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Hands-On GPU Programming with Python and CUDA - Explore high-performance parallel computing with CUDA (Paperback)
Loot Price: R1,263
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Hands-On GPU Programming with Python and CUDA - Explore high-performance parallel computing with CUDA (Paperback)
Expected to ship within 10 - 15 working days
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Build 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.
Key Features 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 Book DescriptionHands-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. What you will learn 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 Who this book is forHands-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.
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