7 citations · 14 across the 7 of their papers we have counts for
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cs.DC2023★ 1 cited
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…
cs.LG2023
Adam Accumulation to Reduce Memory Footprints of both Activations and Gradients for Large-scale DNN Training
Yijia Zhang, Yibo Han, Shijie Cao +5
Running out of GPU memory has become a main bottleneck for large-scale DNN training. How to reduce the memory footprint during training has received intensive research attention. W…
cs.DC2023★ 2 cited
SuperScaler: Supporting Flexible DNN Parallelization via a Unified Abstraction
Zhiqi Lin, Youshan Miao, Guodong Liu +10
With the growing model size, deep neural networks (DNN) are increasingly trained over massive GPU accelerators, which demands a proper parallelization plan that transforms a DNN mo…