Beschreibung Hands-On GPU Computing with Python: Explore the capabilities of GPUs for solving high performance computational problems. 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 Description 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. 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 for Data 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.Table of Contents Introduction to GPU computing Designing A GPU Computing Strategy Setting up a GPU Computing Platform with NVIDIA and AMD Fundamentals of GPU programming Setting up your environment for GPU programming Working with PyCUDA Working with PyOpenCL Working with Anaconda and Anaconda Accelerate Containerization on GPU enabled platforms Machine Learning on GPUs: Use cases GPU Acceleration for Scientific Applications using Deepchem
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Hands-On GPU Computing with Python: Explore the ~ Hands-On GPU Computing with Python: Explore the capabilities of GPUs for solving high performance computational problems - Ebook written by Avimanyu Bandyopadhyay. Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Hands-On GPU Computing with Python: Explore the capabilities of GPUs for .
Hands-On GPU Computing with Python (Explore the ~ Hands-On GPU Computing with Python Explore the capabilities of GPUs for solving high performance computational problems. 0 . 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 .
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Hands-On GPU Computing with Python: Explore the ~ Hands-On GPU Computing with Python: Explore the capabilities of GPUs for solving high performance computational problems - Kindle edition by Bandyopadhyay, Avimanyu. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Hands-On GPU Computing with Python: Explore the capabilities of GPUs for solving .
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GPU Computing - IEEE Journals & Magazine ~ The GPU's rapid increase in both programmability and capability has spawned a research community that has successfully mapped a broad range of computationally demanding, complex problems to the GPU. This effort in general-purpose computing on the GPU, also known as GPU computing, has positioned the GPU as a compelling alternative to traditional microprocessors in high-performance computer .
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