CUDA Programming
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CUDA Programming

A Developer's Guide to Parallel Computing with GPUs
 Taschenbuch
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ISBN-13:
9780124159334
Einband:
Taschenbuch
Erscheinungsdatum:
01.12.2012
Seiten:
576
Autor:
Shane Cook
Gewicht:
1162 g
Format:
235x193x33 mm
Serie:
Morgan Kaufmann
Sprache:
Englisch
Beschreibung:

Cook, ShaneShane Cook is Technical Director at CUDA Developer, a consultancy company that helps companies exploit the power of GPUs by re-engineering code to make the optimal use of the hardware available. He formed CUDA Developer upon realizing the potential of heterogeneous systems and CUDA to disrupt existing serial and parallel programming technologies. He has a degree in Applied Software Engineering, specializing in the embedded software field. He has worked in senior roles with blue chip companies over the past twenty years, always seeking to help to develop the engineers in his team. He has worked on C programming standards including the MISRA Safer C used by widely in the automotive software community, and previously developed code for companies in the Germany automotive and defense contracting industries as well as Nortel and Ford Motor Company.
If you need to learn CUDA but don't have experience with parallel computing, CUDA Programming: A Developer's Introduction offers a detailed guide to CUDA with a grounding in parallel fundamentals. It starts by introducing CUDA and bringing you up to speed on GPU parallelism and hardware, then delving into CUDA installation. Chapters on core concepts including threads, blocks, grids, and memory focus on both parallel and CUDA-specific issues. Later, the book demonstrates CUDA in practice for optimizing applications, adjusting to new hardware, and solving common problems.

Comprehensive introduction to parallel programming with CUDA, for readers new to both
Detailed instructions help readers optimize the CUDA software development kit
Practical techniques illustrate working with memory, threads, algorithms, resources, and more
Covers CUDA on multiple hardware platforms: Mac, Linux and Windows with several NVIDIA chipsets
Each chapter includes exercises to test reader knowledge
A Short History of Supercomputing
Understanding Parallelism with GPUs
CUDA Hardware Overview
Setting Up Cuda
Grids, Blocks, and Threads
Memory Handling with CUDA
Using CUDA in Practice
Multi-CPU and Multi-GPU Solutions
Optimizing Your Application
Libraries and SDK
Designing GPU-Based Systems
Common Problems, Causes, and Solutions

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