Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (7): 248-254.doi: 10.11896/jsjkx.241000181

• Artificial Intelligence • Previous Articles     Next Articles

Research on Automatic Vectorization Benefit Evaluation Model Based on Particle SwarmAlgorithm

LIU Mengzhen1, ZHOU Qinglei1, HAN Lin2, NIE Kai2, LI Haoran2, CHEN Mengyao1, LIU Haohao2   

  1. 1 School of Computer Science and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
    2 National Supercomputing Center in Zhengzhou, Zhengzhou University, Zhengzhou 450001, China
  • Received:2024-10-31 Revised:2025-02-12 Published:2025-07-17
  • About author:LIU Mengzhen,born in 2000,postgra-duate.Her main research interests include compiler optimization and high-performance computing.
    NIE Kai,born in 1987,Ph.D,senior engineer,graduate advisor.His main research interests include advanced compilation techniques,high-performance computing and so on.
  • Supported by:
    2024 Henan Provincial Major Science and Technology Project(241100210100),2024 Henan Provincial Science and Technology Research Project(242102211094),2023 National Key R&D Program for High-Performance Computing(2023YFB3002505) and 2022 Henan Provincial Major Science and Technology Project(221100210600).

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Abstract: Automatic vectorization leverages SIMD components to accelerate program execution,easing the programmers' workload and serving as a key optimization in the GCC compiler.However,the current benefit evaluation model in GCC lacks precision,affecting vectorization decisions.To improve vectorization efficiency on the Sunway platform,this study introduces a new benefit evaluation model within GCC.The model designs cost metrics specific to the Sunway processor's backend instruction set and applies a particle swarm algorithm to optimize these costs,enhancing evaluation accuracy and vectorization performance.Experimental results on SPEC2006 and SPEC2017 benchmarks show that the proposed model delivers up to 7.6% and 5.75% performance gains,respectively,over the default GCC model.These outcomes confirm the model's effectiveness in refining automatic vectorization and improving the usability of the Sunway compilation system.With more accurate evaluations,the model supports better optimization decisions,resulting in enhanced platform performance.

Key words: Sunway, GCC compiler, Auto-vectorization, Cost model, Particle swarm optimization

CLC Number: 

  • TP314
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