We first saw how to query our GPU from PyCUDA, and with this re-create the CUDA deviceQuery program in Python. We then learned how to transfer NumPy arrays to and from the GPU's memory with the PyCUDA gpuarray class and its to_gpu and get functions. We got a feel for using gpuarray objects by observing how to use them to do basic calculations on the GPU, and we learned to do a little investigative work using IPython's prun profiler. We saw there is sometimes some arbitrary slowdown when running GPU functions from PyCUDA for the first time in a session, due to PyCUDA launching NVIDIA's nvcc compiler to compile inline CUDA C code. We then saw how to use the ElementwiseKernel function to compile and launch element-wise operations, which are automatically parallelized onto the GPU from Python. We did a brief review of functional programming in Python (in particular...
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Book Overview & Buying
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Table Of Contents
Hands-On GPU Programming with Python and CUDA
By :
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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