CIDR	6.189963e-05
6,710	PIM-tree: A Skew-resistant Index for Processing-in-Memory	2023	VLDB	5.796491e-05
6,957	Design and Analysis of a Processing-in-DIMM Join Algorithm: A Case Study with UPMEM DIMMs	2023	SIGMOD	5.7303405e-05
7,760	NOCAP: Near-Optimal Correlation-Aware Partitioning Joins	2023	SIGMOD	5.5505651e-05
8,234	The Case for Learned In-Memory Joins	2023	VLDB	5.460955e-05

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