Using CUDA technology to form ensembles of pseudo-random sequences and calculating their correlation functions

Shubin, D. N.; Lobov, E. M.

doi:10.1109/sinkhroinfo.2017.7997573

Cited by 3 publications

(2 citation statements)

References 1 publication

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“…The de Bruijn sequence [21] is a kind of pseudo-random sequence [22], which is in one-to-one correspondence with Euler cycles in the de Bruijn graph [23]. Given a t-sized alphabet A = {s 0 , s 1 , .…”

Section: De Bruijn Sequencementioning

confidence: 99%

Fast Format-Aware Fuzzing for Structured Input Applications

Chen

Zhu

et al. 2022

Applied Sciences

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Fuzzing is one of the most successful software testing techniques used to discover vulnerabilities in programs. Without seeds that fit the input format, existing runtime dependency recognition strategies are limited by incompleteness and high overhead. In this paper, for structured input applications, we propose a fast format-aware fuzzing approach to recognize dependencies from the specified input to the corresponding comparison instruction. We divided the dependencies into Input-to-State (I2S) and indirect dependencies. Our approach has the following advantages compared to existing works: (1) recognizing I2S dependencies more completely and swiftly using the input based on the de Bruijn sequence and its mapping structure; (2) obtaining indirect dependencies with a light dependency existence analysis on the input fragments. We implemented a fast format-aware fuzzing prototype, FFAFuzz, based on our method and evaluated FFAFuzz in real-world structured input applications. The evaluation results showed that FFAFuzz reduced the average time overhead by 76.49% while identifying more completely compared with Redqueen and by 89.10% compared with WEIZZ. FFAFuzz also achieved higher code coverage by 14.53% on average compared to WEIZZ.

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Section: De Bruijn Sequencementioning

confidence: 99%

Fast Format-Aware Fuzzing for Structured Input Applications

Chen

Zhu

et al. 2022

Applied Sciences

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“…The use of CUDA is not limited to solving the heat equation [14][15] [11] but several works that have used CUDA to solve other problems namely the parallel implementation of the hybrid intuitionistic fuzzy edge detection algorithm [23], the routing based on the neural network thanks to the parallelism exploit [24], the calculation of the correlation function of ensembles of Pseudo-random sequences formed automatically by CUDA program [25], and optimization of tasks that require massively parallel calculations to produce an effective implementation of the integral image algorithm [26].…”

Section: B Software Impactmentioning

confidence: 99%

Comparative Study of the Execution Time of Parallel Heat Equation on CPU and GPU

Belhaous¹,

Chokri²,

Baroud³

et al. 2021

JCOMSS

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Parallelization has become a universal technique for computing an intensive scientific simulation to shorten the execution time of complex problems. It consists of bringing together the power of several thousand processors to perform complex calculations at high speed. The choice of the runtime environment to execute parallel programs significantly influences the execution time. For this reason, this article aims to materialize the impact of computing architectures on the performance of parallel implementations. To better achieve this contribution, we have implemented the heat equation executed on CUDA platform and we have compared the results with those of SkelGIS implementation from the literature. Through the results of the experiments, we demonstrated that the execution time of the CUDA implementation on graphics processing unit (GPU) is almost 100X faster for very large meshes compared to the other implementations.

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Modification of Majority Processing of Synchronizing Information in MAC Multiple Access Protocols

Podoltsev

Azhmuhamedov

2020

2020 International Russian Automation Conference (RusAutoCon)

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Using CUDA technology to form ensembles of pseudo-random sequences and calculating their correlation functions

Cited by 3 publications

References 1 publication

Fast Format-Aware Fuzzing for Structured Input Applications

Fast Format-Aware Fuzzing for Structured Input Applications

Comparative Study of the Execution Time of Parallel Heat Equation on CPU and GPU

Modification of Majority Processing of Synchronizing Information in MAC Multiple Access Protocols

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