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cs.AR2022
Characterizing and Understanding HGNNs on GPUs
Mingyu Yan, Mo Zou, Xiaocheng Yang +4
Heterogeneous graph neural networks (HGNNs) deliver powerful capacity in heterogeneous graph representation learning. The execution of HGNNs is usually accelerated by GPUs. Therefo…
cs.AR2022
Multi-node Acceleration for Large-scale GCNs
Gongjian Sun, Mingyu Yan, Duo Wang +5
Limited by the memory capacity and compute power, singe-node graph convolutional neural network (GCN) accelerators cannot complete the execution of GCNs within a reasonable amount…
cs.AR2022
Alleviating Datapath Conflicts and Design Centralization in Graph Analytics Acceleration
Haiyang Lin, Mingyu Yan, Duo Wang +5
Previous graph analytics accelerators have achieved great improvement on throughput by alleviating irregular off-chip memory accesses. However, on-chip side datapath conflicts and…