CUDA Application Design and Development

04/10/2013 · No Responses

As the computer industry retools to leverage massively parallel graphics processing units (GPUs), this book is designed to meet the needs of working software developers who need to understand GPU programming with CUDA and increase efficiency in their projects. CUDA Application Design and Development starts with an introduction to parallel computing concepts for readers with no previous parallel experience, and focuses on issues of immediate importance to working software developers: achieving high performance, maintaining competitiveness, analyzing CUDA benefits versus costs, and determining application lifespan.

The book then details the thought behind CUDA and teaches how to create, analyze, and debug CUDA applications. Throughout, the focus is on software engineering issues: how to use CUDA in the context of existing application code, with existing compilers, languages, software tools, and industry-standard API libraries.

Using an approach refined in a series of well-received articles at Dr Dobb’s Journal, author Rob Farber takes the reader step-by-step from fundamentals to implementation, moving from language theory to practical coding.

Includes multiple examples building from simple to more complex applications in four key areas: machine learning, visualization, vision recognition, and mobile computing
Addresses the foundational issues for CUDA development: multi-threaded programming and the different memory hierarchy
Includes teaching chapters designed to give a full understanding of CUDA tools, techniques and structure.
Presents CUDA techniques in the context of the hardware they are implemented on as well as other styles of programming that will help readers bridge into the new material

Table of Contents
Chapter 1. First Programs and How to Think in CUDA
Chapter 2. CUDA for Machine Learning and Optimization
Chapter 3. The CUDA Tool Suite: Profiling a PCA/NLPCA
Chapter 4. The CUDA Execution Model
Chapter 5. CUDA Memory
Chapter 6. Efficiently Using GPU Memory
Chapter 7. Techniques to Increase Parallelism
Chapter 8. CUDA for All GPU and CPU Applications
Chapter 9. Mixing CUDA and Rendering
Chapter 10. CUDA in a Cloud and Cluster Environments
Chapter 11. CUDA for Real Problems
Chapter 12. Application Focus on Live Streaming Video