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Hands-On GPU Programming with Python and CUDA

You're reading from   Hands-On GPU Programming with Python and CUDA Explore high-performance parallel computing with CUDA

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781788993913
Length 310 pages
Edition 1st Edition
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Author (1):
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Dr. Brian Tuomanen Dr. Brian Tuomanen
Author Profile Icon Dr. Brian Tuomanen
Dr. Brian Tuomanen
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Table of Contents (15) Chapters Close

Preface 1. Why GPU Programming? 2. Setting Up Your GPU Programming Environment FREE CHAPTER 3. Getting Started with PyCUDA 4. Kernels, Threads, Blocks, and Grids 5. Streams, Events, Contexts, and Concurrency 6. Debugging and Profiling Your CUDA Code 7. Using the CUDA Libraries with Scikit-CUDA 8. The CUDA Device Function Libraries and Thrust 9. Implementation of a Deep Neural Network 10. Working with Compiled GPU Code 11. Performance Optimization in CUDA 12. Where to Go from Here 13. Assessment 14. Other Books You May Enjoy

The CUDA Math API

Now, we will take a look at the CUDA Math API. This is a library that consists of device functions similar to those in the standard C math.h library that can be called from individual threads in a kernel. One difference here is that single and double valued floating-point operations are overloaded, so if we use sin(x) where x is a float, the sin function will yield a 32-bit float as the output, while if x were a 64-bit double, then the output of sin would also be a 64-bit value (Usually, this is the proper name for a 32-bit function, but it has an f at the end, such as sinf). There are also additional instrinsic functions. Intrinsic functions are less accurate but faster math functions that are built into the NVIDIA CUDA hardware; generally, they have similar names to the original function, except that they are preceded with two underscores—therefore,...

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