Jyotsna Sharma

Work place: Computer Science & Engineering Department Thapar University Patiala, INDIA

E-mail: jyotsana.sharma@thapar.edu


Research Interests: Software Construction, Software Development Process, Software Engineering


Jyotsna Sharma is a research scholar at the CSED,Thapar University. She has focused her research on DPI based Forensic Analysis of Network Traffic using Grid Infrastructure. She is an M.Phil. in Computer Science and also a Graduate Member of The Institution of Engineers(India). She is a Certified Ethical Hacker(C|EH) from the EC-Council. She has several research articles to her credit and has also contributed a chapter to the „Handbook of Research on Grid Technologies and Utility Computing, an IGI Global Publication, and is currently authoring a book on „Web Engineering‟. She received the Suman Sharma National Award from the Institution of Engineers (India) for academic distinction in the computer engineering discipline. She won the 2009 Google Global Community Scholarship for GHC2009. She has several years experience as an Assistant Professor and a Software Developer.

Author Articles
CUDA based Rabin-Karp Pattern Matching for Deep Packet Inspection on a Multicore GPU

By Jyotsna Sharma Maninder Singh

DOI: https://doi.org/10.5815/ijcnis.2015.10.08, Pub. Date: 8 Sep. 2015

This paper presents a study of the improvement in efficiency of the Rabin-Karp pattern-matching algorithm based Deep Packet Inspection. NVIDIA GPU is programmed with the NVIDIA's general purpose parallel computing architecture, CUDA, that leverages the parallel compute engine in NVIDIA GPUs to solve many complex computational problems in a more efficient way than on a CPU. The proposed CUDA based implementation on a multicore GPU outperforms the Intel quadcore processor and runs upto 14 times faster by executing the algorithm in parallel to search for the pattern from the text. The speedup may not sound exorbitant but nonetheless is significant, keeping in view that the experiments have been conducted on real data and not synthetic data and, optimal performance even with the huge increase in traffic was the main expectation, not just an improvement in speed with few test cases.

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