output
20152025
most citedSimple and Deep Graph Convolutional Networks

402 citations

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5 papers · 1 filter

cs.DC2021

GearV: A Two-Gear Hypervisor for Mixed-Criticality IoT Systems

Kaiwen Long, Chong Xing, Yuebin Qi +10

This paper presents GearV, a two-gear lightweight hypervisor architecture to address the some known challenges. By dividing hypervisor into some partitions, and dividing scheduling…

cs.DC202029 cited

DAPPLE: A Pipelined Data Parallel Approach for Training Large Models

Shiqing Fan, Yi Rong, Chen Meng +10

It is a challenging task to train large DNN models on sophisticated GPU platforms with diversified interconnect capabilities. Recently, pipelined training has been proposed as an e…

cs.DC2020

HaoCL: Harnessing Large-scale Heterogeneous Processors Made Easy

Yao Chen, Xin Long, Jiong He +5

The pervasive adoption of Deep Learning (DL) and Graph Processing (GP) makes it a de facto requirement to build large-scale clusters of heterogeneous accelerators including GPUs an…

cs.DC2019

FusionStitching: Boosting Execution Efficiency of Memory Intensive Computations for DL Workloads

Guoping Long, Jun Yang, Wei Lin

Performance optimization is the art of continuous seeking a harmonious mapping between the application domain and hardware. Recent years have witnessed a surge of deep learning (DL…

cs.DC201915 cited

AliGraph: A Comprehensive Graph Neural Network Platform

Rong Zhu, Kun Zhao, Hongxia Yang +5

An increasing number of machine learning tasks require dealing with large graph datasets, which capture rich and complex relationship among potentially billions of elements. Graph…