Abstract
Parallel Computing contributes significantly to most disciplines for solving several scientific problems such as partial differential equations (PDEs), load balancing, and deep learning. The primary characteristic of parallelism is its ability to ameliorate performance on many different sets of computers. Consequently, many researchers are continually expending their efforts to produce efficient parallel solutions for various problems such as heat equation. Heat equation is a natural phenomenon used in many fields like mathematics and physics. Usually, its associated model is defined by a set of partial differential equations (PDEs). This paper is primarily aimed at showing two parallel programs for solving the heat equation which has been discrete-sized using the finite difference method (FDM). These programs have been implemented through different parallel platforms such as SkelGIS and Compute Unified Device Architecture (CUDA).
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Acknowledgment
This project has received funding from:
\(\bullet \) The European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 777720.
\(\bullet \) The National Center for Scientific and Technical Research (NCSTR - Morocco) grant agreement No. 10UH2C2017.
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Belhaous, S., Hidila, Z., Baroud, S., Chokri, S., Mestari, M. (2020). An Execution Time Comparison of Parallel Computing Algorithms for Solving Heat Equation. In: Hamlich, M., Bellatreche, L., Mondal, A., Ordonez, C. (eds) Smart Applications and Data Analysis. SADASC 2020. Communications in Computer and Information Science, vol 1207. Springer, Cham. https://doi.org/10.1007/978-3-030-45183-7_22
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