计算机代写|C++作业代写C++代考|Concurrent vs. Parallel

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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。

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  • Statistical Inference 统计推断
  • Statistical Computing 统计计算
  • Advanced Probability Theory 高等概率论
  • 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|C++作业代写C++代考|Concurrent vs. Parallel

计算机代写|C++作业代写C++代考|Concurrent vs. Parallel

It is worth noting that the terms concurrent and parallel are related, but subtly different. Concurrent simply means “happening during the same time span” whereas parallel is more specific and is taken to mean “happening at the same time (at least some of the time).” Concurrency is more like what a single person tries to do when multitasking, whereas parallel is akin to what multiple people can do together. Figure P-1 illustrates the concepts of concurrency vs. parallelism. When we create effective parallel programs, we are aiming to accomplish more than just concurrency. In general, speaking of concurrency will mean there is not an expectation for a great deal of activity to be truly parallel – which means that two workers are not necessarily getting more work done than one could in theory (see tasks A and B in Figure P-1). Since the work is not done sooner, concurrency does not improve the latency of a task (the delay to start a task). Using the term parallel conveys an expectation that we improve latency and throughput (work done in a given time). We explore this in more depth starting on page xxxv when we explore limits of parallelism and discuss the very important concepts of Amdahl’s Law.

计算机代写|C++作业代写C++代考|Enemies of Parallelism

Bearing in mind the enemies of parallel programming will help understand our advocacy for particular programming methods. Key parallel programming enemies include

  • Locks: In parallel programming, locks or mutual exclusion objects (mutexes) are used to provide a thread with exclusive access to a resource – blocking other threads from simultaneously accessing the same resource. Locks are the most common explicit way to ensure parallel tasks update shared data in a coordinated fashion (as opposed to allowing pure chaos). We hate locks because they serialize part of our programs, limiting scaling. The sentiment “we hate locks” is on our minds throughout the book. We hope to instill this mantra in you as well, without losing sight of when we must synchronize properly. Hence, a word of caution: we actually do love locks when they are needed, because without them disaster will strike. This love/hate relationship with locks needs to be understood.
  • Shared mutable state: Mutable is another word for “can be changed.” Shared mutable state happens any time we share data among multiple threads, and we allow it to change while being shared. Such sharing either reduces scaling when synchronization is needed and used correctly, or it leads to correctness issues (race conditions or deadlocks) when synchronization (e.g., a lock) is incorrectly applied. Realistically, we need shared mutable state when we write interesting applications. Thinking about careful handling of shared mutable state may be an easier way to understand the basis of our love/hate relationship with locks. In the end, we all end up “managing” shared mutable state and the mutual exclusion (including locks) to make it work as we wish.
  • Not “Thinking Parallel”: Use of clever bandages and patches will not make up for a poorly thought out strategy for scalable algorithms. Knowing where the parallelism is available, and how it can be

exploited, should be considered before implementation. Trying to add parallelism to an application, after it is written, is fraught with peril. Some preexisting code may shift to use parallelism relatively well, but most code will benefit from considerable rethinking of algorithms.

  • Forgetting that algorithms win: This may just be another way to say “Think Parallel.” The choice of algorithms has a profound effect on the scalability of applications. Our choice of algorithms determine how tasks can divide, data structures are accessed, and results are coalesced. The optimal algorithm is really the one which serves as the basis for optimal solution. An optimal solution is a combination of the appropriate algorithm, with the best matching parallel data structure, and the best way to schedule the computation over the data. The search for, and discovery of, algorithms which are better is seemingly unending for all of us as programmers. Now, as parallel programmers, we must add scalable to the definition of better for an algorithm.

计算机代写|C++作业代写C++代考|Terminology of Parallelism

The vocabulary of parallel programming is something we need to learn in order to converse with other parallel programmers. None of the concepts are particularly hard, but they are very important to internalize. A parallel programmer, like any programmer, spends years gaining a deep intuitive feel for their craft, despite the fundamentals being simple enough to explain.
We will discuss decomposition of work into parallel tasks, scaling terminology, correctness considerations, and the importance of locality due primarily to cache effects. When we think about our application, how do we find the parallelism?
At the highest level, parallelism exists either in the form of data to operate on in parallel, or in the form of tasks to execute in parallel. And they are not mutually exclusive. In a sense, all of the important parallelism is in data parallelism. Nevertheless, we will introduce both because it can be convenient to think of both. When we discuss scaling, and Amdahl’s Law, our intense bias to look for data parallelism will become more understandable.

