In the last chapter, looking at a fairly broad overview of the libraries that are available in CUDA through the Scikit-CUDA wrapper module. We will now look at a few other libraries that we will have to use directly from within CUDA C proper, without the assistance of wrappers like those in Scikit-CUDA. We will start by looking at two standard libraries that consist of device functions that we may invoke from any CUDA C kernel cuRAND and the CUDA Math API. By the end of learning how to use these libraries, we will know how to use these libraries in the context of Monte Carlo integration. Monte Carlo integration is a well-known randomized method that provides estimates for the values of definite integrals from calculus. We will first look at a basic example of how to implement a simple Monte Carlo method with cuRAND to do a basic estimate...
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Book Overview & Buying
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Table Of Contents
Hands-On GPU Programming with Python and CUDA
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Hands-On GPU Programming with Python and CUDA
By:
Overview of this book
Hands-On GPU Programming with Python and CUDA hits the ground running: you’ll start by learning how to apply Amdahl’s Law, use a code profiler to identify bottlenecks in your Python code, and set up an appropriate GPU programming environment. You’ll then see how to “query” the GPU’s features and copy arrays of data to and from the GPU’s own memory.
As you make your way through the book, you’ll launch code directly onto the GPU and write full blown GPU kernels and device functions in CUDA C. You’ll get to grips with profiling GPU code effectively and fully test and debug your code using Nsight IDE. Next, you’ll explore some of the more well-known NVIDIA libraries, such as cuFFT and cuBLAS.
With a solid background in place, you will now apply your new-found knowledge to develop your very own GPU-based deep neural network from scratch. You’ll then explore advanced topics, such as warp shuffling, dynamic parallelism, and PTX assembly. In the final chapter, you’ll see some topics and applications related to GPU programming that you may wish to pursue, including AI, graphics, and blockchain.
By the end of this book, you will be able to apply GPU programming to problems related to data science and high-performance computing.
Table of Contents (15 chapters)
Preface
Free Chapter
Why GPU Programming?
Setting Up Your GPU Programming Environment
Getting Started with PyCUDA
Kernels, Threads, Blocks, and Grids
Streams, Events, Contexts, and Concurrency
Debugging and Profiling Your CUDA Code
Using the CUDA Libraries with Scikit-CUDA
The CUDA Device Function Libraries and Thrust
Implementation of a Deep Neural Network
Working with Compiled GPU Code
Performance Optimization in CUDA
Where to Go from Here
Assessment
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