國立虎尾科技大學 |

Exploiting Heterogeneity for Efficient Deep Learning.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Exploiting Heterogeneity for Efficient Deep Learning./
作者:	Lin, Shouxu.
面頁冊數:	1 online resource (56 pages)
附註:	Source: Masters Abstracts International, Volume: 84-06.
Contained By:	Masters Abstracts International84-06.
標題:	Information science. -
電子資源:	click for full text (PQDT)
ISBN:	9798363516306

Exploiting Heterogeneity for Efficient Deep Learning.
Lin, Shouxu.

Exploiting Heterogeneity for Efficient Deep Learning. - 1 online resource (56 pages)

Source: Masters Abstracts International, Volume: 84-06.

Thesis (M.S.)--Carnegie Mellon University, 2022.

Includes bibliographical references

In recent years, we have observed that the scale of Deep Neural Network (DNN) Models, with respect to the number of training examples, the number of model parameters, or both, has multiplied by several orders of magnitude. Accompanied by this is the significant growth in the computational requirements for training these large DNN models. To accelerate these giant DNN models, several different parallelization strategies, such as data and model parallelism, have been widely adopted, where the computation workloads are distributed and parallelized across multiple accelerators. Several recent works have been conducted to automate the parallelization strategy for either model-specific or general DNN models.This thesis steps beyond the existing early works in distributed deep learning by exploiting hardware accelerator and DNN model heterogeneity. We investigate the exploitation of heterogeneity through two case studies: heterogeneity-aware DL cluster scheduler and heterogeneity-aware DNN parallelization planner.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798363516306Subjects--Topical Terms:

561178
Information science.
Subjects--Index Terms:

HeterogeneityIndex Terms--Genre/Form:

554714
Electronic books.

Exploiting Heterogeneity for Efficient Deep Learning.
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502 $a Thesis (M.S.)--Carnegie Mellon University, 2022.
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520 $a In recent years, we have observed that the scale of Deep Neural Network (DNN) Models, with respect to the number of training examples, the number of model parameters, or both, has multiplied by several orders of magnitude. Accompanied by this is the significant growth in the computational requirements for training these large DNN models. To accelerate these giant DNN models, several different parallelization strategies, such as data and model parallelism, have been widely adopted, where the computation workloads are distributed and parallelized across multiple accelerators. Several recent works have been conducted to automate the parallelization strategy for either model-specific or general DNN models.This thesis steps beyond the existing early works in distributed deep learning by exploiting hardware accelerator and DNN model heterogeneity. We investigate the exploitation of heterogeneity through two case studies: heterogeneity-aware DL cluster scheduler and heterogeneity-aware DNN parallelization planner.
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538 $a Mode of access: World Wide Web
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650 4 $a Computer science. $3 573171
653 $a Heterogeneity
653 $a Deep Learning
653 $a Deep Neural Networks
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