Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (5): 76-82.doi: 10.11896/jsjkx.231200174

• High Performance Computing • Previous Articles     Next Articles

Research on Function Vectorization Technology Based on Directive Statements

LIU Lili1, SHAN Zheng1, LI Yingying1, WU Wenhao2, LIU Wenbo1   

  1. 1 PLA Strategic Support Force Information Engineering University,Zhengzhou 450001,China
    2 National Research Center of Parallel Computer Engineering Technology,Wuxi,Jiangsu 100190,China
  • Received:2023-12-25 Revised:2024-06-29 Online:2025-05-15 Published:2025-05-12
  • About author:LIU Lili,born in 1999,postgraduate.Her main research interests include automatic vectorization of compilers and so on.
    LI Yingying,born in 1984,Ph.D,associate professor,master supervisor.Her main research interests include high performance computing and advanced compilation techniques.
  • Supported by:
    2024 Laboratory for Advanced Computing and Intelligence Engineering(ACIE) Project.

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Abstract: With the continuous development of processor technology,SIMD(Single Instruction Multiple Data) vectorization has been widely applied in various fields.However,previous research has mainly focused on loops and basic blocks,while full-function vectorization can better exploit the advantages of SIMD instructions,thereby improving application performance.This paper proposes a guided statement-based function vectorization method.Firstly,a relatively simple guided statement is added to the loop involving function calls,which can vectorize the instructions involving function calls in the loop.Secondly,the vectorization of the called function is achieved by using full function vectorization to generate a vectorized full function instead of inlining it.Finally,the function call instructions in the loop are processed to generate vectorized function call instructions.We selected 10 benchmarks from ISPC benchmark tests and SIMD library benchmark tests to evaluate our method,and the experimental results show that compared to scalar,the average speedup achieved is 6.949 times.

Key words: Function vectorization, SIMD, Automatic vectorization

CLC Number: 

  • TP312
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