Advertisement

Optimizing Performance of Text Searching Using CPU and GPUs

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

Abstract

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.

Keywords

KMP algorithm BMH algorithm Heterogeneous computing GPGPU CUDA Performance factors 

References

  1. 1.
    Park, S., Kim, D., Park, N.: High performance parallel KMP algorithm on heterogeneous architecture. In: IEEE 3rd International Journal of Foundation and Application (2018)Google Scholar
  2. 2.
    Rajesh, S., Prathima, S., Reddy, D.: Unusual pattern detection in DNA database using KMP algorithm. Int. J. Comput. Appl. 1, 1–7 (2010)Google Scholar
  3. 3.
    Nayak, S.R., Mishra, J., Palai, G.: Analysing roughness of surface through fractal dimension: a review. Image Vis. Comput. 89, 21–34 (2019)CrossRefGoogle Scholar
  4. 4.
    Nayak, S.R., Khandual, A., Mishra, J.: Ground truth study of fractal dimension of color images of similar textures. J. Text. Inst. 109, 1159–1167 (2018)CrossRefGoogle Scholar
  5. 5.
    Nayak, S.R., Mishra, J., Khandual, A., Palai, G.: Fractal dimension of RGB color images. Int. J. Light Electron Opt. 162, 196–205 (2018)CrossRefGoogle Scholar
  6. 6.
    Nayak, S.R., Mishra, J., Palai, G.: An extended DBC approach by using maximum Euclidian distance for fractal dimension of color images. Int. J. Light Electron Opt. 166, 110–115 (2018)CrossRefGoogle Scholar
  7. 7.
    Nayak, S.R., Mishra, J.: A modified triangle box-counting with precision in error fit. J. Inform. Optim. Sci. 39(1), 113–128 (2018)MathSciNetGoogle Scholar
  8. 8.
    Zha, X., Sahni, S.: GPU-to-GPU and Host-to-Host multipattern string matching on a GPU. IEEE Trans. Comput. 62, 1156–1169 (2013)Google Scholar
  9. 9.
    Xu, K., Cui, W., Hu, Y., Guo, L.: Bit-parallel multiple approximate string matching based on GPU. Procedia Comput. Sci. 17, 523–529 (2013)Google Scholar
  10. 10.
    Nagaveni, V., Raju, G.: Various string matching algorithms for DNA sequences to detect breast cancer using CUDA processors. Int. J. Eng. Technol. (2014)Google Scholar
  11. 11.
    Kouzinopoulos, C.S., Michailidis, P.D., Margaritis, K.G.: Scalable Comput. Pract. Exp. (2015)Google Scholar
  12. 12.
    Sharma, J., Singh, M.: CUDA based Rabin-Karp patterns matching for deep packet inspection on a multicore GPU. Int. J. Comput. Netw. Inform. Secur. 7 (2015)Google Scholar
  13. 13.
    Ashkiani, S., Amenta, N., Owens, J.D.: Parallel approaches to the string matching problem on the GPU. In: 28th ACM Symposium on Parallelism in Algorithms and Architectures (2016)Google Scholar

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

Personalised recommendations