### 计算机代写|并行计算作业代写Parallel Computing代考|Decide When to Use parfor

statistics-lab™ 为您的留学生涯保驾护航 在代写并行计算Parallel Computing方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写并行计算Parallel Computing代写方面经验极为丰富，各种代写并行计算Parallel Computing相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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|parfor-Loops in MATLAB

A parfor-loop in MATLAB executes a series of statements in the loop body in parallel. The MATLAB client issues the parfor command and coordinates with MATLAB workers to execute the loop iterations in parallel on the workers in a parallel pool. The client sends the necessary data on which parfor operates to workers, where most of the computation is executed. The results are sent back to the client and assembled.
A parfor-loop can provide significantly better performance than its analogous for-loop, because several MATLAB workers can compute simultaneously on the same loop.

Each execution of the body of a parfor-loop is an iteration. MATLAB workers evaluate iterations in no particular order and independently of each other. Because each iteration is independent, there is no guarantee that the iterations are synchronized in any way, nor is there any need for this. If the number of workers is equal to the number of loop iterations, each worker performs one iteration of the loop. If there are more iterations than workers, some workers perform more than one loop iteration; in this case, a worker might receive multiple iterations at once to reduce communication time.

## 计算机代写|并行计算作业代写Parallel Computing代考|Deciding When to Use parfor

A parfor-loop can be useful if you have a slow for-loop. Consider parfor if you have:

• Some loop iterations that take a long time to execute. In this case, the workers can execute the long iterations simultancously. Male sure that the number of iterations cxcceds the number of workers. Otherwise, you will not use all workers available.
• Many loop iterations of a simple calculation, such as a Monte Carlo simulation or a parameter sweep. parfor divides the loop iterations into groups so that each worker executes some portion of the total number of iterations.
A parfor-loop might not be useful if you have:
• Code that has vectorized out the for-loops. Generally, if you want to make code run faster, first try to vectorize it. For details how to do this, see “Vectorization”. Vectorizing code allows you to benefit from the built-in parallelism provided by the multithreaded nature of many of the underlying MATLAB libraries. However, if you have vectorized code and you have access only to local workers, then parfor-loops may run slower than for-loops. Do not devectorize code to allow for parfor; in general, this solution does not work well.
• Loop iterations that take a short time to execute. In this case, parallel overhead dominates your calculation.

You cannot use a parfor-loop when an iteration in your loop depends on the results of other iterations. Each iteration must be independent of all others. For help dealing with independent loops, see “Ensure That parfor-Loop Iterations are Independent” on page $2-10$. The exception to this rule is to accumulate values in a loop using “Reduction Variables” on page $2-42$.
In deciding when to use parfor, consider parallel overhead. Parallel overhead includes the time required for communication, coordination and data transfer – sending and receiving data – from client to workers and back. If iteration evaluations are fast, this overhead could be a significant part of the total time. Consider two different types of loop iterations:

• for-loops with a computationally demanding task. These loops are generally good candidates for conversion into a parfor-loop, because the time needed for computation dominates the time required for data transfer.
• for-loops with a simple computational task. These loops generally do not benefit from conversion into a parfor-loop, because the time needed for data transfer is significant compared with the time needed for computation.

## 计算机代写|并行计算作业代写Parallel Computing代考|Example of parfor With High Parallel Overhead

In this example, you write a loop to create a simple sine wave. Replacing the for-loop with a parforloop does not speed up your calculation. This loop does not have a lot of iterations, it does not take long to execute and you do not notice an increase in execution speed. This example has a high parallel overhead and does not benefit from conversion into a parfor-loop.

1 Write a loop to create a sine wave. Use tic and toc to measure the time elapsed.
tic
$n=1024$;
$A=$ zeros $(n)$;
for $i=1: n$
$A(i,:)=(1: n) . * \sin (i * 2 * p i / 1024)$;
end
toc
Elapsed time is $0.012501$ seconds.
2 Replace the for-loop with a parfor-loop. Add ticBytes and tocBytes to measure how much data is transferred to and from the workers in the parallel pool.
tic
ticBytes (gcp) ;
$\mathrm{n}=1024$;
$A=\operatorname{zeros}(n)$;
parfor (i=1:n)
$A(i,:)=(1: n) . * \sin (i * 2 * p i / 1024)$;
end
tocBytes ( $g c p$ )
toc
3 Run the script on four workers and run the code again. Note that the first run is slower than the second run, because the parallel pool takes some time to start and make the code available to the workers. Note the data transfer and elapsed time for the second run.
\begin{tabular}{lll}
\cline { 1 } 13176 & & $2.0615 \mathrm{e}+06$ \
15188 & $2.0874 \mathrm{e}+06$ \
13176 & $2.4056 \mathrm{e}+06$ \
13176 & $1.8567 \mathrm{e}+06$ \
54716 & $8.4112 \mathrm{e}+06$
\end{tabular}
Elapsed time is $0.743855$ seconds.
Note that the elapsed time is much smaller for the serial for-loop than for the parfor-loop on four workers. In this case, you do not benefit from turning your for-loop into a parfor-loop. The reason is that the transfer of data is much greater than in the previous example, see “Example of parfor With Low Parallel Overhead” on page 2-3. In the current example, the parallel overhead dominates the computing time. Therefore the sine wave iteration does not benefit from convẻrsion intó ả parforr-lóóp.
This example illustrates why high parallel overhead calculations do not benefit from conversion into a parfor-loop. To learn more about speeding up your code, see “Convert for-Loops Into parfor-Loops” on page 2-7

