سیستم های چندهسته ای غیر متمرکز
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سیستم های چندهسته ای غیر متمرکز

عنوان فارسی مقاله: یک الگوریتم تحلیلی محاسبه محور برای سیستم های چندهسته ای غیر متمرکز
عنوان انگلیسی مقاله: An analytic computation-driven algorithm for Decentralized Multicore Systems
مجله/کنفرانس: سیستم های کامپیوتری نسل آینده-Future Generation Computer Systems
رشته های تحصیلی مرتبط: مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: الگوریتم ها و محاسبات، معماری سیستم های کامپیوتری
کلمات کلیدی فارسی: الگوریتم موازی، روش تجزیه دوتایی Adomian–Rach، سیستم های چندهسته ای غیر متمرکز، Adomian چند جمله ای
کلمات کلیدی انگلیسی: Parallel algorithm، Adomian–Rach double decomposition method، Adomian polynomials، Decentralized Multicore Systems
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master journals – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.future.2019.01.031
دانشگاه: Wenzhou Medical University, Wenzhou 325000, PR China
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 7.007 در سال 2018
شاخص H_index: 93 در سال 2019
شاخص SJR: 0.835 در سال 2018
شناسه ISSN: 0167-739X
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 10
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
کد محصول: E12063
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Key parallel algorithms


3- The application of the package AdomianPy


4- Discussions


5- Conclusion and future work


Acknowledgments


Appendix. The interface and usage of our package AdomianPy


References

نمونه متن انگلیسی مقاله

 Abstract


In the modern era, increasing numbers of cores per chip are applied for decentralized systems, but there is not any appropriate symbolic computation approach to construct multicore analytic approximation. Thus, it is essential to develop an efficient, simple and unified way for decentralized Adomian decomposition method to increase the potential speed of the multicore systems. In our paper, we present an innovative parallel algorithm of constructing analytic solutions for nonlinear differential system, which based on the Adomian–Rach double decomposition method and Rach’s Adomian polynomials. Based on our algorithm, we further developed a user-friendly Python software package to construct analytic approximations of initial or boundary value problems. Finally, the scope of validity of our Python software package is illustrated by several different types of nonlinear examples. The obtained results demonstrate the effectiveness of our package by compared with exact solution and numeric method, the characteristics of each class of Adomian polynomials and the efficiency of parallel algorithm with multicore processors. We emphasis that the super-linear speedup may happens for the duration of constructing approximate solutions. So, it can be considered as a promising alternative algorithm of decentralized Adomian decomposition method for solving nonlinear problems in science and engineering.


Introduction


A large number of enigmas in engineering, biology, economics and other disciplines, e.g. flow and heat transfer problem, the simulations of the immune system, control and optimization theory, bound price problem, are often modeled using a system of nonlinear problems [1–۵]. In particular, various kinds of decentralized systems, appeared in economics, medicine etc., are convenient and cost effective [6,7]. A wide range of analytic methods, like the Adomian decomposition method (ADM) [1,3], the perturbationincremental method [8], the variational iteration method [9], the homotopy perturbation method [10], etc., is a reliable and efficient technique to handle such problems. It should be mentioned that the ADM is among the most simple and effective analytic methods to construct approximations of nonlinear differential equations, and have been modified and improved by Adomian and his collaborator, like the Adomian–Rach double decomposition [1,11], etc. These modifications, in most cases, undoubtedly have provided higher accuracy and faster convergence in nonlinear differential equations [1,12]. Furthermore, the ADM, which have been proved that it works efficiently for a large number of nonlinear problems including fractional differential equations [13], even stochastic system [1], is easy to be implemented by various programming languages, such as Maple [13], Mathematics [14], etc.

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