If you need to learn CUDA but dont have experience with parallel computing, CUDA Programming: A Developers 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
CUDA Programming: A Developer's Guide to Parallel Computing with GPUs
โ Scribed by Shane Cook
- Publisher
- Morgan Kaufmann
- Year
- 2012
- Tongue
- English
- Leaves
- 600
- Series
- Applications of GPU Computing Series
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
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
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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 delvin
<p><b>Explore different GPU programming methods using libraries and directives, such as OpenACC, with extension to languages such as C, C++, and Python</b></p> <h4>Key Features</h4> <ul><li>Learn parallel programming principles and practices and performance analysis in GPU computing </li> <li>Get to
<p><b>Explore different GPU programming methods using libraries and directives, such as OpenACC, with extension to languages such as C, C++, and Python</b></p> <h4>Key Features</h4> <ul><li>Learn parallel programming principles and practices and performance analysis in GPU computing </li> <li>Get to