In this chapter, we started by giving the definition of an artificial neural network, and showed you how individual ANs can be combined into dense layers, which combine together into a full-on deep neural network. We then implemented a dense layer in CUDA-C and made an appropriate corresponding Python wrapper class. We also included functionality to add ReLU and sigmoid layers on the outputs of a dense layer. We saw the definition and motivation of using a softmax layer, which is used for classification problems, and then implemented this in CUDA-C and Python. Finally, we implemented a Python class so that we could build a sequential feed-forward DNN from the prior classes; we implemented a cross-entropy loss function, and then used this in our loss function in our implementation of gradient descent to train the weights and biases in our DNN. Finally, we used our implementation...
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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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