### 计算机代写|C++作业代写C++代考|Achieving Parallelism

C++ 是一种高级语言，它是由Bjarne Stroustrup 于1979 年在贝尔实验室开始设计开发的。 C++ 进一步扩充和完善了C 语言，是一种面向对象的程序设计语言。 C++ 可运行于多种平台上，如Windows、MAC 操作系统以及UNIX 的各种版本。

statistics-lab™ 为您的留学生涯保驾护航 在代写C++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C++代写方面经验极为丰富，各种代写C++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|C++作业代写C++代考|Achieving Parallelism

Coordinating people around the job of preparing and mailing the envelopes is easily expressed by the following two conceptual steps:

1. Assign people to tasks (and feel free to move them around to balance the workload).
2. Start with one person on each of the six tasks but be willing to split up a given task so that two or more people can work on it together.
The six tasks are folding, stuffing, sealing, addressing, stamping, and mailing. We also have six people (resources) to help with the work. That is exactly how TBB works best: we define tasks and data at a level we can explain and then split or combine data to match up with resources available to do the work.
The first step in writing a parallel program is to consider where the parallelism is. Many textbooks wrestle with task and data parallelism as though there were a clear choice. TBB allows any combination of the two that we express. If we are lucky, our program will have an abundant amount of data parallelism available for us to exploit. To simplify this work, TBB requires only that we specify tasks and how to split them. For a completely data-parallel task, in TBB we will define one task to which we give all the data. That task will then be split up automatically

to use the available hardware parallelism. The implicit synchronization (as opposed to synchronization we directly ask for with coding) will often eliminate the need for using locks to achieve synchronization. Referring back to our enemies list, and the fact that we hate locks, the implicit synchronization is a good thing. What do we mean by “implicit” synchronization? Usually, all we are saying is that synchronization occurred but we did not explicitly code a synchronization. At first, this should seem like a “cheat.” After all, synchronization still happened – and someone had to ask for it! In a sense, we are counting on these implicit synchronizations being more carefully planned and implemented. The more we can use the standard methods of TBB, and the less we explicitly write our own locking code, the better off we will be – in general.
By letting TBB manage the work, we hand over the responsibility for splitting up the work and synchronizing when needed. The synchronization done by the library for us, which we call implicit synchronization, in turn often eliminates the need for an explicit coding for synchronization (see Chapter 5 ).

We strongly suggest starting there, and only venturing into explicit synchronization (Chapter 5 ) when absolutely necessary or beneficial. We can say, from experience, even when such things seem to be necessary – they are not. You’ve been warned. If you are like us, you’ll ignore the warning occasionally and get burned. We have.

People have been exploring decomposition for decades, and some patterns have emerged. We’ll cover this more later when we discuss design patterns for parallel programming.

## 计算机代写|C++作业代写C++代考|Terminology: Scaling and Speedup

The scalability of a program is a measure of how much speedup the program gets as we add more computing capabilities. Speedup is the ratio of the time it takes to run a program without parallelism vs. the time it takes to run in parallel. A speedup of $4 \times$ indicates that the parallel program runs in a quarter of the time of the serial program. An example would be a serial program that takes 100 seconds to run on a one-processor machine and 25 seconds to run on a quad-core machine.

As a goal, we would expect that our program running on two processor cores should run faster than our program running on one processor core. Likewise, running on four processor cores should be faster than running on two cores.
Any program will have a point of diminishing returns for adding parallelism. It is not uncommon for performance to even drop, instead of simply leveling off, if we force the use of too many compute resources. The granularity at which we should stop subdividing a problem can be expressed as a grain size. TBB uses a notion of grain size to help limit the splitting of data to a reasonable level to avoid this problem of dropping in performance. Grain size is generally determined automatically, by an automatic partitioner within TBB, using a combination of heuristics for an initial guess and dynamic refinements as execution progresses. However, it is possible to explicitly manipulate the grain size settings if we want to do so. We will not encourage this in this book, because we seldom will do better in performance with explicit specifications than the automatic partitioner in TBB, it tends to be somewhat machine specific, and therefore explicitly setting grain size reduces performance portability.

As Thinking Parallel becomes intuitive, structuring problems to scale will become second nature.

## 计算机代写|C++作业代写C++代考|Amdahl’s Law

Renowned computer architect, Gene Amdahl, made observations regarding the maximum improvement to a computer system that can be expected when only a portion of the system is improved. His observations in 1967 have come to be known as Amdahl’s Law. It tells us that if we speed up everything in a program by $2 x$, we can expect the

resulting program to run $2 \times$ faster. However, if we improve the performance of only $2 / 5$ th of the program by $2 \times$, the overall system improves only by $1.25 \times$.
Amdahl’s Law is easy to visualize. Imagine a program, with five equal parts, that runs in 500 seconds, as shown in Figure P-10. If we can speed up two of the parts by $2 \times$ and $4 \times$, as shown in Figure P-11, the 500 seconds are reduced to only 400 (1.25 × speedup) and 350 seconds (1.4× speedup), respectively. More and more, we are seeing the limitations of the portions that are not speeding up through parallelism. No matter how many processor cores are available, the serial portions create a barrier at 300 seconds that will not be broken (see Figure P-12) leaving us with only $1.7 \times$ speedup. If we are limited to parallel programming in only $2 / 5$ th of our execution time, we can never get more than a $1.7 \times$ boost in performance！

## 计算机代写|C++作业代写C++代考|Achieving Parallelism

1. 将人员分配给任务（并随意调动他们以平衡工作量）。
2. 从一个人开始处理六项任务中的每一项，但愿意将给定的任务分开，以便两个或更多人可以一起工作。
这六项任务是折叠、填充、密封、寻址、盖章和邮寄。我们还有六个人（资源）来帮助工作。这正是 TBB 的最佳工作方式：我们在可以解释的级别定义任务和数据，然后拆分或组合数据以匹配可用于完成工作的资源。
编写并行程序的第一步是考虑并行性在哪里。许多教科书都在与任务和数据并行性作斗争，好像有一个明确的选择。TBB 允许我们表达的两者的任意组合。如果幸运的话，我们的程序将有大量的数据并行性可供我们利用。为了简化这项工作，TBB 只需要我们指定任务以及如何拆分它们。对于完全数据并行的任务，在 TBB 中，我们将定义一个任务，我们将向其提供所有数据。然后该任务将自动拆分

## 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。