November 2018
Intermediate to advanced
310 pages
7h 54m
English
Content preview from Hands-On GPU Programming with Python and CUDA
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What this book covers
Chapter 1, Why GPU Programming?, gives us some motivations as to why we should learn this field, and how to apply Amdahl's Law to estimate potential performance improvements from translating a serial program to making use of a GPU.
Chapter 2, Setting Up Your GPU Programming Environment, explains how to set up an appropriate Python and C++ development environment for CUDA under both Windows and Linux.
Chapter 3, Getting Started with PyCUDA, shows the most essential skills we will need for programming GPUs from Python. We will notably see how to transfer data to and from a GPU using PyCUDA's gpuarray class, and how to compile simple CUDA kernels with PyCUDA's ElementwiseKernel function.
Chapter 4, Kernels, Threads, Blocks, ...
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