开发了在三种异构处理器上确定大维矩阵元素乘法最优划分形式的应用

Q3 Earth and Planetary Sciences
Y. Klyuyeva
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引用次数: 0

摘要

本文介绍了一个web应用程序的开发结果,该应用程序用于在三个抽象异构处理器之间进行乘法运算时寻找分割矩阵元素的最佳形式。本文研究了五类并行矩阵乘法算法:带屏障的串行通信、带屏障的并行通信、带重叠的串行通信、带重叠的并行通信、带交替的并行重叠。霍克尼模型用于估计算法的通信复杂度。这项工作使用了Ashley DeFlumere在她的工作[1]中确定的六种非矩形候选划分形状,这是应用在处理器之间重新分配矩阵元素的“推”技术的结果:方角、矩形角、方形矩形、块矩形、l形矩形、传统1D矩形。确定形式的最优性是基于作品[2,3]中提出的数学模型。使用编程语言Python和JavaScript、Django框架、pip包管理器和Ajax技术开发web应用程序。解决确定最优矩阵形状的问题将允许使用并行矩阵乘法对各种科学领域的计算能力资源进行有效规划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of the application for determination of the optimal partitioning form of large-dimensional matrix’s elements for multiplication on three heterogeneous processors
The article presents the results of the development of a web application for finding the optimal form of splitting matrix elements between three abstract heterogeneous processors when performing the operation of their multiplication. The paper considers five classes of parallel matrix multiplication algorithms: serial communication with a barrier, parallel communication with a barrier, serial communication with overlapping, parallel communication with overlapping, parallel overlapping with alternation. The Hockney model is used to estimate the communication complexity of the algorithms. The work uses six non-rectangular candidate partitioning shapes identified by Ashley DeFlumere in her work [1] as a result of applying the «push» technology of redistribution of matrix elements between the processors: Square Corner, Rectangle Corner, Square Rectangle, Block Rectangle, L-Rectangle, Traditional 1D Rectangular. Determination of the optimality of the form is made on the basis of mathematical models presented in the works [2,3]. The programming languages Python and JavaScript, the Django framework, the pip package manager, and Ajax technology were used to develop a web application. Solving the problem of determining the optimal matrix shape will allow for efficient planning of computing power resources for various scientific fields using parallel matrix multiplication.
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来源期刊
CiteScore
1.80
自引率
0.00%
发文量
83
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