most citedPyTorch-Direct: Enabling GPU Centric Data Access for Very Large Graph Neural Network Training with Irregular Accesses

3 citations · 4 across the 2 of their papers we have counts for

collaborators
Showing cs.DCShow all

2 papers · 1 filter

cs.DC2023

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…

cs.DC2020★ 1 cited

TEMPI: An Interposed MPI Library with a Canonical Representation of CUDA-aware Datatypes

Carl Pearson, Kun Wu, I-Hsin Chung +2

MPI derived datatypes are an abstraction that simplifies handling of non-contiguous data in MPI applications. These datatypes are recursively constructed at runtime from primitive…