Build an inexpensive cluster of multiple Raspberry Pi computers and install all the required libraries to write parallel and scientific programs in Python 3. This book covers setting up your Raspberry Pis, installing the necessary software, and making a cluster of multiple Pis. Once the cluster is built, its power has to be exploited by means of programs to run on it. So, Raspberry Pi Supercomputing and Scientific Programming teaches you to code the cluster with the MPI4PY library of Python 3. Along the way, you will learn the concepts of the Message Passing Interface (MPI) standards and will explore the fundamentals of parallel programming on your inexpensive cluster. This will make this book a great starting point for supercomputing enthusiasts who want to get started with parallel programming. The book finishes with details of symbolic mathematics and scientific and numerical programming in Python, using SymPi, SciPy, NumPy, and Matplotlib. You’ll see how to process signals and images, carry out calculations using linear algebra, and visualize your results, all using Python code. With the power of a Raspberry Pi supercomputer at your fingertips, data-intensive scientific programming becomes a reality at home. What You Will Learn Discover the essentials of supercomputing Build a low-cost cluster of Raspberry Pis at home Harness the power of parallel programming and the Message Passing Interface (MPI) Use your Raspberry Pi for symbolic, numerical, and scientific programming Who This Book Is For Python 3 developers who seek the knowledge of parallel programming, Raspberry Pi enthusiasts, researchers, and the scientific Python community.

This will make this book a great starting point for supercomputing enthusiasts who want to get started with parallel programming. This book covers setting up your Raspberry Pis, installing the necessary software, and making a cluster of multiple Pis. Once the cluster is built, its power has to be exploited by means of programs to run on it. So, Raspberry Pi Supercomputing and Scientific Programming teaches you to code the cluster with the MPI4PY library of Python 3. Along the way, you will learn the concepts of the Message Passing Interface (MPI) standards and will explore the fundamentals of parallel programming on your inexpensive cluster.

The world of single-board computing puts powerful coding tools in the palm of your hand. The portable Raspberry Pi computing platform with the power of Linux yields an exciting exploratory tool for beginning scientific computing. Science and Computing with Raspberry Pi takes the enterprising researcher, student, or hobbyist through explorations in a variety of computing exercises with the physical sciences. The book has tutorials and exercises for a wide range of scientific computing problems while guiding the user through: * Configuring your Raspberry Pi and Linux operating system * Understanding the software requirements while using the Pi for scientific computing * Computing exercises in physics, astronomy, chaos theory, and machine learning

A step-by-step guide that will enhance your skills in creating powerful systems to solve complex issues About This Book Carlos R. Morrison from NASA will teach you to build a supercomputer with Raspberry Pi 3 Deepen your understanding of setting up host nodes, configuring networks, and automating mountable drives Learn various math, physics, and engineering applications to solve complex problems Who This Book Is For This book targets hobbyists and enthusiasts who want to explore building supercomputers with microcomputers. Researchers will also find this book useful. Prior programming knowledge is necessary; knowledge of supercomputers is not. What You Will Learn Understand the concept of the Message Passing Interface (MPI) Understand node networking. Configure nodes so that they can communicate with each other via the network switch Build a Raspberry Pi3 supercomputer. Test the supercluster Use the supercomputer to calculate MPI p codes. Learn various practical supercomputer applications In Detail Author Carlos R. Morrison (Staff Scientist, NASA) will empower the uninitiated reader to quickly assemble and operate a Pi3 supercomputer in the shortest possible time. The lifeblood of a supercomputer, the MPI code, is introduced early, and sample MPI code provides additional practice opportunities for you to test the effectiveness of your creation. You will learn how to configure various nodes and switches so that they can effectively communicate with each other. By the end of this book, you will have successfully built a supercomputer and the various applications related to it. Style and approach A progressive guide that will start off with serial coding and MPI concepts, moving towards configuring a complete supercluster, and solving real world problems

Implement real-time data processing applications on the Raspberry Pi. This book uniquely helps you work with data science concepts as part of real-time applications using the Raspberry Pi as a localized cloud. You’ll start with a brief introduction to data science followed by a dedicated look at the fundamental concepts of Python programming. Here you’ll install the software needed for Python programming on the Pi, and then review the various data types and modules available. The next steps are to set up your Pis for gathering real-time data and incorporate the basic operations of data science related to real-time applications. You’ll then combine all these new skills to work with machine learning concepts that will enable your Raspberry Pi to learn from the data it gathers. Case studies round out the book to give you an idea of the range of domains where these concepts can be applied. By the end of Data Science with the Raspberry Pi, you’ll understand that many applications are now dependent upon cloud computing. As Raspberry Pis are cheap, it is easy to use a number of them closer to the sensors gathering the data and restrict the analytics closer to the edge. You’ll find that not only is the Pi an easy entry point to data science, it also provides an elegant solution to cloud computing limitations through localized deployment. What You Will Learn Interface the Raspberry Pi with sensors Set up the Raspberry Pi as a localized cloud Tackle data science concepts with Python on the Pi Who This Book Is For Data scientists who are looking to implement real-time applications using the Raspberry Pi as an edge device and localized cloud. Readers should have a basic knowledge in mathematics, computers, and statistics. A working knowledge of Python and the Raspberry Pi is an added advantage.

This book follows a step-by-step, tutorial-based approach which will teach you how to develop your own super cluster using Raspberry Pi computers quickly and efficiently. Raspberry Pi Super Cluster is an introductory guide for those interested in experimenting with parallel computing at home. Aimed at Raspberry Pi enthusiasts, this book is a primer for getting your first cluster up and running. Basic knowledge of C or Java would be helpful but no prior knowledge of parallel computing is necessary.

The book presents the proceedings of four conferences: The 26th International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA'20), The 18th International Conference on Scientific Computing (CSC'20); The 17th International Conference on Modeling, Simulation and Visualization Methods (MSV'20); and The 16th International Conference on Grid, Cloud, and Cluster Computing (GCC'20). The conferences took place in Las Vegas, NV, USA, July 27-30, 2020. The conferences are part of the larger 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20), which features 20 major tracks. Authors include academics, researchers, professionals, and students. Presents the proceedings of four conferences as part of the 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20); Includes the research tracks Parallel and Distributed Processing, Scientific Computing, Modeling, Simulation and Visualization, and Grid, Cloud, and Cluster Computing; Features papers from PDPTA’20, CSC’20, MSV’20, and GCC’20.