211 citations · 733 across the 33 of their papers we have counts for
8 papers · 1 filter
GNNPipe: Scaling Deep GNN Training with Pipelined Model Parallelism
Jingji Chen, Zhuoming Chen, Xuehai Qian
Communication is a key bottleneck for distributed graph neural network (GNN) training. This paper proposes GNNPipe, a new approach that scales the distributed full-graph deep GNN t…
Kudu: An Efficient and Scalable Distributed Graph Pattern Mining Engine
Jingji Chen, Xuehai Qian
This paper proposes Kudu, a distributed execution engine with a well-defined abstraction that can be integrated with existing single-machine graph pattern mining (GPM) systems to p…
DwarvesGraph: A High-Performance Graph Mining System with Pattern Decomposition
Jingji Chen, Xuehai Qian
Graph pattern mining (GPM) is an important application that identifies structures from graphs. Despite the recent progress, the performance gap between the state-of-the-art GPM sys…
Comprehensive Framework of RDMA-enabled Concurrency Control Protocols
Chao Wang, Kezhao Huang, Xuehai Qian
In this paper, we develop RCC, the first unified and comprehensive RDMA-enabled distributed transaction processing framework supporting six serializable concurrency control protoco…
Heterogeneity-Aware Asynchronous Decentralized Training
Qinyi Luo, Jiaao He, Youwei Zhuo +1
Distributed deep learning training usually adopts All-Reduce as the synchronization mechanism for data parallel algorithms due to its high performance in homogeneous environment. H…
Hop: Heterogeneity-Aware Decentralized Training
Qinyi Luo, Jinkun Lin, Youwei Zhuo +1
Recent work has shown that decentralized algorithms can deliver superior performance over centralized ones in the context of machine learning. The two approaches, with the main dif…
HyPar: Towards Hybrid Parallelism for Deep Learning Accelerator Array
Linghao Song, Jiachen Mao, Youwei Zhuo +3
With the rise of artificial intelligence in recent years, Deep Neural Networks (DNNs) have been widely used in many domains. To achieve high performance and energy efficiency, hard…
GraphR: Accelerating Graph Processing Using ReRAM
Linghao Song, Youwei Zhuo, Xuehai Qian +2
This paper presents GRAPHR, the first ReRAM-based graph processing accelerator. GRAPHR follows the principle of near-data processing and explores the opportunity of performing mass…