树莓派超级计算机和科学编程指南

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"Raspberry Pi 超级计算和科学编程" 本书《Raspberry Pi 超级计算和科学编程》由 Ashwin Pajankar 撰写，出版社为 Apress，出版于 2017 年，ISBN 号为 978-1-4842-2878-4。本书的主要内容围绕 Raspberry Pi 超级计算和科学编程展开，涵盖了 MPI4PY、NumPy 和 SciPy 等技术的应用。首先，Raspberry Pi 是一种基于 Linux 的单板计算机，具有低成本、低功耗和高性能的特点。这种小型计算机广泛应用于robots、自动化、物联网等领域。Raspberry Pi 的出现 революизировало了计算机科学和工程领域，提供了一个经济实惠的解决方案，使得更多的人能够参与科学研究和开发。在科学计算领域，Raspberry Pi 可以作为一个低成本的超级计算机，用于科学模拟、数据分析和机器学习等领域。MPI4PY 是一种基于 MPI（Message Passing Interface）的 Python 库，用于并行计算和分布式计算。NumPy 和 SciPy 是两个流行的 Python 库，用于科学计算和数据分析。这些技术的结合可以实现高性能的科学计算和数据分析。本书还涵盖了 Raspberry Pi 在科学编程中的应用，包括数据采集、数据分析、机器学习和可视化等领域。作者 Ashwin Pajankar 详细介绍了如何使用 Raspberry Pi 和相关技术来实现科学编程和数据分析。此外，本书还讨论了 Raspberry Pi 在超级计算和科学编程中的应用场景，包括气候模拟、金融分析、生物信息学和计算机视觉等领域。作者还提供了一些实践项目和案例，帮助读者更好地理解和应用这些技术。《Raspberry Pi 超级计算和科学编程》是一本非常实用的书籍，适合科学家、工程师和开发者阅读。该书提供了一个全面的介绍，涵盖了 Raspberry Pi 和相关技术的应用场景和实践经验。知识点： * Raspberry Pi 的基本概念和应用场景 * MPI4PY 的基本概念和应用场景 * NumPy 和 SciPy 的基本概念和应用场景 * Raspberry Pi 在科学编程中的应用场景 * 超级计算和科学编程的基本概念和应用场景 * 数据采集、数据分析、机器学习和可视化等技术的应用场景标签：Raspberry Pi、超级计算、科学编程、MPI4PY、NumPy、SciPy、Python

■ about the teChniCal RevieweR

xvi

also launched at ICRA 2016, and is one of the bestselling books on ROS. The third book is on

ROS Robotics Projects (www.rosrobots.com), which is expected to release on April 2017.

He also reviewed one book about Robot Operating System called Effective Robotics

Programming Using ROS (www.packtpub.com/hardware-and-creative/effective-

robotics-programming-ros-third-edition)

Lentin and his team were also winners of the HRATC 2016 challenge conducted as a

part of ICRA 2016. He was also a finalist in the ICRA 2015 challenge, HRATC

(www.icra2016.org/conference/challenges/).

xix

Introduction

“I’m sorry, buddy, you’ve got a C in parallel programming,” my classmate said to me. It

was my second semester at IIIT Hyderabad, where I was pursuing my graduate studies

towards an MTech in computer science and engineering. To be very honest, I was really

not expecting an A in parallel programming. However, I was not expecting a C either. Like

most of the folks in my class, I was expecting a B or a B- in the worst case. As I mentioned

earlier, it was my second semester and I was getting used to boring and rigorous

academics at the Institute. I was also getting used to seeing C and C- grades on my

scorecard. I was really starting to wonder if I had what it took to pursue graduate studies

at the coveted and hallowed Institute. Little did I know that the worst was yet to come,

because exactly one year after that, I saw two Ds on my scorecard.

I wondered if there was a way to make scary subjects like parallel programming more

interesting and appealing to undergrad and graduate students. I vividly remember that

our prof for the course mentioned that the US Air Force created a cluster of PS3s (http://

phys.org/news/2010-12-air-playstation-3s-supercomputer.html). You can also

check the Wikipedia page (https://en.wikipedia.org/wiki/PlayStation_3_cluster)

for the same. I fancied owning a small-scale cluster like that. However, the biggest hurdle

was and still is the cost associated with individual unit. A PS3 costs around 250 USD and

a PS2 costs 100 USD. Moreover, it is a really tedious task to install Linux OS on the PS2

and PS3. I have nothing against Sony or the PS2/PS3, as Playstations are excellent gaming

consoles, but I wanted something cheaper and simpler to build the cluster. So I shelved

my plans to build a cluster for the time being and got engrossed in academics at the

Institute.

Fast forward two years: I saw a bunch of kids booting up a strange-looking PCB

(printed circuit board) with Linux and I literally jumped with excitement. When I asked

them what it was, they said, “Raspberry Pi.” This is how I was introduced to Raspberry Pi.

The occasion was a Hackathon organized at my former workplace for specially abled kids.