## 计算机代写|并行计算作业代写Parallel Computing代考|parfor-Loops in MATLAB

MATLAB 中的 parfor 循环在循环体中并行执行一系列语句。MATLAB 客户端发出 parfor 命令并与 MATLAB 工作程序协调，以在并行池中的工作程序上并行执行循环迭代。客户端将 parfor 操作所需的数据发送给执行大部分计算的工作人员。结果被发送回客户端并组装。
parfor 循环可以提供比其类似的 for 循环更好的性能，因为多个 MATLAB worker 可以在同一个循环上同时进行计算。

parfor 循环体的每次执行都是一次迭代。MATLAB worker 以无特定顺序且彼此独立地评估迭代。因为每次迭代都是独立的，所以不能保证迭代以任何方式同步，也不需要这样做。如果 worker 的数量等于循环迭代的次数，则每个 worker 执行一次循环迭代。如果迭代次数多于worker，则一些worker执行不止一次循环迭代；在这种情况下，worker 可能会一次接收多个迭代以减少通信时间。

## 计算机代写|并行计算作业代写Parallel Computing代考|Deciding When to Use parfor

• 一些需要很长时间才能执行的循环迭代。在这种情况下，工作人员可以同时执行长迭代。男性确保迭代次数与工人人数一致。否则，您将不会使用所有可用的工人。
• 简单计算的许多循环迭代，例如蒙特卡罗模拟或参数扫描。parfor 将循环迭代分成组，以便每个工作人员执行迭代总数的一部分。
如果您有以下情况，则 parfor 循环可能没有用：
• 将 for 循环向量化的代码。一般来说，如果你想让代码运行得更快，首先尝试对其进行矢量化。有关如何执行此操作的详细信息，请参阅“矢量化”。向量化代码使您可以受益于许多底层 MATLAB 库的多线程特性所提供的内置并行性。但是，如果您有矢量化代码并且您只能访问本地工作人员，那么 parfor 循环可能比 for 循环运行得慢。不要去向量化代码以允许 parfor；通常，此解决方案效果不佳。
• 执行时间较短的循环迭代。在这种情况下，并行开销支配了您的计算。

• 具有计算要求高的任务的 for 循环。这些循环通常是转换为 parfor 循环的良好候选者，因为计算所需的时间支配了数据传输所需的时间。
• 具有简单计算任务的 for 循环。这些循环通常不会从转换为 parfor 循环中受益，因为与计算所需的时间相比，数据传输所需的时间是显着的。

## 计算机代写|并行计算作业代写Parallel Computing代考|Example of parfor With High Parallel Overhead

1 编写一个循环来创建一个正弦波。使用 tic 和 toc 来测量经过的时间。

n=1024;

end
toc

2 用 parfor 循环替换 for 循环。添加 ticBytes 和 tocBytes 来衡量有多少数据传入和传出并行池中的工作人员。
tic
ticBytes (gcp) ;
n=1024;

parfor (i=1:n)

tocBytes (GCp)
toc
3 在四个工人上运行脚本并再次运行代码。请注意，第一次运行比第二次运行慢，因为并行池需要一些时间来启动并使代码对工作人员可用。注意第二次运行的数据传输和经过的时间。
\ 开始 {tabular {{lll} BytesSentToworkers & & BytesReceived \ \ cline {1} 13176 & & $2.0615 \ mathrm {e} + 06$ \ 15188 & $2.0874 \ mathrm {e} + 06$ \ 13176 & $2.4056 \数学} + 06$ \ 13176 & $1.8567 \ mathrm {e} + 06$ \ 54716 & $8.4112 \ mathrm {e} + 06$ \ end {表格\ 开始 {tabular {{lll} BytesSentToworkers & & BytesReceived \ \ cline {1} 13176 & & $2.0615 \ mathrm {e} + 06$ \ 15188 & $2.0874 \ mathrm {e} + 06$ \ 13176 & $2.4056 \数学} + 06$ \ 13176 & $1.8567 \ mathrm {e} + 06$ \ 54716 & $8.4112 \ mathrm {e} + 06$ \ end {表格

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。