798 citations
- Nvidia (United Kingdom)GB35 papers
- Stanford UniversityUS21 papers
- California Institute of TechnologyUS17 papers
- University of TorontoCA17 papers
- Massachusetts Institute of TechnologyUS16 papers
- University of California, BerkeleyUS14 papers
- University of Illinois Urbana-ChampaignUS14 papers
- Seattle UniversityUS13 papers
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- Lawrence Berkeley National LaboratoryUS12 papers
- University of WashingtonUS12 papers
- Argonne National LaboratoryUS11 papers
Showing 2023 · cs.DCShow all
3 papers · 2 filters
cs.DC2023★ 26 cited
Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses
Jeongmin Brian Park, Vikram Sharma Mailthody, Zaid Qureshi +1
Graph Neural Networks (GNNs) are emerging as a powerful tool for learning from graph-structured data and performing sophisticated inference tasks in various application domains. Al…
cs.DC2023★ 2 cited
Accelerating MPI Collectives with Process-in-Process-based Multi-object Techniques
Jiajun Huang, Kaiming Ouyang, Yujia Zhai +8
In the exascale computing era, optimizing MPI collective performance in high-performance computing (HPC) applications is critical. Current algorithms face performance degradation d…
cs.DC2023★ 3 cited
Hector: An Efficient Programming and Compilation Framework for Implementing Relational Graph Neural Networks in GPU Architectures
Kun Wu, Mert Hidayetoğlu, Xiang Song +4
Relational graph neural networks (RGNNs) are graph neural networks with dedicated structures for modeling the different types of nodes and edges in heterogeneous graphs. While RGNN…