GPU accelerated fuzzy connected image segmentation by using CUDA

Zhuge, Ying; Cao, Yong; Miller, Robert W.

doi:10.1109/iembs.2009.5333158

Cited by 8 publications

(1 citation statement)

References 17 publications

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“…Besides acceleration of password cracking, GPU is used in speeding up Secure Socket Layer (SSL) in SSLShader [14], and outperforming the exsiting software routers [15]. And, fast GPU-based implementaions of the brute-force K-nearest neighbor search algorithm are shown by [16], and a parallel fuzzy connected image segmentation algorithm with GPU is presented by [17]. Representative GPUs that can be used for general operations such as password cracking are Nvidia Geforce, Tesla, and Quadro series.…”

Section: Hardware Acceleration Of Brute-force Password Crackingmentioning

confidence: 99%

GPU-Accelerated Password Cracking of PDF Files

Kim¹

2011

KSII TIIS

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Digital document file such as Adobe Acrobat or MS-Office is encrypted by its own ciphering algorithm with a user password. When this password is not known to a user or a forensic inspector, it is necessary to recover the password to open the encrypted file. Password cracking by brute-force search is a perfect approach to discover the password but a time consuming process. This paper presents a new method of speeding up password recovery on Graphic Processing Unit (GPU) using a Compute Unified Device Architecture (CUDA). PDF files are chosen as a password cracking target, and the Abode Acrobat password recovery algorithm is examined. Experimental results show that the proposed method gives high performance at low cost, with a cluster of GPU nodes significantly speeding up the password recovery by exploiting a number of computing nodes. Password cracking performance is increased linearly in proportion to the number of computing nodes and GPUs.

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Section: Hardware Acceleration Of Brute-force Password Crackingmentioning

confidence: 99%

GPU-Accelerated Password Cracking of PDF Files

Kim¹

2011

KSII TIIS

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A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation

et al. 2015

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Techniques of medical image processing and analysis accelerated by high-performance computing: a systematic literature review

Gulo

Sementille

Tavares

2017

J Real-Time Image Proc

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Techniques of medical image processing and analysis play a crucial role in many clinical scenarios, including in diagnosis and treatment planning. However, immense quantities of data and high complexity of the algorithms often used are computationally demanding. As a result, there now exists a wide range of techniques of medical image processing and analysis that require the application of high-performance computing solutions in order to reduce the required runtime. The main purpose of this review is to provide a comprehensive reference source of techniques of medical image processing and analysis that have been accelerated by high-performance computing solutions. With this in mind, the articles available in the Scopus and Web of Science electronic repositories were searched. Subsequently, the most relevant articles found were individually analyzed in order to identify: (a) the metrics used to evaluate computing performance, (b) the high-performance computing solution used, (c) the parallel design adopted, and (d) the task of medical image processing and analysis involved. Hence, the techniques of medical image processing and analysis found were identified, reviewed, and discussed, particularly in terms of computational performance. Consequently, the techniques reviewed herein present the progress made so far in reducing the computational runtime involved, and the difficulties and challenges that remain to be overcome.

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GPU accelerated fuzzy connected image segmentation by using CUDA

Cited by 8 publications

References 17 publications

GPU-Accelerated Password Cracking of PDF Files

GPU-Accelerated Password Cracking of PDF Files

A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation

Techniques of medical image processing and analysis accelerated by high-performance computing: a systematic literature review

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