An Execution Time Comparison of Parallel Computing Algorithms for Solving Heat Equation

Autor:	Zineb Hidila, Mohammed Mestari, Soumia Chokri, Sohaib Baroud, Safa Belhaous
Rok vydání:	2020
Předmět:	CUDA Partial differential equation business.industry Deep learning Finite difference method Heat equation Natural phenomenon Artificial intelligence Parallel computing Load balancing (computing) business Execution time
Zdroj:	Communications in Computer and Information Science ISBN: 9783030451820 SADASC
DOI:	10.1007/978-3-030-45183-7_22
Popis:	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).
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::e62258abdf7af30a481230efa0fc653f https://doi.org/10.1007/978-3-030-45183-7_22 Zobrazit plný text záznamu