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A Hybrid B+-tree as Solution for In-Memory Indexing on CPU-GPU Heterogeneous Computing Platforms
Summary: HB+-tree: a hybrid CPU-GPU B+-tree for in-memory indexing on heterogeneous platforms, addressing GPU memory limits by jointly using CPU and GPU compute and memory. Achieves up to 240M index queries/sec, about 2.4× faster than CPU-only.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 5109
- Venue
- SIGMOD
- Year
- 2016
- Pagerank
- 6.7750901e-05
- Overall Rank
- 3,777 | 73.73%
- DOI
-
10.1145/2882903.2882918
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 12 of 12 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 102 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049545203 |
| 3,131 |
FINEdex: A Fine-grained Learned Index Scheme for Scalable and Concurrent Memory Systems |
2022 |
VLDB |
7.4985793e-05 |
| 3,151 |
A Memory Bandwidth-Efficient Hybrid Radix Sort on GPUs |
2017 |
SIGMOD |
7.4720668e-05 |
| 3,327 |
Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects |
2020 |
SIGMOD |
7.2205738e-05 |
| 4,161 |
Access Path Selection in Main-Memory Optimized Data Systems: Should I Scan or Should I Probe? |
2017 |
SIGMOD |
6.3938006e-05 |
| 6,059 |
Cache-Efficient Fork-Processing Patterns on Large Graphs |
2021 |
SIGMOD |
5.2307519e-05 |
| 6,476 |
Parallel Index-based Stream Join on a Multicore CPU |
2020 |
SIGMOD |
5.0496617e-05 |
| 7,360 |
ParPaRaw: Massively Parallel Parsing of Delimiter-Separated Raw Data |
2020 |
VLDB |
4.7525925e-05 |
| 8,171 |
GTS: GPU-based Tree Index for Fast Similarity Search |
2024 |
SIGMOD |
4.5688498e-05 |
| 9,953 |
Distributed Stream KNN Join |
2021 |
SIGMOD |
4.2405999e-05 |
| 10,562 |
FB+-tree: A Memory-Optimized B+-tree with Latch-Free Update |
2025 |
VLDB |
4.1945683e-05 |
| 11,020 |
Accelerating Merkle Patricia Trie with GPU |
2024 |
VLDB |
4.1945683e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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