计算机代写|C++作业代写C++代考|Concurrent vs. Parallel


计算机代写|C++作业代写C++代考|Concurrent vs. Parallel

值得注意的是,并发和并行这两个术语是相关的,但有细微的不同。并发只是意味着“在同一时间跨度内发生”,而并行更具体,被认为是指“同时发生(至少在某些时候)”。并发更像是一个人在多任务处理时尝试做的事情,而并行类似于多个人可以一起做的事情。图 P-1 说明了并发与并行的概念。当我们创建有效的并行程序时,我们的目标不仅仅是并发。一般来说,谈到并发意味着不期望大量活动真正并行——这意味着两名工作人员完成的工作不一定比理论上多(参见图 P 中的任务 A 和 B -1)。由于工作没有尽快完成,并发不会改善任务的延迟(启动任务的延迟)。使用术语并行表达了我们改善延迟和吞吐量(在给定时间内完成的工作)的期望。我们从第 xxxv 页开始更深入地探讨这一点,当时我们探讨了并行性的限制并讨论了阿姆达尔定律的非常重要的概念。

计算机代写|C++作业代写C++代考|Enemies of Parallelism


  • 锁:在并行编程中,锁或互斥对象(互斥体)用于为线程提供对资源的独占访问——阻止其他线程同时访问同一资源。锁是确保并行任务以协调的方式更新共享数据的最常见的显式方式(而不是允许纯粹的混乱)。我们讨厌锁,因为它们序列化了我们程序的一部分,限制了扩展。“我们讨厌锁”的情绪贯穿整本书。我们也希望将这个口头禅灌输给你,同时不要忽视我们何时必须正确同步。因此,请注意:我们实际上会在需要时使用爱锁,因为没有它们,灾难就会降临。需要了解这种与锁的爱/恨关系。
  • 共享可变状态:可变是“可以更改”的另一个词。每当我们在多个线程之间共享数据时,都会发生共享可变状态,并且我们允许它在共享时更改。这种共享要么在需要和正确使用同步时减少缩放,要么在不正确地应用同步(例如,锁)时导致正确性问题(竞争条件或死锁)。实际上,当我们编写有趣的应用程序时,我们需要共享可变状态。仔细考虑共享可变状态的处理可能是一种更容易理解我们对锁的爱/恨关系的基础的方法。最后,我们最终都会“管理”共享可变状态和互斥(包括锁),以使其按我们的意愿工作。
  • 不是“并行思考”:使用巧妙的绷带和补丁无法弥补可扩展算法的考虑不周的策略。了解并行性在哪里可用,以及如何实现


  • 忘记算法获胜:这可能只是“并行思考”的另一种说法。算法的选择对应用程序的可扩展性有着深远的影响。我们对算法的选择决定了如何划分任务、访问数据结构以及合并结果。最优算法实际上是作为最优解的基础的算法。最佳解决方案是适当的算法、最佳匹配的并行数据结构以及调度数据计算的最佳方式的组合。对于我们所有的程序员来说,寻找和发现更好的算法似乎是永无止境的。现在,作为并行程序员,我们必须将可扩展性添加到更好的算法的定义中。

计算机代写|C++作业代写C++代考|Terminology of Parallelism


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术语 广义线性模型(GLM)通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归,以及方差分析和方差分析(仅含固定效应)。



有限元是一种通用的数值方法,用于解决两个或三个空间变量的偏微分方程(即一些边界值问题)。为了解决一个问题,有限元将一个大系统细分为更小、更简单的部分,称为有限元。这是通过在空间维度上的特定空间离散化来实现的,它是通过构建对象的网格来实现的:用于求解的数值域,它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统,以模拟整个问题。然后,有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。





随机过程,是依赖于参数的一组随机变量的全体,参数通常是时间。 随机变量是随机现象的数量表现,其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值(如1秒,5分钟,12小时,7天,1年),因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中,往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录,以得到其自身发展的规律。


多元回归分析渐进(Multiple Regression Analysis Asymptotics)属于计量经济学领域,主要是一种数学上的统计分析方法,可以分析复杂情况下各影响因素的数学关系,在自然科学、社会和经济学等多个领域内应用广泛。


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。



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