GPU computing gems (Morgan Kaufmann, 2012)
This is the second volume of Morgan Kaufmann's GPU Computing Gems, offering an all-new set of insights, ideas, and practical "hands-on" skills from researchers and developers worldwide. Each chapter gives you a window into the work being performed across a variety of application domains, and the opportunity to witness the impact of parallel GPU computing on the efficiency of scientific research. GPU Computing Gems: Jade Edition showcases the latest research solutions with GPGPU and CUDA, including: Improving memory access patterns for cellular automata using CUDA Large-scale gas turbine simulations on GPU clusters Identifying and mitigating credit risk using large-scale economic capital simulations GPU-powered MATLAB acceleration with Jacket Biologically-inspired machine vision An efficient CUDA algorithm for the maximum network flow problem 30 more chapters of innovative GPU computing ideas, written to be accessible to researchers from any industry GPU Computing Gems: Jade Edition contains 100% new material covering a variety of application domains: algorithms and data structures, engineering, interactive physics for games, computational finance, and programming tools. This second volume of GPU Computing Gems offers 100% new material of interest across industry, including finance, medicine, imaging, engineering, gaming, environmental science, green computing, and more Covers new tools and frameworks for productive GPU computing application development and offers immediate benefit to researchers developing improved programming environments for GPUs Even more hands-on, proven techniques demonstrating how general purpose GPU computing is changing scientific research Distills the best practices of the community of CUDA programmers; each chapter provides insights and ideas as well as 'hands on' skills applicable to a variety of fields
"Since the introduction of CUDA in 2007, more than 100 million computers with CUDA capable GPUs have been shipped to end users. GPU computing application developers can now expect their application to have a mass market. With the introduction of OpenCL in 2010, researchers can now expect to develop GPU applications that can run on hardware from multiple vendors"--
Includes bibliographical references and index