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Books > Computing & IT > Computer hardware & operating systems > Computer architecture & logic design > Parallel processing
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Hands-On GPU Computing with Python - Explore the capabilities of GPUs for solving high performance computational problems (Paperback)
Loot Price: R1,164
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Hands-On GPU Computing with Python - Explore the capabilities of GPUs for solving high performance computational problems (Paperback)
Expected to ship within 10 - 15 working days
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Explore GPU-enabled programmable environment for machine learning,
scientific applications, and gaming using PuCUDA, PyOpenGL, and
Anaconda Accelerate Key Features Understand effective
synchronization strategies for faster processing using GPUs Write
parallel processing scripts with PyCuda and PyOpenCL Learn to use
the CUDA libraries like CuDNN for deep learning on GPUs Book
DescriptionGPUs are proving to be excellent general
purpose-parallel computing solutions for high performance tasks
such as deep learning and scientific computing. This book will be
your guide to getting started with GPU computing. It will start
with introducing GPU computing and explain the architecture and
programming models for GPUs. You will learn, by example, how to
perform GPU programming with Python, and you'll look at using
integrations such as PyCUDA, PyOpenCL, CuPy and Numba with Anaconda
for various tasks such as machine learning and data mining. Going
further, you will get to grips with GPU work flows, management, and
deployment using modern containerization solutions. Toward the end
of the book, you will get familiar with the principles of
distributed computing for training machine learning models and
enhancing efficiency and performance. By the end of this book, you
will be able to set up a GPU ecosystem for running complex
applications and data models that demand great processing
capabilities, and be able to efficiently manage memory to compute
your application effectively and quickly. What you will learn
Utilize Python libraries and frameworks for GPU acceleration Set up
a GPU-enabled programmable machine learning environment on your
system with Anaconda Deploy your machine learning system on cloud
containers with illustrated examples Explore PyCUDA and PyOpenCL
and compare them with platforms such as CUDA, OpenCL and ROCm.
Perform data mining tasks with machine learning models on GPUs
Extend your knowledge of GPU computing in scientific applications
Who this book is forData Scientist, Machine Learning enthusiasts
and professionals who wants to get started with GPU computation and
perform the complex tasks with low-latency. Intermediate knowledge
of Python programming is assumed.
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