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pro vyhledávání: '"Bian, Zhengda"'
Autor:
Fang, Jiarui, Zhang, Geng, Han, Jiatong, Li, Shenggui, Bian, Zhengda, Li, Yongbin, Liu, Jin, You, Yang
Deep learning recommendation models (DLRMs) have been widely applied in Internet companies. The embedding tables of DLRMs are too large to fit on GPU memory entirely. We propose a GPU-based software cache approaches to dynamically manage the embeddin
Externí odkaz:
http://arxiv.org/abs/2208.05321
Autor:
Li, Shenggui, Liu, Hongxin, Bian, Zhengda, Fang, Jiarui, Huang, Haichen, Liu, Yuliang, Wang, Boxiang, You, Yang
The success of Transformer models has pushed the deep learning model scale to billions of parameters. Due to the limited memory resource of a single GPU, However, the best practice for choosing the optimal parallel strategy is still lacking, since it
Externí odkaz:
http://arxiv.org/abs/2110.14883
Efficient GPU resource scheduling is essential to maximize resource utilization and save training costs for the increasing amount of deep learning workloads in shared GPU clusters. Existing GPU schedulers largely rely on static policies to leverage t
Externí odkaz:
http://arxiv.org/abs/2108.03645
Together with the improvements in state-of-the-art accuracies of various tasks, deep learning models are getting significantly larger. However, it is extremely difficult to implement these large models because limited GPU memory makes it impossible t
Externí odkaz:
http://arxiv.org/abs/2105.14500
The recent Natural Language Processing techniques have been refreshing the state-of-the-art performance at an incredible speed. Training huge language models is therefore an imperative demand in both industry and academy. However, huge language model
Externí odkaz:
http://arxiv.org/abs/2105.14450
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