Concepts of Parallel Computing下载

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斯坦福大学并行计算课件第一辑，讲解很翔实，一共49页
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斯坦福大学并行计算课件第一辑，讲解很翔实，一共49页

CUDA范例精解通用GPU编程CUDA by Example addresses the heart of the software development challenge by leveraging one of the most innovative and powerful solutions to the problem of programming the massively parallel accelerators in recent years. This book introduces you to programming in CUDA C by providing examples and insight into the process of constructing and effectively using NVIDIA GPUs. It presents introductory concepts of parallel computing from simple examples to debugging (both logical and performance), as well as covers advanced topics and issues related to using and building many applications. Throughout the book, programming examples reinforce the concepts that have been presented. The book is required reading for anyone working with accelerator-based computing systems. It explores parallel computing in depth and provides an approach to many problems that may be encountered. It is especially useful for application developers, numerical library writers, and students and teachers of parallel computing. I have enjoyed and learned from this book, and I feel confident that you will as well. Jack Dongarra University Distinguished Professor, University of Tennessee Distinguished Research Staff Member, Oak Ridge National Laboratory

Master efficient parallel programming to build powerful applications using Python About This Book Design and implement efficient parallel software Master new programming techniques to address and solve complex programming problems Explore the world of parallel programming with this book, which is a go-to resource for different kinds of parallel computing tasks in Python, using examples and topics covered in great depth Who This Book Is For Python Parallel Programming Cookbook is intended for software developers who are well versed with Python and want to use parallel programming techniques to write powerful and efficient code. This book will help you master the basics and the advanced of parallel computing. What You Will Learn Synchronize multiple threads and processes to manage parallel tasks Implement message passing communication between processes to build parallel applications Program your own GPU cards to address complex problems Manage computing entities to execute distributed computational tasks Write efficient programs by adopting the event-driven programming model Explore the cloud technology with DJango and Google App Engine Apply parallel programming techniques that can lead to performance improvements In Detail Parallel programming techniques are required for a developer to get the best use of all the computational resources available today and to build efficient software systems. From multi-core to GPU systems up to the distributed architectures, the high computation of programs throughout requires the use of programming tools and software libraries. Because of this, it is becoming increasingly important to know what the parallel programming techniques are. Python is commonly used as even non-experts can easily deal with its concepts. This book will teach you parallel programming techniques using examples in Python and will help you explore the many ways in which you can write code that allows more than one process to happen at once. Starting with introducing you to the world of parallel computing, it moves on to cover the fundamentals in Python. This is followed by exploring the thread-based parallelism model using the Python threading module by synchronizing threads and using locks, mutex, semaphores queues, GIL, and the thread pool. Next you will be taught about process-based parallelism where you will synchronize processes using message passing along with learning about the performance of MPI Python Modules. You will then go on to learn the asynchronous parallel programming model using the Python asyncio module along with handling exceptions. Moving on, you will discover distributed computing with Python, and learn how to install a broker, use Celery Python Module, and create a worker. You will also understand the StarCluster framework, Pycsp, Scoop, and Disco modules in Python. Further on, you will learn GPU programming with Python using the PyCUDA module along with evaluating performance limitations. Next you will get acquainted with the cloud computing concepts in Python, using Google App Engine (GAE), and building your first application with GAE. Lastly, you will learn about grid computing concepts in Python and using PyGlobus toolkit, GFTP and GASS COPY to transfer files, and service monitoring in PyGlobus. Style and approach A step-by-step guide to parallel programming using Python, with recipes accompanied by one or more programming examples. It is a practically oriented book and has all the necessary underlying parallel computing concepts. Table of Contents Chapter 1: Getting Started with Parallel Computing and Python Chapter 2: Thread-based Parallelism Chapter 3: Process-based Parallelism Chapter 4: Asynchronous Programming Chapter 5: Distributed Python Chapter 6: GPU Programming with Python

Paperback: 107 pages Publisher: Packt Publishing - ebooks Account (June 25, 2014) Language: English Develop efficient parallel systems using the robust Python environment Overview Demonstrates the concepts of Python parallel programming Boosts your Python computing capabilities Contains easy-to-understand explanations and plenty of examples In Detail Starting with the basics of parallel programming, you will proceed to learn about how to build parallel algorithms and their implementation. You will then gain the expertise to evaluate problem domains, identify if a particular problem can be parallelized, and how to use the Threading and Multiprocessor modules in Python. The Python Parallel (PP) module, which is another mechanism for parallel programming, is covered in depth to help you optimize the usage of PP. You will also delve into using Celery to perform distributed tasks efficiently and easily. Furthermore, you will learn about asynchronous I/O using the asyncio module. Finally, by the end of this book you will acquire an in-depth understanding about what the Python language has to offer in terms of built-in and external modules for an effective implementation of Parallel Programming. This is a definitive guide that will teach you everything you need to know to develop and maintain high-performance parallel computing systems using the feature-rich Python. What you will learn from this book Explore techniques to parallelize problems Integrate the Parallel Python module to implement Python code Execute parallel solutions on simple problems Achieve communication between processes using Pipe and Queue Use Celery Distributed Task Queue Implement asynchronous I/O using the Python asyncio module Create thread-safe structures Approach A fast, easy-to-follow and clear tutorial to help you develop Parallel computing systems using Python. Along with explaining the fundamentals, the book will also introduce you to slightly advanced concepts and will help you in implementing these techniques in the real world. Who this book is written for If you are an experienced Python programmer and are willing to utilize the available computing resources by parallelizing applications in a simple way, then this book is for you. You are required to have a basic knowledge of Python development to get the most of this book.

通过模型理论介绍、建模步骤流程图、结合权威文献模型选择等实现，零计量、Stata基础，照样做好计量实证分析。还赠送Eviews操作高清视频哟！ Invasive Computing for Mapping Parallel Programs to Many-Core Architectures (Computer Architecture and Design Methodologies) By 作者: Andreas Weichslgartner – Stefan Wildermann – Michael Glaß – Jürgen Teich ISBN-10 书号: 9811073554 ISBN-13 书号: 9789811073557 Edition 版本: 1st ed. 2018 出版日期: 2017-12-30 pages 页数: (178) Springer英文原版超清 This book provides an overview of and essential insights on invasive computing. Pursuing a comprehensive approach, it addresses proper concepts, invasive language constructs, and the principles of invasive hardware. The main focus is on the important topic of how to map task-parallel applications to future multi-core architectures including 1,000 or more processor units. A special focus today is the question of how applications can be mapped onto such architectures while not only taking into account functional correctness, but also non-functional execution properties such as execution times and security properties. The book provides extensive experimental evaluations, investigating the benefits of applying invasive computing and hybrid application mapping to give guarantees on non-functional properties such as timing, energy, and security. The techniques in this book are presented in a step-by-step manner, supported by examples and figures. All proposed ideas for providing guarantees on performance, energy consumption, and security are enabled by using the concept of invasive computing and the exclusive usage of resources. Cover Front Matter 1.Introduction 2.Invasive Computing 3.Fundamentals 4.Self-embedding 5.Hybrid Application Mapping 6.Hybrid Mapping for Increased Security 7.Conclusions and Future Work Back Matter

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