In this chapter, we will be taking a tour of three of the standard CUDA libraries intended for streamlined numerical and scientific computation. The first that we will look at is cuBLAS, which is NVIDIA's implementation of the Basic Linear Algebra Subprograms (BLAS) specification for CUDA. (cuBLAS is NVIDIA's answer to various optimized, CPU-based implementations of BLAS, such as the free/open source OpenBLAS or Intel's proprietary Math Kernel Library.) The next library that we will look at is cuFFT, which can perform virtually every variation of the fast Fourier transform (FFT) on the GPU. We'll look at how we can use cuFFT for filtering in image processing in particular. We will then look at cuSolver, which can perform more involved linear algebra operations than those featured in cuBLAS, such as singular value decomposition...
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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
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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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