Optimizing Performance of Text Searching Using CPU and GPUs

  • M. Musthafa Baig
  • S. Sivakumar
  • Soumya Ranjan NayakEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1119)


In this work, we are solving the major problem of reducing the time complexity of searching a string in huge corpus by using GPU as our computational environment (utilizing GPGPU and CUDA as programming platform) and Knuth–Morris–Pratt (KMP) and BMH (Boyer–Moore–Horspool) as string matching algorithms. String matching is a widely used technique in current research interest of various application areas such as bioinformatics, network intrusion detection, and computer virus scan. Although data are memorized in various ways, text remains the main form to exchange information. This is particularly evident in literature or linguistics where data are composed of huge corpus and dictionaries. These analytics are required in computer science where a large amount of data is stored in linear files. To search a particular string from these huge corpus takes more time in traditional CPU’s and this is a major problem.


KMP algorithm BMH algorithm Heterogeneous computing GPGPU CUDA Performance factors 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • M. Musthafa Baig
    • 1
  • S. Sivakumar
    • 1
  • Soumya Ranjan Nayak
    • 2
    Email author
  1. 1.Department of Computer Science and EngineeringKoneru Lakshamiah Education FoundationVaddeswaram, GunturIndia
  2. 2.Chitkara University Institute of Engineering and Technology, Chitkara UniversityPunjabIndia

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