Automatic SIMD Vectorization of SSA-based Control Flow Graphs - Brossura

Karrenberg, Ralf

Brossura

ISBN 10: 3658101121 ISBN 13: 9783658101121

Casa editrice: Springer Vieweg, 2015

Riassunto

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.

Su questo libro

Informazioni sull'autore

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.

Dalla quarta di copertina

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.

Dettagli dell'edizione

• Editore: Springer Vieweg
• Data di pubblicazione: 2015
• Lingua: Inglese
• ISBN 10: 3658101121
• ISBN 13: 9783658101121
• Rilegatura: Copertina flessibile
• Numero di pagine: 204
• Contatto del produttore: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Germania