GPU Computing Gems Jade Edition (Applications of GPU Computing Series)

Posted by Marwan Abd Ellah on Saturday, March 31, 2012 Under: GPGPU

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


Tags: gpgpu gpu "parallel programming" 
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