### 电子工程代写|计算机系统原理代写Principles of Computer Systems代考|CSC110

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• 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

## 电子工程代写|计算机系统原理代写Principles of Computer Systems代考|Synchronous Vector Systems

Considerations on the level of machine instructions will be concluded with demonstration of action of a synchronous vector system, composed of very simple identical processors. Each of them executes only four instructions and has access to shared memory for data. It is assumed that their clocks, of identical frequencies, are precisely synchronized (or equivalently: the processes use a common clock). Such systems, composed of a great number of very simple processors, are capable of offering large computing power, when performing algorithms, where identical instructions (machine commands) in each processor are being executed simultaneously in one instruction execution cycle. Systems of such architecture are specialized for certain tasks, for instance, in computation with vectors or matrices. To illustrate such system, let us consider a simple example of adding vectors of four components (obviously the gain of such architecture is significant in case of vectors of large number of components). Given vectors:
\begin{aligned} &\mathrm{X}=(\mathrm{X}[1], \mathrm{X}[2], \mathrm{X}[3], \mathrm{X}[4])=(3,8,-2,6) \ &\mathrm{Y}=(\mathrm{Y}[1], \mathrm{Y}[2], \mathrm{Y}[3], \mathrm{Y}[4])=(5,-7,0,23) \end{aligned}
let us compute their sum:
$$\mathrm{Z}=\mathrm{X}+\mathrm{Y}=(\mathrm{Z}[1], \mathrm{Z}[2], \mathrm{Z}[3], \mathrm{Z}[4])=(8,1,-2,29)$$
Table $1.8$ shows the activity of of 4-processor vector system, computing the sum $\mathrm{X}+\mathrm{Y}$ and storing result in the vector $\mathrm{Z}$. Note that computation of sum of n-components vectors takes as much time as computing sum of two numbers $\mathrm{X}[i]+\mathrm{Y}[i](i=1, \ldots, n)$ : only 4 instructions are being executed, instead of at least 4 $(n+1)$, when performed by one processor. Replacing instruction of addition (AD) with multiplication (MU), leads to computing products of respective components, whose sum yields the inner product of vectors (summation of the products may be performed by an algorithm which would sum up all the products; such efficient algorithms are elaborated and easily found in the literature). Note that computation of the inner product of vectors is a basic activity of computation of the product of matrices, which is encountered in a number of problems, like solving linear equations systems, fast Fourier transform (FFT) and others. For this purpose, synchronous matrix architectures are devised, included in the supercomputers, as well as very fast, so-called systolic arrays, of very large integration scale (VLSI), worked out by Kung and Leiserson (1979), Petkov (Petkov 1992), for special tasks. Architectures like vector, matrix or others of regular interconnection structures between simple but numerous processing and memory units, acting synchronously, are sometimes referred to as massively parallel.

## 电子工程代写|计算机系统原理代写Principles of Computer Systems代考|Some Classifications of Computer Systems

Before we pass on, to presentation of fundamental features and functions of distributed systems, let us take a look at possible types of computer systems depicted in the following diagram (Fig. 1.10).

The reader will easily ascribe exemplary computer systems outlined in this chapter to some types shown in Fig. 1.10.

A classification based on different principle (i.e. on multiplicity of instruction streams and data streams) is the the so-called Flynn’s taxonomy (Flynn 1972):

• SISD (Single Instruction [stream] Single Data [stream])-traditional computers with one instruction stream
• SIMD (Single Instruction [stream] Multiple Data [stream])-systems with one nstruction stream and more than one stream of data
• MISD (Multiple Instruction [stream] Single Data [stream])-systems with more than one instruction stream and one data stream (do not exist)
• MIMD (Multiple Instruction [stream] Multiple Data [stream]) -systems with more than one instruction and more than one data stream

## 电子工程代写|计算机系统原理代写Principles of Computer Systems代考|Synchronous Vector Systems

$$\mathrm{X}=(\mathrm{X}[1], \mathrm{X}[2], \mathrm{X}[3], \mathrm{X}[4])=(3,8,-2,6) \quad \mathrm{Y}=(\mathrm{Y}[1], \mathrm{Y}[2], \mathrm{Y}[3], \mathrm{Y}[4])=(5,-7,0,23)$$

$$\mathrm{Z}=\mathrm{X}+\mathrm{Y}=(\mathrm{Z}[1], \mathrm{Z}[2], \mathrm{Z}[3], \mathrm{Z}[4])=(8,1,-2,29)$$

## 电子工程代写|计算机系统原理代写Principles of Computer Systems代考|Some Classifications of Computer Systems

• SISD (Single Instruction [stream] Single Data [stream])——传统的计算机只有一个指令流
• SIMD (Single Instruction [stream] Multiple Data [stream]) – 具有一个指令流和多个数据流的系统
• MISD（Multiple Instruction [stream] Single Data [stream]）——多于一个指令流和一个数据流的系统（不存在）
• MIMD (Multiple Instruction [stream] Multiple Data [stream]) – 具有多条指令和多条数据流的系统

## 广义线性模型代考

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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。