Automatic SIMD Vectorization of SSA-based Control Flow Graphs, by Ralf Karrenberg
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Automatic SIMD Vectorization of SSA-based Control Flow Graphs, by Ralf Karrenberg
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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.
Automatic SIMD Vectorization of SSA-based Control Flow Graphs, by Ralf Karrenberg- Amazon Sales Rank: #4524797 in Books
- Published on: 2015-06-16
- Released on: 2015-06-16
- Original language: English
- Number of items: 1
- Dimensions: 8.27" h x .48" w x 5.83" l, .55 pounds
- Binding: Paperback
- 187 pages
From the Back Cover
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.
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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Most helpful customer reviews
1 of 1 people found the following review helpful. Ralf has developed a nice notation & a practical framework to analyze a function ... By SB Ralf has developed a nice notation & a practical framework to analyze a function to see if it is vectorizable. It is the holy grail of SIMD (Single Instruction Multiple Data). Secs 5.6 & 5.9 — are elegant. Ch 5 must be fully grasped before Ch.6.
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