國立虎尾科技大學 |

High-Level Program Optimizations for Data Analytics.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	High-Level Program Optimizations for Data Analytics./
作者:	Ding, Yufei.
面頁冊數:	1 online resource (170 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Contained By:	Dissertation Abstracts International79-04B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355458329

High-Level Program Optimizations for Data Analytics.
Ding, Yufei.

High-Level Program Optimizations for Data Analytics. - 1 online resource (170 pages)

Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Data analytics in many modern applications often spend a large number of cycles on unnecessary computations, while such redundant computations have been hidden in the useful instructions of the applications and are elusive for those automatic code optimizations developed over the past decades. Many algorithms have been manually designed for each specific application, however, none of them are automatic/semi-automatic or generally applicable. My research resides at the intersection of Compiler Technology and (Big) Data Analytics, pioneering the efforts of raising the level of automatic program optimizations from implementations to algorithms, and from instructions to formulas. In particular, we find that the algorithm generated by such automatic High-Level Program Optimization matches or outperforms the algorithms manually designed by the domain experts, and thus could have saved decades of manual effort by domain experts.

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

Mode of access: World Wide Web

ISBN: 9780355458329Subjects--Topical Terms:

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

554714
Electronic books.

High-Level Program Optimizations for Data Analytics.
LDR:03001ntm a2200337Ki 4500 001 910860
005 20180517112612.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9780355458329
035 $a (MiAaPQ)AAI10708347
035 $a AAI10708347
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Ding, Yufei. $3 1182344
245 1 0 $a High-Level Program Optimizations for Data Analytics.
264 0 $c 2017
300 $a 1 online resource (170 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
500 $a Adviser: Xipeng Shen.
502 $a Thesis (Ph.D.) $c North Carolina State University $d 2017.
504 $a Includes bibliographical references
520 $a Data analytics in many modern applications often spend a large number of cycles on unnecessary computations, while such redundant computations have been hidden in the useful instructions of the applications and are elusive for those automatic code optimizations developed over the past decades. Many algorithms have been manually designed for each specific application, however, none of them are automatic/semi-automatic or generally applicable. My research resides at the intersection of Compiler Technology and (Big) Data Analytics, pioneering the efforts of raising the level of automatic program optimizations from implementations to algorithms, and from instructions to formulas. In particular, we find that the algorithm generated by such automatic High-Level Program Optimization matches or outperforms the algorithms manually designed by the domain experts, and thus could have saved decades of manual effort by domain experts.
520 $a Enabling such High-Level Program Optimizations, in general, faces three fundamental challenges: (1) the requirement of the high-level semantics of the application, which is often hidden in the low-level implementation; (2) the requirement of knowledge of beneficial and legal higher-level transformations that could change large blocks of code; and (3) the need for efficient ways to explore the enormous space of algorithmic or other higher-level transformation choices. My thesis shows how these challenges can be addressed through the development of proper abstractions, new optimization algorithms, and an innovative two-level learning algorithm design. The enabled High-Level Program Optimizations frequently lower the practical computational complexities of applications, boosting their performance by up to several orders of magnitude.
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
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a North Carolina State University. $b Computer Science. $3 1182345
773 0 $t Dissertation Abstracts International $g 79-04B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10708347 $z click for full text (PQDT)