Accelerating String Matching Using Multi-Threaded Algorithm on GPU

Lin, Cheng-Hung; Tsai, Sheng-Yu; Liu, Chen-Hsiung; Chang, Shih-Chieh; Shyu, Jyuo-Min

doi:10.1109/glocom.2010.5683320

Cited by 60 publications

(65 citation statements)

References 14 publications

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“…For example in the Fig. 2 assume that using a single thread to find out pattern PQR in the text takes 16 cycles as [1]. If we divide the single thread into four threads and allocate each segment a thread to find out the same pattern simultaneously then four threads takes only four cycle to detect same pattern.…”

Section: Problem Of Direct Implementation Of Boyer-moore Algorithm Onmentioning

confidence: 99%

“…The pattern PQR appeared in the segment 3 and 4.In this condition thread 2 and 3 cannot find out pattern. To overcome these problems as in fig.4 with any despite the boundary detection problem can be resolve by using refer to (1).We divide the text record into threads such as that number of character in the each threads stimulated by refer to (1).…”

Section: Fig 2: Single and Multiple Threadsmentioning

confidence: 99%

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Accelerating Enhanced Boyer-Moore String Matching Algorithm on Multicore GPU for Network Security

Jaiswal¹

2014

IJCA

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Graphics Processing Units (GPUs) were developed for graphics processing and it was not highly-parallel. But to overcome this problem developed General Purpose Computing on GPU, this is known as GPGPU.Boyer-Moore exact string matching algorithm are heavily used in the application of antivirus engines, DNA sequencing, text editors, intrusion detection etc. In this environment, the GPU was highly-parallel, multithreaded. In this Paper extend the GPU application into other area such as string matching problem. This paper shows the results on adapting the enhanced Boyer-Moore (EBM) string matching algorithm to run on GPU paradigm and comparison with serial version and multithreaded version on CPU.The experimental results demonstrate that GPU version of enhanced Boyer-Moore (EBM) string matching algorithm 10 times faster than CPU version and 9 times faster than the multithreaded version. It can be also see there that multithreaded version of EBM algorithm about 12% to 13% peak performance than serial version of EBM.Speedup of EBM algorithm is grow and 12x to 13x than serial one. General TermsPattern Recognition, Security, Algorithms etc.

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Section: Problem Of Direct Implementation Of Boyer-moore Algorithm Onmentioning

confidence: 99%

Section: Fig 2: Single and Multiple Threadsmentioning

confidence: 99%

Accelerating Enhanced Boyer-Moore String Matching Algorithm on Multicore GPU for Network Security

Jaiswal¹

2014

IJCA

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“…Then the machine regards the same input character until the character causes a valid transition. In our OpenCL version snort, we used OpenCL to change the AC algorithm for parallelism based on PFAC [9]. The idea of the parallel algorithm of AC is: Give an input stream have N byte, we will create N threads corresponding to N byte.…”

Section: Packet Detecting Using Opencl Ac Algorithmmentioning

confidence: 99%

Parallel Design and Performance Optimization based on OpenCL Snort

Xie¹,

Xiang²,

Chen³

2017

Proceedings of the 2017 2nd Joint International Information Technology, Mechanical and Electronic Engineering Conference (JIMEC

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With the rapid increasement of the network speed and number of threats which hide in the network poses enormous challenges to network intrusion detection systems (NIDS). As the most popular NIDS, snort can run as a single threaded application. However, it may not be able to detect intrusions in real-time especially in networks with high traffic. In this paper, a parallel module OpenCL Snort (OCLSnort) is introduced: realize parallel pattern matching algorithm using GPU and innovate new architecture which is more suitable for the parallel algorithm. The result showed that OCLSnort can detect the attacks correctly and effectively, the new system not only has markedly improved on throughput, also effectively reduced the CPU utilization and memory usage.

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“…These networks and bioinformatics applications are computationally demanding and require high speed parallel processing. To speed up the performance of Aho-Corasick algorithm, a parallel version of Aho-Corasick PFAC (parallel failure less Aho-Corasick) is developed [12,13,14]. DFA is placed in global memory from where it is going to accessed by each thread to take the copy of DFA.…”

Section: Related Workmentioning

confidence: 99%

Serial and Parallel Bayesian Spam Filtering using Aho-Corasick and PFAC

Haseeb¹,

Motwani²,

Saxena³

2013

IJCA

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With the rapid growth of Internet, E-mail, with its convenient and efficient characteristics, has become an important means of communication in people"s life. It reduces the cost of communication. It comes with Spam. Spam emails, also known as "junk e-mails", are unsolicited one"s sent in bulk with hidden or forged identity of the sender, address, and header information. It is vital to pursue more effective spam filtering approaches to maintain normal operations of e-mail systems and to protect the interests of email users. In this paper we developed a Spam filter based on Bayesian filtering method using Aho-corasick and PFAC string matching algorithm. This filter developed an improved version of spam filter based on traditional Bayesian spam filtering to improve spam filtering efficiency, and to reduce chances of misjudgement of malignant spam. For further improvement of Spam filtering process we are transform the filter in to parallel spam filter on GPGPU's by using PFAC Algorithm.

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Accelerating String Matching Using Multi-Threaded Algorithm on GPU

Cited by 60 publications

References 14 publications

Accelerating Enhanced Boyer-Moore String Matching Algorithm on Multicore GPU for Network Security

Accelerating Enhanced Boyer-Moore String Matching Algorithm on Multicore GPU for Network Security

Parallel Design and Performance Optimization based on OpenCL Snort

Serial and Parallel Bayesian Spam Filtering using Aho-Corasick and PFAC

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