3 citations · 5 across the 4 of their papers we have counts for
5 papers
Tessel: Boosting Distributed Execution of Large DNN Models via Flexible Schedule Search
Zhiqi Lin, Youshan Miao, Guanbin Xu +4
Increasingly complex and diverse deep neural network (DNN) models necessitate distributing the execution across multiple devices for training and inference tasks, and also require…
ZIPPER: Exploiting Tile- and Operator-level Parallelism for General and Scalable Graph Neural Network Acceleration
Zhihui Zhang, Jingwen Leng, Shuwen Lu +5
Graph neural networks (GNNs) start to gain momentum after showing significant performance improvement in a variety of domains including molecular science, recommendation, and trans…
CrossoverScheduler: Overlapping Multiple Distributed Training Applications in a Crossover Manner
Cheng Luo, Lei Qu, Youshan Miao +2
Distributed deep learning workloads include throughput-intensive training tasks on the GPU clusters, where the Distributed Stochastic Gradient Descent (SGD) incurs significant comm…
Towards Efficient Large-Scale Graph Neural Network Computing
Lingxiao Ma, Zhi Yang, Youshan Miao +4
Recent deep learning models have moved beyond low-dimensional regular grids such as image, video, and speech, to high-dimensional graph-structured data, such as social networks, br…
RPC Considered Harmful: Fast Distributed Deep Learning on RDMA
Jilong Xue, Youshan Miao, Cheng Chen +3
Deep learning emerges as an important new resource-intensive workload and has been successfully applied in computer vision, speech, natural language processing, and so on. Distribu…