This book has been a journey, much like a daring mountain hike… but now, at last, we have arrived at the end of our trek. We now stand upon the summit of mount introductory-GPU-programming, and we stand proud as we gaze back upon our native village of serial-programming-ville and smile as we think about the quaint naivity of our old one-dimensional programming traditions, where we considered forking a process in Unix to be our entire understanding of the notion of parallel programming. We have braved many pitfalls and dangers to arrive at this point, and we may have even made such mishaps as installing a broken NVIDIA driver module in Linux, or maybe downloading the wrong Visual Studio version over a slow 100k connection while visiting our parents for vacation. But these setbacks were only temporary, leaving wounds that developed into calluses that...
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
Other Books You May Enjoy
Customer Reviews