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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

Summary

In this chapter, we looked at how to initialize a stream of random numbers in cuRAND by choosing the appropriate seed. Since computers are deterministic devices, they can only generate lists of pseudo-random numbers, so our seed should be something truly random; generally, adding a thread ID to the clock time in milliseconds will work well enough for most purposes.

We then looked at how we can use the uniform distribution from cuRAND to do a basic estimate of Pi. Then we took on a more ambitious project of creating a Python class that can compute definite integrals of arbitrary functions; we used some ideas from metaprogramming coupled with the CUDA Math API to define these arbitrary functions. Finally, we had a brief overview of the CUDA Thrust library, which is generally used for writing pure CUDA C programs outside of Python. Thrust most notably provides a device_vector...

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