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内容説明
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
著者について
Rob Farber has served as a scientist at the Irish Center for High-End Computing, U.S. national labs in Los Alamos, Berkeley, and the Pacific Northwest, and external faculty at the Santa Fe Institute. His articles have appeared in Dr. Dobb's Journal and Scientific Computing, among others.
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Details on Memory Coalescing and Linux Tools (Profiler + GDB)
2012/1/13
形式:Kindle版|Amazonが確認した購入
I desperately need some insights about CUDA memory performance, and there are two chapters discussing memory coalescing which are very useful to me. I need to write a very simple kernel to convert 3 bytes RGB into 4 bytes RGBA so that OpenGL could render it correctly, but what I have done naively is to spawn too many threads and by doing so it slow down the code so much, so much so that I wondered what I have really done.
Later I came to know that CUDA memory utilization is my bottleneck. Being a traditional programmer, I never pay attention to how I access memory. Now with the insights on memory coalescing in this book, I get the real education about how GPU really works.
On the other hand, I use CUDA under Linux, it is important to know that CUDA has provided a profiling tool to diagnose memory bottleneck, and cuda-gdb could use to step through my CUDA kernel. This book also spend quite some details to illustrate how to use nvcc. This is also up to date to discuss Fermi architecture (Compute Capability 2.x with L1, L2 cache) and CUDA Toolkit 4.0 .
Get this book if you need real insights from a real practitioner on CUDA. Also for the entry, you should get CUDA by Example (Jason Sanders and Edward Kandrot) as well. You would be so glad as I did.
Later I came to know that CUDA memory utilization is my bottleneck. Being a traditional programmer, I never pay attention to how I access memory. Now with the insights on memory coalescing in this book, I get the real education about how GPU really works.
On the other hand, I use CUDA under Linux, it is important to know that CUDA has provided a profiling tool to diagnose memory bottleneck, and cuda-gdb could use to step through my CUDA kernel. This book also spend quite some details to illustrate how to use nvcc. This is also up to date to discuss Fermi architecture (Compute Capability 2.x with L1, L2 cache) and CUDA Toolkit 4.0 .
Get this book if you need real insights from a real practitioner on CUDA. Also for the entry, you should get CUDA by Example (Jason Sanders and Edward Kandrot) as well. You would be so glad as I did.
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