HANDS-ON GPU PROGRAMMING WITH PYTHON AND CUDA

HANDS-ON GPU PROGRAMMING WITH PYTHON AND CUDA. EXPLORE HIGH-PERFORMANCE PARALLEL COMPUTING WITH CUDA

Editorial:
VARIAS
Año de edición:
ISBN:
978-1-78899-391-3
EAN:
9781788993913
Páginas:
310
Disponibilidad:
NO DISPONIBLE

Descuento:

-10%

Antes:

$ 299.000,00

Despues:

$ 269.100,00

U$ 71,66 59,85 €

u003cpu003eu003cbu003eBuild real-world applications with Python 2.7, CUDA 9, and CUDA 10. We suggest the use of Python 2.7 over Python 3.x, since Python 2.7 has stable support across all the libraries we use in this book.u003c/bu003eu003c/pu003e Key Features u003culu003e u003cliu003eExpand your background in GPU programming--PyCUDA, scikit-cuda, and Nsightu003c/liu003e u003cliu003eEffectively use CUDA libraries such as cuBLAS, cuFFT, and cuSolveru003c/liu003e u003cliu003eApply GPU programming to modern data science applicationsu003c/liu003e u003c/ulu003e Book Description u003cpu003eHands-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.u003c/pu003e u003cpu003eAs 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.u003c/pu003e u003cpu003eWith 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.u003c/pu003e u003cpu003eBy the end of this book, you will be able to apply GPU programming to problems related to data science and high-performance computing.u003c/pu003e What you will learn u003culu003e u003cliu003eLaunch GPU code directly from Pythonu003c/liu003e u003cliu003eWrite effective and efficient GPU kernels and device functionsu003c/liu003e u003cliu003eUse libraries such as cuFFT, cuBLAS, and cuSolveru003c/liu003e u003cliu003eDebug and profile your code with Nsight and Visual Profileru003c/liu003e u003cliu003eApply GPU programming to datascience problemsu003c/liu003e u003cliu003eBuild a GPU-based deep neuralnetwork from scratchu003c/liu003e u003cliu003eExplore advanced GPU hardware features, such as warp shufflingu003c/liu003e u003c/ulu003e Who this book is for u003cpu003eHands-On GPU Programming with Python and CUDA is for developers and data scientists who want to learn the basics of effective GPU programming to improve performance using Python code. You should have an understanding of first-year college or university-level engineering mathematics and physics, and have some experience with Python as well as in any C-based programming language such as C, C++, Go, or Java.u003c/pu003e