### 计算机代写|并行计算作业代写Parallel Computing代考|Distribute Arrays and Run SPMD

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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等楖率论
• Advanced Mathematical Statistics 高等数理统计学
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

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

The workers in a parallel pool communicate with each other, so you can distribute an array among the workers. Each worker contains part of the array, and all the workers are aware of which portion of the array each worker has.
Use the distributed function to distribute an array among the workers:
$M=$ magic(4) \&o a 4-by-4 magic square in the client workspace
$M M=$ distributed $(M)$
Now MM is a distributed array, equivalent to $\mathrm{M}$, and you can manipulate or access its elements in the same way as any other array.
$M 2=2 * M M ;$ क M2 is also distributed, calculation performed on workers
$x=M 2(1,1)$ \& $x$ on the client is set to first element of M2.

## 计算机代写|并行计算作业代写Parallel Computing代考|Composites

Following an spmd statement, in the client context, the values from the block are accessible, even though the data is actually stored on the workers. On the client, these variables are called Composite objects. Each element of a composite is a symbol referencing the value (data) on a worker in the pool. Note that because a variable might not be defined on every worker, a Composite might have undefined elements.
Continuing with the example from above, on the client, the Composite R has one element for each worker:
$X=R{3}$; set $X$ to the value of $R$ from worker 3 .
The line above retrieves the data from worker 3 to assign the value of $X$. The following code sends data to worker 3 :
$X=X+2$;
$R{3}=X$; is Send the value of $X$ from the client to worker $3 .$
If the parallel pool remains open between spmd statements and the same workers are used, the data on each worker persists from one spmd statement to another.
spmd
$R=R+$ labindex $\quad$ os Use values of $R$ from previous spmd.
end

A typical use for spmd is to run the same code on a number of workers, each of which accesses a different set of data. For example:
spmd
INP $=\operatorname{load}([‘$ somedatafile’ num2str(labindex) ‘ mat’ $]) ;$
RES = somefun (INP)
end
Then the values of RES on the workers are accessible from the client as RES 1$}$ from worker 1 , RES ${2}$ from worker 2 , etc.
There are two forms of indexing a Composite, comparable to indexing a cell array:

• AA ${n}$ returns the values of AA from worker $n$.
• AA $(n)$ returns a cell array of the content of AA from worker $n$.
Although data persists on the workers from one spmd block to another as long as the parallel pool remains open, data does not persist from one instance of a parallel pool to another. That is, if the pool is deleted and a new one created, all data from the first pool is lost.
For more information about using distributed arrays, spmd, and Composites, see “Distributed Arrays”.

## 计算机代写|并行计算作业代写Parallel Computing代考|What Is Parallel Computing

Parallel computing allows you to carry out many calculations simultaneously. Large problems can often be split into smaller ones, which are then solved at the same time.
The main reasons to consider parallel computing are to

• Save time by distributing tasks and executing these simultaneously
• Solve big data problems by distributing data
• Take advantage of your desktop computer resources and scale up to clusters and cloud computing With Parallel Computing Toolbox, you can
• Accelerate your code using interactive parallel computing tools, such as parfor and parfeval
• Scale up your computation using interactive Big Data processing tools, such as distributed, tall, datastore, and mapreduce
• Use gpuArray to speed up your calculation on the GPU of your computer
• Use batch to offload your calculation to computer clusters or cloud computing facilities
Here are some useful Parallel Computing concepts:
• Node: standalone computer, containing one or more CPUs / GPUs. Nodes are networked to form a cluster or supercomputer
• Thread: smallest set of instructions that can be managed independently by a scheduler. On a GPU, multiprocessor or multicore system, multiple threads can be executed simultaneously (multithreading)
• Batch: off-load execution of a functional script to run in the background
• Scalability: increase in parallel speedup with the addition of more resources
What tools do MATLAB and Parallel Computing Toolbox offer?
• MATLAB workers: MATLAB computational engines that run in the background without a graphical desktop. You use functions in the Parallel Computing Toolbox to automatically divide tasks and assign them to these workers to execute the computations in parallel. You can run local workers to take advantage of all the cores in your multicore desktop computer. You can also scale up to run your workers on a cluster of machines, using the MATLAB Parallel Server. The MATLAB session you interact with is known as the MATLAB client. The client instructs the workers with parallel language functions.
• Parallel pool: a parallel pool of MATLAB workers created using parpool or functions with automatic parallel support. By default, parallel language functions automatically create a parallel pool for you when necessary. To learn more, see “Run Code on Parallel Pools” on page 2-56.
For the default local profile, the default number of workers is one per physical CPU core using a single computational thread. This is because even though each physical core can have several virtual cores, the virtual cores share some resources, typically including a shared floating point unit (FPU). Most MATLAB computations use this unit because they are double-precision floating point. Restricting to one worker per physical core ensures that each worker has exclusive access to a floating point unit, which generally optimizes performance of computational code. If your code is not computationally intensive, for example, it is input/output (I/O) intensive, then consider using up to two workers per physical core. Running too many workers on too few resources may impact performance and stability of your machine.

