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  • 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

GPUs 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.

  • 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
Page Count 452
Course Length 13 hours 33 minutes
ISBN 9781789341072
Date Of Publication 13 May 2019


Avimanyu Bandyopadhyay

Avimanyu Bandyopadhyay is currently pursuing a PhD degree in Bioinformatics based on applied GPU computing in Computational Biology at Heritage Institute of Technology, Kolkata, India. Since 2014, he developed a keen interest in GPU computing, and used CUDA for his master's thesis. He has experience as a systems administrator as well, particularly on the Linux platform.

Avimanyu is also a scientific writer, technology communicator, and a passionate gamer. He has published technical writing on open source computing and has actively participated in NVIDIA's GPU computing conferences since 2016. A big-time Linux fan, he strongly believes in the significance of Linux and an open source approach in scientific research. Deep learning with GPUs is his new passion!