Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781788993913
Length 310 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Dr. Brian Tuomanen Dr. Brian Tuomanen
Author Profile Icon Dr. Brian Tuomanen
Dr. Brian Tuomanen
Arrow right icon
View More author details
Toc

Table of Contents (15) Chapters Close

Preface 1. Why GPU Programming? FREE CHAPTER 2. Setting Up Your GPU Programming Environment 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...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image