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Automatic SIMD Vectorization of SSA-based Control Flow Graphs

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Automatic SIMD Vectorization of SSA-based Control Flow Graphs/ by Ralf Karrenberg.
Author:	Karrenberg, Ralf.
Description:	XVI, 187 p. 41 illus., 5 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Programming languages (Electronic computers). -
Online resource:	https://doi.org/10.1007/978-3-658-10113-8
ISBN:	9783658101138

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. - 1st ed. 2015. - XVI, 187 p. 41 illus., 5 illus. in color.online resource.

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

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

1127615
Programming languages (Electronic computers).

LC Class. No.: QA76.7-76.73

Dewey Class. No.: 005.13

Automatic SIMD Vectorization of SSA-based Control Flow Graphs
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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.
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