Introduction To Parallel Programming Udacity
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Programming Massively Parallel Processors
Programming Massively Parallel Processors: A Hands-on Approach, Second Edition, teaches students how to program massively parallel processors. It offers a detailed discussion of various techniques for constructing parallel programs. Case studies are used to demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. This guide shows both student and professional alike the basic concepts of parallel programming and GPU architecture. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in depth. This revised edition contains more parallel programming examples, commonly-used libraries such as Thrust, and explanations of the latest tools. It also provides new coverage of CUDA 5.0, improved performance, enhanced development tools, increased hardware support, and more; increased coverage of related technology, OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism; and two new case studies (on MRI reconstruction and molecular visualization) that explore the latest applications of CUDA and GPUs for scientific research and high-performance computing. This book should be a valuable resource for advanced students, software engineers, programmers, and hardware engineers. - New coverage of CUDA 5.0, improved performance, enhanced development tools, increased hardware support, and more - Increased coverage of related technology, OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism - Two new case studies (on MRI reconstruction and molecular visualization) explore the latest applications of CUDA and GPUs for scientific research and high-performance computing
MOOCs and Open Education Around the World
As new digital forms of formal and informal learning proliferate, there is an increasing need to better understand how people in different regions of the world are implementing massive open online courses (MOOCs) and other forms of open educational resources (OERs). Educators, researchers, politicians, and numerous other stakeholders want to grasp what the outcomes of these initiatives are and how they can be improved. Ongoing e-learning developments related to both technology and pedagogy have pushed institutions and organizations to grapple with issues of accreditation, credentialing, quality standards, innovative assessment, and learner motivation and attrition, among other areas of concern. In response, MOOCs and Open Education Around the World explores and illuminates unique implementations of MOOCs and open education across regions and nations. The book also focuses on the various opportunities as well as the dilemmas presented in this rapidly evolving age of technology-enabled learning. What are the different delivery formats, interaction possibilities, assessment schemes, and business models? What are the key controversies or issues that must be discussed and addressed? This edited collection explains MOOCs and open education trends and issues in a variety of contexts, shares key research findings, and provides practical suggestions and recommendations for the near future.
CUDA for Engineers
Author: Duane Storti
language: en
Publisher: Addison-Wesley Professional
Release Date: 2015-11-02
CUDA for Engineers gives you direct, hands-on engagement with personal, high-performance parallel computing, enabling you to do computations on a gaming-level PC that would have required a supercomputer just a few years ago. The authors introduce the essentials of CUDA C programming clearly and concisely, quickly guiding you from running sample programs to building your own code. Throughout, you’ll learn from complete examples you can build, run, and modify, complemented by additional projects that deepen your understanding. All projects are fully developed, with detailed building instructions for all major platforms. Ideal for any scientist, engineer, or student with at least introductory programming experience, this guide assumes no specialized background in GPU-based or parallel computing. In an appendix, the authors also present a refresher on C programming for those who need it. Coverage includes Preparing your computer to run CUDA programs Understanding CUDA’s parallelism model and C extensions Transferring data between CPU and GPU Managing timing, profiling, error handling, and debugging Creating 2D grids Interoperating with OpenGL to provide real-time user interactivity Performing basic simulations with differential equations Using stencils to manage related computations across threads Exploiting CUDA’s shared memory capability to enhance performance Interacting with 3D data: slicing, volume rendering, and ray casting Using CUDA libraries Finding more CUDA resources and code Realistic example applications include Visualizing functions in 2D and 3D Solving differential equations while changing initial or boundary conditions Viewing/processing images or image stacks Computing inner products and centroids Solving systems of linear algebraic equations Monte-Carlo computations