Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects
Summary: NVLink 2.0-based interconnect eliminates CPU-GPU transfer bottlenecks, enabling large-scale in-memory processing on GPUs. Demonstrates scalable no-partitioning hash join beyond GPU memory with up to 18x speedup vs PCIe 3.0 and 7.3x vs optimized CPU. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Clemens Lutz
- 2. Sebastian Breß
- 3. Steffen Zeuch
- 4. Tilmann Rabl
- 5. Volker Markl
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| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,568 | Powerful GPUs or Fast Interconnects: Analyzing Relational Workloads on Modern GPUs | 2025 | VLDB | 4.7084322e-05 |
| 775 | Relational Joins on Graphics Processors | 2008 | SIGMOD | 0.00016823862 |
| 4,002 | MG-Join: A Scalable Join for Massively Parallel Multi-GPU Architectures | 2021 | SIGMOD | 6.545665e-05 |
| 6,223 | Distributed GPU Joins on Fast RDMA-capable Networks | 2023 | SIGMOD | 5.1496398e-05 |
| 7,155 | Evaluating Multi-GPU Sorting with Modern Interconnects | 2022 | SIGMOD | 4.8149812e-05 |
| 3,898 | Efficient Join Algorithms For Large Database Tables in a Multi-GPU Environment | 2021 | VLDB | 6.6551268e-05 |
| 10,749 | Scaling GPU-Accelerated Databases beyond GPU Memory Size | 2025 | VLDB | 4.1945683e-05 |
| 7,209 | GPU-accelerated data management under the test of time | 2020 | CIDR | 4.7996023e-05 |
| 9,838 | Efficiently Joining Large Relations on Multi-GPU Systems | 2025 | VLDB | 4.2740344e-05 |
| 5,247 | Triton Join: Efficiently Scaling to a Large Join State on GPUs with Fast Interconnects | 2022 | SIGMOD | 5.6057839e-05 |