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Future Access Enablers for Ubiquitous and Intelligent Infrastructures. 4th EAI International Conference, FABULOUS 2019, Sofia, Bulgaria, March 28-29, 2019, Proceedings

Research Article

GPU Extended Stock Market Software Architecture

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  • @INPROCEEDINGS{10.1007/978-3-030-23976-3_34,
        author={Alisa Krstova and Marjan Gusev and Vladimir Zdraveski},
        title={GPU Extended Stock Market Software Architecture},
        proceedings={Future Access Enablers for Ubiquitous and Intelligent Infrastructures. 4th EAI International Conference, FABULOUS 2019, Sofia, Bulgaria, March 28-29, 2019, Proceedings},
        proceedings_a={FABULOUS},
        year={2019},
        month={9},
        keywords={Stock market GPU Parallel programming CUDA},
        doi={10.1007/978-3-030-23976-3_34}
    }
    
  • Alisa Krstova
    Marjan Gusev
    Vladimir Zdraveski
    Year: 2019
    GPU Extended Stock Market Software Architecture
    FABULOUS
    Springer
    DOI: 10.1007/978-3-030-23976-3_34
Alisa Krstova1,*, Marjan Gusev1, Vladimir Zdraveski1
  • 1: University Ss. Cyril and Methodius
*Contact email: krstova.alisa@gmail.com

Abstract

We propose a stock market software architecture extended by a graphics processing unit, which employs parallel programming paradigm techniques to optimize long-running tasks like computing daily trends and performing statistical analysis of stock market data in real-time. The system uses the ability of Nvidia’s CUDA parallel computation application programming interface (API) to integrate with traditional web development frameworks. The web application offers extensive statistics and stocks’ information which is periodically recomputed through scheduled batch jobs or calculated in real-time. To illustrate the advantages of using many-core programming, we explore several use-cases and evaluate the improvement in performance and speedup obtained in comparison to the traditional approach of executing long-running jobs on a central processing unit (CPU).

Keywords
Stock market GPU Parallel programming CUDA
Published
2019-09-17
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-23976-3_34
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