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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

Thread-safe atomic operations

We will now learn about atomic operations in CUDA. Atomic operations are very simple, thread-safe operations that output to a single global array element or shared memory variable, which would normally lead to race conditions otherwise.

Let's think of one example. Suppose that we have a kernel, and we set a local variable called x across all threads at some point. We then want to find the maximum value over all xs, and then set this value to the shared variable we declare with __shared__ int x_largest. We can do this by just calling atomicMax(&x_largest, x) over every thread.

Let's look at a brief example of atomic operations. We will write a small program for two experiments:

  • Setting a variable to 0 and then adding 1 to this for each thread
  • Finding the maximum thread ID value across all threads

Let's start out by setting the...

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