東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Automatic SIMD vectorization of SSA-...

Karrenberg, Ralf.

FindBook

Google Book

Amazon

博客來

Automatic SIMD vectorization of SSA-based control flow graphs

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Automatic SIMD vectorization of SSA-based control flow graphs/ by Ralf Karrenberg.
作者:	Karrenberg, Ralf.
出版者:	Wiesbaden :Springer Fachmedien Wiesbaden : : 2015.,
面頁冊數:	xvi, 187 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Vector processing (Computer science) -
電子資源:	http://dx.doi.org/10.1007/978-3-658-10113-8
ISBN:	9783658101138 (electronic bk.)

Automatic SIMD vectorization of SSA-based control flow graphs
Karrenberg, Ralf.

Automatic SIMD vectorization of SSA-based control flow graphs[electronic resource] /by Ralf Karrenberg. - Wiesbaden :Springer Fachmedien Wiesbaden :2015. - xvi, 187 p. :ill., digital ;24 cm.

Ralf Karrenberg presents Whole-Function Vectorization (WFV), an approach that allows a compiler to automatically create code that exploits data-parallelism using SIMD instructions. Data-parallel applications such as particle simulations, stock option price estimation, or video decoding require the same computations to be performed on huge amounts of data. Without WFV, one processor core executes a single instance of a data-parallel function. WFV transforms the function to execute multiple instances at once using SIMD instructions. The author describes an advanced WFV algorithm that includes a variety of analyses and code generation techniques. He shows that this approach improves the performance of the generated code in a variety of use cases. Contents Introduction, Foundations & Terminology, Related Work SIMD Property Analyses Whole-Function Vectorization Dynamic Code Variants, Evaluation, Conclusion, Outlook Target Groups Computer science researchers and students working in data-parallel computing Software and compiler engineers in the fields high-performance computing and compiler construction About the Author Ralf Karrenberg received his PhD in computer science at Saarland University in 2015. His seminal research on compilation techniques for SIMD architectures found wide recognition in both academia and the CPU and GPU industry. Currently, he is working for NVIDIA in Berlin. Prior to that, he contributed to research and development for visual effects in blockbuster movies at Weta Digital, New Zealand.

ISBN: 9783658101138 (electronic bk.)

Standard No.: 10.1007/978-3-658-10113-8doiSubjects--Topical Terms:

692137
Vector processing (Computer science)

LC Class. No.: QA76.5

Dewey Class. No.: 004.35

Automatic SIMD vectorization of SSA-based control flow graphs
LDR:02525nam a2200325 a 4500 001 2007533
003 DE-He213
005 20160115151321.0
006 m d
007 cr nn 008maaau
008 160219s2015 gw s 0 eng d
020 $a 9783658101138 (electronic bk.)
020 $a 9783658101121 (paper)
024 7 $a 10.1007/978-3-658-10113-8 $2 doi
035 $a 978-3-658-10113-8
040 $a GP $c GP
041 1 $a eng $b eng $b ger $h eng
050 4 $a QA76.5
072 7 $a UMX $2 bicssc
072 7 $a UMC $2 bicssc
072 7 $a COM051010 $2 bisacsh
072 7 $a COM010000 $2 bisacsh
082 0 4 $a 004.35 $2 23
090 $a QA76.5 $b .K18 2015
100 1 $a Karrenberg, Ralf. $3 2156393
245 1 0 $a Automatic SIMD vectorization of SSA-based control flow graphs $h [electronic resource] / $c by Ralf Karrenberg.
260 $a Wiesbaden : $b Springer Fachmedien Wiesbaden : $b Imprint: Springer Vieweg, $c 2015.
300 $a xvi, 187 p. : $b ill., digital ; $c 24 cm.
520 $a Ralf Karrenberg presents Whole-Function Vectorization (WFV), an approach that allows a compiler to automatically create code that exploits data-parallelism using SIMD instructions. Data-parallel applications such as particle simulations, stock option price estimation, or video decoding require the same computations to be performed on huge amounts of data. Without WFV, one processor core executes a single instance of a data-parallel function. WFV transforms the function to execute multiple instances at once using SIMD instructions. The author describes an advanced WFV algorithm that includes a variety of analyses and code generation techniques. He shows that this approach improves the performance of the generated code in a variety of use cases. Contents Introduction, Foundations & Terminology, Related Work SIMD Property Analyses Whole-Function Vectorization Dynamic Code Variants, Evaluation, Conclusion, Outlook Target Groups Computer science researchers and students working in data-parallel computing Software and compiler engineers in the fields high-performance computing and compiler construction About the Author Ralf Karrenberg received his PhD in computer science at Saarland University in 2015. His seminal research on compilation techniques for SIMD architectures found wide recognition in both academia and the CPU and GPU industry. Currently, he is working for NVIDIA in Berlin. Prior to that, he contributed to research and development for visual effects in blockbuster movies at Weta Digital, New Zealand.
650 0 $a Vector processing (Computer science) $3 692137
650 0 $a Compilers (Computer programs) $3 535138
650 0 $a Parallel processing (Electronic computers) $3 653284
650 1 4 $a Computer Science. $3 626642
650 2 4 $a Programming Languages, Compilers, Interpreters. $3 891123
650 2 4 $a Computer Graphics. $3 892532
650 2 4 $a Appl.Mathematics/Computational Methods of Engineering. $3 890892
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-658-10113-8
950 $a Computer Science (Springer-11645)