![]() ![]() It will start with introducing GPU computing and explain the architecture and programming models for GPUs. ![]() This book will be your guide to getting started with GPU computing. GPUs are proving to be excellent general purpose-parallel computing solutions for high performance tasks such as deep learning and scientific computing. Learn to use the CUDA libraries like CuDNN for deep learning on GPUs Write parallel processing scripts with P圜uda and PyOpenCL Understand effective synchronization strategies for faster processing using GPUs Visit ourĬUDA by Example page for more details on obtaining the book, a sample chapter, and the full source code for the book's examples.Įxplore GPU-enabled programmable environment for machine learning, scientific applications, and gaming using PuCUDA, PyOpenGL, and Anaconda Accelerate Now available: "CUDA by Example: An Introduction to General-Purpose GPU Programming" CUDA by Example, written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology, including a concise introduction to the CUDA platform and architecture, as well as a quick-start guide to CUDA C. Sign up for a GPU Computing registered developer account to get early access to prerelease drivers, tools, bug reporting, and more. With the CUDA architecture and tools, developers are achieving dramatic speedups in fields such as medical imaging and natural resource exploration, and creating breakthrough applications in areas such as image recognition and real-time HD video playback and encoding.ĬUDA enables this unprecedented performance via standard APIs such OpenCL and DirectCompute, and high level programming languages such as C/C++, Fortran, Java, Python, and the Microsoft. The CUDA™ architecture enables developers to leverage the massively parallel processing power of NVIDIA GPUs, delivering the performance of NVIDIA’s world-renowned graphics processor technology to general purpose GPU Computing. ![]() CUDA Developer Guide for Optimus Platforms.CUDA Programming Guide for CUDA Toolkit 3.2.*New* Updated versions of the CUDA C Programming Guide and the Fermi Tuning Guide are available via the links below. ![]()
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