Abstract
With the ever-increasing capabilities of modern hardware and breakthroughs in the DNA biometrics field, we are presenting a new, scalable and innovative method to accelerate the DNA analysis process used in criminal investigations, by building an improved methodology for using large-scale GPU-based automata for performing high-throughput pattern-matching. Our approach focuses on all important stages of preparing for the pattern-matching process, tackling with all major steps, from creation, to preprocessing, to the runtime performance. Finally, we experiment using real-world DNA sequences and apply the process to the human DNA genome, for an evaluation of our implementation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
NVIDIA: NVIDIA CUDA Compute Unified Device Architecture Programming Guide, version 4.1. http://developer.download.nvidia.com/compute/DevZone/docs/html/C/doc/CUDA_C_Programming_Guide.pdf
OpenCL: The open standard for parallel programming of heterogeneous systems. https://www.khronos.org/opencl/
Aho, A.V., Corasick, M.J.: Efficient string matching: An aid to bibliographic search. Comm. ACM 18(6), 333–340 (1975)
Yin, Z., Lan, H., Tan, G., et al.: Computing platforms for big biological data analytics: perspectives and challenges. Comput. Struct. Biotechnol. J. 15, 403–411 (2017)
Adey, S.: GPU Accelerated Pattern Matching Algorithm for DNA Sequences to Detect Cancer using CUDA, Master’s Dissertation, Master of Technology in Computer Engineering, Department of Computer Engineering and Information Technology, College of Engineering, Pune (2013)
Memeti, S., Pllana, S.: GSNP: a DNA single-nucleotide polymorphism detection system with GPU acceleration. In: International Conference on Parallel Processing (ICPP), pp. 592–601 (2011)
Memeti, S., Pllana, S.: Accelerating DNA sequence analysis using intel xeon Phi. In: Trustcom/BigDataSE/ISPA, vol. 3, pp. 222–227. IEEE (2015)
Santos, F., Machado, H.: Patterns of exchange of forensic DNA data in the European Union through the Prüm system. Sci. Justice 57(4), 307–313 (2017)
Taverne, M.D., Broeders, A.P.A.: Cross-border patterns in DNA matches between the Netherlands and Belgium. Sci. Justice 57(1), 28–34 (2017)
Thambawita, D., Ragel, R., Elkaduwe, D.: To use or not to use: Graphics processing units (GPUs) for pattern matching algorithms. In: 7th International Conference on Information and Automation for Sustainability (ICIAfS) (2014)
Nehemia, R.: Implementation of multipattern string matching accelerated with GPU for intrusion detection system. In: IAES International Conference on Electrical Engineering, Computer Science and Informatics (2017)
Lin, C.H., Liu, C.H., Chien, L.S., Chang, S.C.: Accelerating pattern matching using a novel parallel algorithm on GPUs. IEEE Trans. Comput. 62(10), 1906–1916 (2012)
Pungila, C., Negru, V.: A highly-efficient memory-compression approach for GPU-accelerated virus signature matching. In: Information Security Conference (ISC) (2012)
The FASTA Project. https://en.wikipedia.org/wiki/FASTA_format, https://www.ebi.ac.uk/Tools/sss/fasta/
GenBank: The NIH genetic sequence database. https://www.ncbi.nlm.nih.gov/genbank/
Acknowledgement
This work was partially supported by the InnoHPC Interreg - Danube Transnational Programme grant. The views expressed in this paper do not necessarily reflect those of the corresponding project consortium members.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Pungila, C., Negru, V. (2019). Accelerating DNA Biometrics in Criminal Investigations Through GPU-Based Pattern Matching. In: Graña, M., et al. International Joint Conference SOCO’18-CISIS’18-ICEUTE’18. SOCO’18-CISIS’18-ICEUTE’18 2018. Advances in Intelligent Systems and Computing, vol 771. Springer, Cham. https://doi.org/10.1007/978-3-319-94120-2_44
Download citation
DOI: https://doi.org/10.1007/978-3-319-94120-2_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94119-6
Online ISBN: 978-3-319-94120-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)