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Many Graph Neural Network (GNN) training systems have emerged recently to support efficient GNN training. Since GNNs embody complex data dependencies between training samples, the training of GNNs should address distinct challenges different from DNN
Externí odkaz:
http://arxiv.org/abs/2311.13279
Graph Neural Networks (GNNs) have demonstrated outstanding performance in various applications. Existing frameworks utilize CPU-GPU heterogeneous environments to train GNN models and integrate mini-batch and sampling techniques to overcome the GPU me
Externí odkaz:
http://arxiv.org/abs/2311.13225
Publikováno v:
Chengshi guidao jiaotong yanjiu, Vol 27, Iss 10, Pp 326-329 (2024)
Objective The maintenance and management of urban rail transit signaling systems encompass various tasks such as equipment monitoring, production organization, and information management, each task involving one or more business systems. These system
Externí odkaz:
https://doaj.org/article/71aac3ff6a434693ba71009f559d385a
Akademický článek
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Dimelaena tibetica M. Ai & Xin Y. Wang, sp. nov. MycoBank No. 841524 Characterized by a grayish yellow surface, usual covered in white pruina, and by a plane, radiate-plicate thallus margin, a crustose, areolate thallus, adnate apothecia and numerous
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9bc3bc647b7947c7b92eb539ff9e47c3
Distributed Machine Learning (DML) systems are utilized to enhance the speed of model training in data centers (DCs) and edge nodes. The Parameter Server (PS) communication architecture is commonly employed, but it faces severe long-tail latency caus
Externí odkaz:
http://arxiv.org/abs/2305.04279
Autor:
Liu, Ziyue, Li, Yixing, Hu, Jing, Yu, Xinling, Shiau, Shinyu, Ai, Xin, Zeng, Zhiyu, Zhang, Zheng
Thermal issue is a major concern in 3D integrated circuit (IC) design. Thermal optimization of 3D IC often requires massive expensive PDE simulations. Neural network-based thermal prediction models can perform real-time prediction for many unseen new
Externí odkaz:
http://arxiv.org/abs/2302.12949
Processing large graphs with memory-limited GPU needs to resolve issues of host-GPU data transfer, which is a key performance bottleneck. Existing GPU-accelerated graph processing frameworks reduce the data transfers by managing the active subgraph t
Externí odkaz:
http://arxiv.org/abs/2208.14935
Publikováno v:
In Global Ecology and Conservation November 2024 55
Publikováno v:
In International Journal of Electrical Power and Energy Systems October 2024 161