### 计算机代写|C++作业代写C++代考|Terminology: Data Parallelism

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• 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|Terminology: Data Parallelism

Data parallelism (Figure $\mathrm{P}-3$ ) is easy to picture: take lots of data and apply the same transformation to each piece of the data. In Figure P-3, each letter in the data set is capitalized and becomes the corresponding uppercase letter. This simple example shows that given a data set and an operation that can be applied element by element, we can apply the same task in parallel to each element. Programmers writing code for supercomputers love this sort of problem and consider it so easy to do in parallel that it has been called embarrassingly parallel. A word of advice: if you have lots of data parallelism, do not be embarrassed – take advantage of it and be very happy. Consider i happy parallelism.

When comparing the effort to find work to do in parallel, an approach that focuses on data parallelism is limited by the amount of data we can grab to process. Approaches based on task parallelism alone are limited by the different task types we program. Whil both methods are valid and important, it is critical to find parallelism in the data that we process in order to have a truly scalable parallel program. Scalability means that our application can increase in performance as we add hardware (e.g., more processor cores) provided we have enough data. In the age of big data, it turns out that big data and parallel programming are made for each other. It seems that growth in data sizes is a reliable source of additional work. We will revisit this observation, a little later in this Preface, when we discuss Amdahl’s Law.

## 计算机代写|C++作业代写C++代考|Terminology: Pipelining

While task parallelism is harder to find than data parallelism, a specific type of task parallelism is worth highlighting: pipelining. In this kind of algorithm, many independent tasks need to be applied to a stream of data. Each item is processed by each stage, as shown by the letter A in (Figure P-4). A stream of data can be processed more quickly when we use a pipeline, because different items can pass through different stages at the same time, as shown in Figure P-5. In these examples, the time to get a result may not be faster (referred to as the latency measured as the time from input to output) but the throughput is greater because it is measured in terms of completions (output) per unit of time. Pipelines enable parallelism to increase throughput when compared with a sequential (serial) processing. A pipeline can also be more sophisticated: it can reroute data or skip steps for chosen items. TBB has specific support for simple pipelines (Chapter 2) and very complex pipelines (Chapter 3). Of course, each step in the pipeline can use data or task parallelism as well. The composability of TBB supports this seamlessly.

## 计算机代写|C++作业代写C++代考|Example of Exploiting Mixed Parallelism

Consider the task of folding, stuffing, sealing, addressing, stamping, and mailing letters. If we assemble a group of six people for the task of stuffing many envelopes, we can arrange each person to specialize in and perform their assigned task in a pipeline fashion (Figure P-6). This contrasts with data parallelism, where we divide up the supplies and give a batch of everything to each person (Figure P-7). Each person then does all the steps on their collection of materials.
Figure P- 7 is clearly the right choice if every person has to work in a different location far from each other. That is called coarse-grained parallelism because the interactions between the tasks are infrequent (they only come together to collect envelopes, then leave and do their task, including mailing). The other choice shown in Figure P-6 approximates what we call fine-grained parallelism because of the frequent interactions (every envelope is passed along to every worker in various steps of the operation).
Neither extreme tends to fit reality, although sometimes they may be close enough to be useful. In our example, it may turn out that addressing an envelope takes enough time to keep three people busy, whereas the first two steps and the last two steps require only one person on each pair of steps to keep up. Figure P-8 illustrates the steps with the corresponding size of the work to be done. We can conclude that if we assigned only one person to each step as we see done in Figure P-6, that we would be “starving” some people in this pipeline of work for things to do – they would be idle. You might say it would be hidden “underemployment.” Our solution, to achieve a reasonable balance in our pipeline (Figure P-9) is really a hybrid of data and task parallelism.

## 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。