When I saw those lovely and creative kids using a little computer, I immediately decided

to explore it, and after six months my first book on Raspberry Pi (www.packtpub.com/

hardware-and-creative/raspberry-pi-computer-vision-programming) got published.

It has been a while since my first book got published. Raspberry Pi underwent major

revisions. Multiple newer and more powerful models of Pi have been introduced.

I explored the world of single board computers in detail as I (correctly) thought that SBCs

could be best for cluster. Then I built a cluster with three units of Raspberry Pis for a

proof of concept. I went on building the clusters for various organizations, T-Schools, and

universities all over India.

Raspberry Pi also serves as an excellent learning platform for scientific

programming. We can use it for exploring important concepts in the scientific domains of

signal and image processing, or to perform symbolic computations with SymPy.

■ intRoduCtion

I have explored the domain of scientific programming in detail in this book. The

book has almost 100 coding examples to demonstrate and explore the world of scientific

programming.

I hope that this book will help the scientific community and young researchers to use

Raspberry Pi and Python as tools to explore scientific frontiers.

Why This Book?

As I said earlier, I found learning and exploring the topics in parallel and scientific

programming quite boring and difficult. However, these are some of the most useful

areas in computer science, with many applications in the real world. In my past and

current jobs, I routinely used various parallel and scientific programming libraries for

accomplishing various tasks. I want more people to get interested in this wonderful field.

However, when people approach me for guest lectures and talks on this topic, they have a

common complaint. They tell me that it’s difficult to get started with Raspberry Pi, parallel

and scientific programming due to lack of reliable materials and references over the

Internet. There are many tutorials and videos which teach people how to create a small

multi-node cluster with Raspberry Pi. But due to unfamiliarity with the notion of single

board computers, they found it difficult to grasp the information provided. So I decided to

combine the topics of Raspberry Pi setup and supercomputers in a single book. This book

teaches readers how to build a Raspberry Pi cluster as well as how to use Python to exploit

its computational power for various scientific tasks. The cluster we will build in this book will

be very basic one. It will be fit to be deployed in academic and research institutions. We will

also learn how to get started with symbolic programming, scientific programming, image

processing, and signal processing in this book. The book has within its scope the following:

• Introduction to single board computers, Python 3, and Raspberry Pi

• Basic concepts in supercomputing

• Preparing a node and building an entire cluster

• Parallel programming in Python

• Symbolic mathematical programming

• Scientific programming

• Image processing

• Signal processing

• Visualization with Matplotlib

Who Is This Book For?

This book is for beginners in Raspberry Pi and parallel programming. It is for people who

are interested in learning how to create a low-cost supercomputer and get started with

scientific programming at very low cost. However, this book is not for people completely

new to the world of computer science. I assume that you have a decent knowledge of

■ intRoduCtion

xxi

computers and that you are not new to the most fundamental concepts related to it.

The perfect reader of this book may be both a hobbyist and a student, someone who is

familiar with computers and wants to learn a more powerful tool to explore computer

science further. Makers and hackers will also find this book interesting. Finally, if you

have no idea why you are reading this book, you might get interested in Raspberry Pi,

supercomputers, scientific and parallel programming. I hope all the readers will enjoy

reading this book as much as I enjoyed writing it.

What This Book Is Not

This book is not a book for learning Python 3 programming and syntax from scratch. It is

more of a DIY cookbook for Raspberry Pi, scientific programming, and supercomputing.

If your understanding of coding is limited or if you are not from a computer science

background, then you will find it difficult to follow this book.

How This Book Is Organized

This book has 14 chapters. Here is a sneak peek into the chapters of the book:

Chapter 1: This chapter introduces the readers to the history and philosophy of

single board computers. It explores Raspberry Pi basics, and teaches readers to set up the

Raspberry Pi and connect it to a network.

Chapter 2: This chapter introduces the readers to important Linux commands. It

also details the procedure to establish remote connectivity with Raspberry Pi.

Chapter 3: The aim of this chapter is to introduce the readers to Python history and

philosophy. It discusses the differences between Python 2 and Python 3. It also explores

various modes of the Python 3 interpreter.

Chapter 4: This chapter serves to introduce the concept of supercomputing to the

readers.

Chapter 5: This chapter will demonstrate how to install MPI4PY on Raspberry Pi.

Chapter 6: This chapter teaches the readers how to build a supercomputer of Pis.

Chapter 7: This chapter teaches the readers how to overclock various models of Pi

safely.

Chapter 8: This detailed chapter introduces readers to MPI4PY programming with

Python 3. It explores many important concepts and constructs in parallel programming.

Chapter 9: This chapter introduces readers to the components of the SciPy stack. It

also gets the readers started with symbolic programming using SymPy.

Chapter 10: This chapter introduces readers to the world of numerical computation

with NumPy.

Chapter 11: This chapter introduces readers to the various modules in the SciPy

library.

Chapter 12: Readers explore the amazing world of signals and signal processing

graphically in this chapter.

Chapter 13: Readers will explore the basics of image processing in this chapter.

Chapter 14: This chapter provides a brief glimpse into the world of data visualization

with Matplotlib in Python.

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