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

X=米2(1,1)\&X在客户端上设置为 M2 的第一个元素。

## 计算机代写|并行计算作业代写Parallel Computing代考|Composites

X=R3; 放X的价值R从工人 3 。

X=X+2;
R3=X; 是发送的值X从客户到工人3.

spmd
R=R+实验室索引os 使用值R从以前的spmd。

spmd 的一个典型用途是在多个工作人员上运行相同的代码，每个工作人员访问一组不同的数据。例如：
spmd
INP=加载⁡([‘somedatafile’num2str（labindex）’垫子’]);
RES = somefun (INP)
end

• AAn从工人返回 AA 的值n.
• AA(n)从工作人员返回 AA 内容的元胞数组n.
尽管只要并行池保持打开状态，数据就会从一个 spmd 块持续到另一个 spmd 块，但数据不会从并行池的一个实例持续到另一个实例。也就是说，如果删除池并创建新池，则第一个池中的所有数据都将丢失。
有关使用分布式数组、spmd 和 Composites 的更多信息，请参阅“分布式数组”。

## 计算机代写|并行计算作业代写Parallel Computing代考|What Is Parallel Computing

• 通过分配任务并同时执行这些任务来节省时间
• 通过分发数据解决大数据问题
• 利用您的桌面计算机资源并扩展到集群和云计算使用 Parallel Computing Toolbox，您可以
• 使用交互式并行计算工具（例如 parfor 和 parfeval）加速您的代码
• 使用分布式、高、数据存储和 mapreduce 等交互式大数据处理工具扩展您的计算
• 使用 gpuArray 加速计算机 GPU 上的计算
• 使用批处理将您的计算卸载到计算机集群或云计算设施
以下是一些有用的并行计算概念：
• 节点：独立计算机，包含一个或多个 CPU/GPU。节点联网形成集群或超级计算机
• 线程：可以由调度程序独立管理的最小指令集。在 GPU、多处理器或多核系统上，可以同时执行多个线程（多线程）
• 批处理：卸载执行功能脚本以在后台运行
• 可扩展性：通过添加更多资源来提高并行加速
MATLAB 和 Parallel Computing Toolbox 提供哪些工具？
• MATLAB worker：无需图形桌面即可在后台运行的 MATLAB 计算引擎。您可以使用 Parallel Computing Toolbox 中的函数来自动划分任务并将它们分配给这些工作人员以并行执行计算。您可以运行本地工作程序以利用多核台式计算机中的所有内核。您还可以使用 MATLAB Parallel Server 扩大规模以在机器集群上运行您的工作器。您与之交互的 MATLAB 会话称为 MATLAB 客户端。客户端用并行语言功能指导工人。
• 并行池：使用 parpool 或具有自动并行支持的函数创建的 MATLAB 工作程序并行池。默认情况下，并行语言函数会在必要时自动为您创建并行池。要了解更多信息，请参阅第 2-56 页的“在并行池上运行代码”。
对于默认本地配置文件，默认工作人员数量是使用单个计算线程的每个物理 CPU 内核一个。这是因为即使每个物理内核都可以有多个虚拟内核，但这些虚拟内核共享一些资源，通常包括共享浮点单元 (FPU)。大多数 MATLAB 计算都使用这个单位，因为它们是双精度浮点数。每个物理内核限制为一个工作人员可确保每个工作人员对浮点单元具有独占访问权限，这通常可以优化计算代码的性能。如果您的代码不是计算密集型的，例如，它是输入/输出 (I/O) 密集型的，则考虑每个物理内核最多使用两个工作器。在太少的资源上运行太多的工作人员可能会影响机器的性能和稳定性。

## 有限元方法代写

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## MATLAB代写

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