Efficient Multi-training Framework of Image Deep Learning on GPU Cluster

Chen, Chun-Fu Richard; Lee, Gwo Giun Chris; Xia, Yinglong; Lin, Wei-An; Suzumura, Toyotaro; Lin, Ching‐Yung

doi:10.1109/ism.2015.119

Cited by 3 publications

(1 citation statement)

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“…However, the training procedures of deep belief networks are highly serial and dependent, which makes it difficult to convert into parallel form. Chen et al developed a pipelining system for image deep learning on a GPU cluster to leverage the heavy workload of training procedure. They organized the training of multiple deep learning models in parallel, where each stage of the pipeline is managed by a particular GPU with a partition of the train data.…”

Section: Data Mining Tasks and Techniquesmentioning

confidence: 99%

A survey on graphic processing unit computing for large‐scale data mining

Cano

2017

WIREs Data Min & Knowl

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General purpose computation using Graphic Processing Units (GPUs) is a wellestablished research area focusing on high-performance computing solutions for massively parallelizable and time-consuming problems. Classical methodologies in machine learning and data mining cannot handle processing of massive and high-speed volumes of information in the context of the big data era. GPUs have successfully improved the scalability of data mining algorithms to address significantly larger dataset sizes in many application areas. The popularization of distributed computing frameworks for big data mining opens up new opportunities for transformative solutions combining GPUs and distributed frameworks. This survey analyzes current trends in the use of GPU computing for large-scale data mining, discusses GPU architecture advantages for handling volume and velocity of data, identifies limitation factors hampering the scalability of the problems, and discusses open issues and future directions.

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Section: Data Mining Tasks and Techniquesmentioning

confidence: 99%

A survey on graphic processing unit computing for large‐scale data mining

Cano

2017

WIREs Data Min & Knowl

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A real-time and reliable dynamic migration model for concurrent taskflow in a GPU cluster

Fang

Chen

2018

Cluster Comput

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GPU-Based Embedded Intelligence Architectures and Applications

Ang

Seng

2021

Electronics

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This paper present contributions to the state-of-the art for graphics processing unit (GPU-based) embedded intelligence (EI) research for architectures and applications. This paper gives a comprehensive review and representative studies of the emerging and current paradigms for GPU-based EI with the focus on the architecture, technologies and applications: (1) First, the overview and classifications of GPU-based EI research are presented to give the full spectrum in this area that also serves as a concise summary of the scope of the paper; (2) Second, various architecture technologies for GPU-based deep learning techniques and applications are discussed in detail; and (3) Third, various architecture technologies for machine learning techniques and applications are discussed. This paper aims to give useful insights for the research area and motivate researchers towards the development of GPU-based EI for practical deployment and applications.

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Efficient Multi-training Framework of Image Deep Learning on GPU Cluster

Cited by 3 publications

References 13 publications

A survey on graphic processing unit computing for large‐scale data mining

A survey on graphic processing unit computing for large‐scale data mining

A real-time and reliable dynamic migration model for concurrent taskflow in a GPU cluster

GPU-Based Embedded Intelligence Architectures and Applications

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