Hands-On GPU Programming with Python and CUDA: Explore high-performance parallel computing with CUDA
By 作者: Dr. Brian Tuomanen
ISBN-10 书号: 1788993918
ISBN-13 书号: 9781788993913
Release Finelybook 出版日期: 2018-11-27
pages 页数: (310 )
The Book Description robot was collected from Amazon and arranged by Finelybook
Launch GPU code directly from Python
Write effective and efficient GPU kernels and device functions
Use libraries such as cuFFT, cuBLAS, and cuSolver
Debug and profile your code with Nsight and Visual Profiler
Apply GPU programming to datascience problems
Build a GPU-based deep neuralnetwork from scratch
Explore advanced GPU hardware features, such as warp shuffling
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.
Expand your background in GPU programming—PyCUDA, scikit-cuda, and Nsight
Effectively use CUDA libraries such as cuBLAS, cuFFT, and cuSolver
Apply GPU programming to modern data science applications
1 Why GPU Programming?
2 Setting Up Your GPU Programming Environment
3 Getting Started with PyCUDA
4 Kernels, Threads, Blocks, and Grids
5 Streams, Events, Contexts, and Concurrency
6 Debugging and Profiling Your CUDA Code
7 Using the CUDA Libraries with Scikit-CUDA
8 The CUDA Device Function Libraries and Thrust
9 Implementation of a Deep Neural Network
10 Working with Compiled GPU Code
11 Performance Optimization in CUDA
12 Where to Go from Here