5 citations · 5 across the 2 of their papers we have counts for
3 papers
cs.DC2021
Cloud Collectives: Towards Cloud-aware Collectives forML Workloads with Rank Reordering
Liang Luo, Jacob Nelson, Arvind Krishnamurthy +1
ML workloads are becoming increasingly popular in the cloud. Good cloud training performance is contingent on efficient parameter exchange among VMs. We find that Collectives, the…
cs.LG2021★ 5 cited
Accelerating SpMM Kernel with Cache-First Edge Sampling for Graph Neural Networks
Chien-Yu Lin, Liang Luo, Luis Ceze
Graph neural networks (GNNs), an emerging deep learning model class, can extract meaningful representations from highly expressive graph-structured data and are therefore gaining p…
cs.DC2018
Parameter Hub: a Rack-Scale Parameter Server for Distributed Deep Neural Network Training
Liang Luo, Jacob Nelson, Luis Ceze +2
Distributed deep neural network (DDNN) training constitutes an increasingly important workload that frequently runs in the cloud. Larger DNN models and faster compute engines are s…