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).
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The author of the dataset does not provide reasons for the very sharp increase in collected data between 2004 and 2005.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Krstova, A., Gusev, M., Zdraveski, V. (2019). GPU Extended Stock Market Software Architecture. In: Poulkov, V. (eds) Future Access Enablers for Ubiquitous and Intelligent Infrastructures. FABULOUS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-030-23976-3_34
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DOI: https://doi.org/10.1007/978-3-030-23976-3_34
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