### 计算机代写|并行计算作业代写Parallel Computing代考|Create and Use Distributed Arrays

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## 计算机代写|并行计算作业代写Parallel Computing代考|Create and Use Distributed Arrays

If your data is currently in the memory of your local machine, you can use the distributed function to distribute an existing array from the client workspace to the workers of a parallel pool.
Distributed arrays use the combined memory of multiple workers in a parallel pool to store the elements of an array. For alternative ways of partitioning data, see “Distributing Arrays to Parallel Workers” on page 4-11. You operate on the entire array as a single entity, however, workers operate only on their part of the array, and automatically transfer data between themselves when necessary. You can use distributed arrays to scale up your big data computation. Consider distributed arrays when you have access to a cluster, as you can combine the memory of multiple machines in your cluster.

A distributed array is a single variable, split over multiple workers in your parallel pool. You can work with this variable as one single entity, without having to worry about its distributed nature. Explore the functionalities available for distributed arrays in the Parallel Computing Toolbox: “Run MATLAB Functions with Distributed Arrays” on page 5-19.

When you create a distributed array, you cannot control the details of the distribution. On the other hand, codistributed arrays allow you to control all aspects of distribution, including dimensions and partitions. In the following, you learn how to create both distributed and codistributed arrays.

## 计算机代写|并行计算作业代写Parallel Computing代考|Creating Distributed Arrays

You can create a distributed array in different ways:

• Use the distributed function to distribute an existing array from the client workspace to the workers of a parallel pool.
• You can directly construct a distributed array on the workers. You do not need to first create the array in the client, so that client workspace memory requirements are reduced. The functions available include eye ( , ‘distributed’ ), rand ( , distributed’), etc. For a full list, see the distributed object reference page.
• Create a codistributed array inside an spmd statement, see “Single Program Multiple Data (spmd) $”$ on page 1-12. Then access it as a distributed array outside the spmd statement. This lets you use distribution schemes other than the default.
In this example, you create an array in the client workspace, then turn it into a distributed array:
parpool(‘Local’, 4) \& Create pool
$A=\operatorname{magic}(4) ; \quad$ \% Create magic 4 -by-4 matrix
$B=$ distributed $(A)$; is Distribute to the workers
$\begin{array}{ll}\text { parpool(‘local’, } 4) & \text { \% Create pool } \ \mathrm{A}=\text { magic }(4) ; & \text { Create magic } 4 \text {-by-4 matrix } \ \mathrm{B}=\text { distributed }(\mathrm{A}) \text {; \% Distribute to the workers } \ \mathrm{B} & \text { \% View results in client. } \ \text { whos } & \text { \& } \mathrm{B} \text { is a distributed array here. } \ \text { delete }(\mathrm{gcp}) & \text { \% Stop pool }\end{array}$
B b View results in client.
whos delete(gcp) is a distributed array here.
delete $(g \mathrm{cp})$ \% Stop pool
You have createdB as a distributed array, split over the workers in your parallel pool. This is shown in the figure.

## 计算机代写|并行计算作业代写Parallel Computing代考|Creating Codistributed Arrays

Unlike distributed arrays, codist ributed arrays allow you to control all aspects of distribution including dimensions and partitions. You can create a codistributed array in different ways:

• “Partitioning a Larger Array” on page 5-6 – Start with a large array that is replicated on all workers, and partition it so that the pieces are distributed across the workers. This is most usefu when you have sufficient memory to store the initial replicated array.
• “Building from Smaller Arrays” on page 5-6 – Start with smaller replicated arrays stored on each worker, and combine them so that each array becomes a segment of a larger codistributed array. This method reduces memory requirements as it lets you build a codistributed array from smaller pieces.
• “Using MATLAB Constructor Functions” on page 5-7 – Use any of the MATLAB constructor functions like rand or zeros with a codistributor object argument. These functions offer a quick means of constructing a codistributed array of any size in just one step.

In this example, you create a codistributed array inside an spmd statement, using a nondefault distribution scheme. First, define 1-D distribution along the third dimension, with 4 parts on worker 1 , and 12 parts on worker 2 . Then create a 3-by-3-by-16 array of zeros.
parpool (‘local’,2) \& Create pool
spmd
codist = codistributorld $(3,[4,12]) ;$
$Z$ = zeros $(3,3,16$, codist $)$
$Z=Z+$ labindex;
end
$Z \quad$ of View results in client.
whos \& $Z$ is a distributed array here.
delete $(g c p)$ \& Stop pool
For more details on codistributed arrays, see “Working with Codistributed Arrays” on page $5-4$.

## 计算机代写|并行计算作业代写Parallel Computing代考|Creating Distributed Arrays

• 使用分布式函数将现有数组从客户端工作区分发到并行池的工作人员。
• 你可以直接在worker上构造一个分布式数组。您无需先在客户端中创建数组，从而减少客户端工作区内存需求。可用的函数包括 eye ( , ‘distributed’ ), rand ( ,distributed’) 等。完整列表请参见分布式对象参考页面。
• 在 spmd 语句中创建一个 codistributed 数组，请参阅“单程序多数据 (spmd)”第 1-12 页。然后在 spmd 语句之外将其作为分布式数组访问。这使您可以使用默认分配方案以外的分配方案。
在此示例中，您在客户端工作区中创建一个数组，然后将其转换为分布式数组：
parpool(‘Local’, 4) \& Create pool
一种=魔法⁡(4);\% 创建魔法 4 × 4 矩阵
乙=分散式(一种); 是分发给工人
parpool（’本地’， 4) \% 创建池  一种= 魔法 (4); 创造魔法 4-by-4 矩阵  乙= 分散式 (一种); \% 分发给工人  乙 \% 在客户端查看结果。   谁是  \& 乙 这里是一个分布式数组。   删除 (GCp) \% 停止池
B b 在客户端查看结果。
whos delete(gcp) 在这里是一个分布式数组。
删除(GCp)\% 停止池
您已将 B 创建为分布式数组，在并行池中的工作人员上进行拆分。如图所示。

## 计算机代写|并行计算作业代写Parallel Computing代考|Creating Codistributed Arrays

• “对更大的数组进行分区”（第 5-6 页） – 从一个在所有工作程序上复制的大型数组开始，然后对它进行分区，以便将片段分布在工作程序中。当您有足够的内存来存储初始复制数组时，这是最有用的。
• “从较小的数组构建”（第 5-6 页）——从存储在每个 worker 上的较小的复制数组开始，然后将它们组合起来，使每个数组成为更大的协同分布式数组的一部分。这种方法减少了内存需求，因为它允许您从较小的部分构建一个共同分布的数组。
• “使用 MATLAB 构造函数”（第 5-7 页） – 将任何 MATLAB 构造函数（如 rand 或 zeros）与 codistributor 对象参数一起使用。这些函数提供了一种快速的方法，只需一步即可构建任意大小的协同分布式数组。

parpool (‘local’,2) \& 创建池
spmd
codist = codistributorld(3,[4,12]);

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。