Zobrazeno 1 - 10
of 10 732
pro vyhledávání: '"distributed training"'
Autor:
Twomey, Beth1 btwomey@udel.edu, Johnson, Annie2 akjohnso@udel.edu, Estes, Colleen3 cestes@udel.edu
Publikováno v:
Information Technology & Libraries. Sep2024, Vol. 43 Issue 3, p1-8. 8p.
Distributed training methods are crucial for large language models (LLMs). However, existing distributed training methods often suffer from communication bottlenecks, stragglers, and limited elasticity. Local SGD methods have been proposed to address
Externí odkaz:
http://arxiv.org/abs/2412.07210
Autor:
Feng, Yicheng, Chen, Yuetao, Chen, Kaiwen, Li, Jingzong, Wu, Tianyuan, Cheng, Peng, Wu, Chuan, Wang, Wei, Ho, Tsung-Yi, Xu, Hong
Simulation offers unique values for both enumeration and extrapolation purposes, and is becoming increasingly important for managing the massive machine learning (ML) clusters and large-scale distributed training jobs. In this paper, we build Echo to
Externí odkaz:
http://arxiv.org/abs/2412.12487
Autor:
Fernandez, Jared, Wehrstedt, Luca, Shamis, Leonid, Elhoushi, Mostafa, Saladi, Kalyan, Bisk, Yonatan, Strubell, Emma, Kahn, Jacob
Dramatic increases in the capabilities of neural network models in recent years are driven by scaling model size, training data, and corresponding computational resources. To develop the exceedingly large networks required in modern applications, suc
Externí odkaz:
http://arxiv.org/abs/2411.13055
Recent advances in Generative Artificial Intelligence have fueled numerous applications, particularly those involving Generative Adversarial Networks (GANs), which are essential for synthesizing realistic photos and videos. However, efficiently train
Externí odkaz:
http://arxiv.org/abs/2411.03999
Distributed machine learning has recently become a critical paradigm for training large models on vast datasets. We examine the stochastic optimization problem for deep learning within synchronous parallel computing environments under communication c
Externí odkaz:
http://arxiv.org/abs/2411.03742
Transformer models have emerged as potent solutions to a wide array of multidisciplinary challenges. The deployment of Transformer architectures is significantly hindered by their extensive computational and memory requirements, necessitating the rel
Externí odkaz:
http://arxiv.org/abs/2407.02081
In the area of large-scale training of graph embeddings, effective training frameworks and partitioning methods are critical for handling large networks. However, they face two major challenges: 1) existing synchronized distributed frameworks require
Externí odkaz:
http://arxiv.org/abs/2409.09887
Heterogeneous Graph Neural Networks (HGNNs) leverage diverse semantic relationships in Heterogeneous Graphs (HetGs) and have demonstrated remarkable learning performance in various applications. However, current distributed GNN training systems often
Externí odkaz:
http://arxiv.org/abs/2408.09697
A number of production deep learning clusters have attempted to explore inference hardware for DNN training, at the off-peak serving hours with many inference GPUs idling. Conducting DNN training with a combination of heterogeneous training and infer
Externí odkaz:
http://arxiv.org/abs/2407.02327