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System Design for Large Scale Machin...

ProQuest Information and Learning Co.

System Design for Large Scale Machine Learning.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	System Design for Large Scale Machine Learning./
作者:	Venkataraman, Shivaram.
面頁冊數:	1 online resource (107 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
Contained By:	Dissertation Abstracts International79-08B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355831740

System Design for Large Scale Machine Learning.
Venkataraman, Shivaram.

System Design for Large Scale Machine Learning. - 1 online resource (107 pages)

Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2017.

Includes bibliographical references

The last decade has seen two main trends in the large scale computing: on the one hand we have seen the growth of cloud computing where a number of big data applications are deployed on shared cluster of machines. On the other hand there is a deluge of machine learning algorithms used for applications ranging from image classification, machine translation to graph processing, and scientific analysis on large datasets. In light of these trends, a number of challenges arise in terms of how we program, deploy and achieve high performance for large scale machine learning applications.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355831740Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

System Design for Large Scale Machine Learning.
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245 1 0 $a System Design for Large Scale Machine Learning.
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300 $a 1 online resource (107 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
500 $a Advisers: Michael J. Franklin; Ion Stoica.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2017.
504 $a Includes bibliographical references
520 $a The last decade has seen two main trends in the large scale computing: on the one hand we have seen the growth of cloud computing where a number of big data applications are deployed on shared cluster of machines. On the other hand there is a deluge of machine learning algorithms used for applications ranging from image classification, machine translation to graph processing, and scientific analysis on large datasets. In light of these trends, a number of challenges arise in terms of how we program, deploy and achieve high performance for large scale machine learning applications.
520 $a In this dissertation we study the execution properties of machine learning applications and based on these properties we present the design and implementation of systems that can address the above challenges. We first identify how choosing the appropriate hardware can affect the performance of applications and describe Ernest, an efficient performance prediction scheme that uses experiment design to minimize the cost and time taken for building performance models. We then design scheduling mechanisms that can improve performance using two approaches: first by improving data access time by accounting for locality using data-aware scheduling and then by using scalable scheduling techniques that can reduce coordination overheads.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, Berkeley. $b Computer Science. $3 1179511
773 0 $t Dissertation Abstracts International $g 79-08B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10686954 $z click for full text (PQDT)

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