GPU Computing Gems Jade EditionGPU Computing Gems, Jade Edition, offers hands-on, proven techniques for general purpose GPU programming based on the successful application experiences of leading researchers and developers. One of few resources available that distills the best practices of the community of CUDA programmers, this second edition contains 100% new material of interest across industry, including finance, medicine, imaging, engineering, gaming, environmental science, and green computing. It covers new tools and frameworks for productive GPU computing application development and provides immediate benefit to researchers developing improved programming environments for GPUs. Divided into five sections, this book explains how GPU execution is achieved with algorithm implementation techniques and approaches to data structure layout. More specifically, it considers three general requirements: high level of parallelism, coherent memory access by threads within warps, and coherent control flow within warps. Chapters explore topics such as accelerating database searches; how to leverage the Fermi GPU architecture to further accelerate prefix operations; and GPU implementation of hash tables. There are also discussions on the state of GPU computing in interactive physics and artificial intelligence; programming tools and techniques for GPU computing; and the edge and node parallelism approach for computing graph centrality metrics. In addition, the book proposes an alternative approach that balances computation regardless of node degree variance. Software engineers, programmers, hardware engineers, and advanced students will find this book extremely usefull. For useful source codes discussed throughout the book, the editors invite readers to the following website: ..." - 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 |
Contents
| 1 | |
Numerical Algorithms | 97 |
Engineering Simulation | 155 |
Interactive Physics and AI for Games and Engineering Simulation | 267 |
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Common terms and phrases
accelerate access pattern algorithm application approach architecture array auto-tuning binary bitonic sort bytes cache chapter coalesced const Core cuckoo hashing CUDA data structures device double precision dynamic edges efficient element equations evaluation example execution factor Figure finite float flow function GB/s GeForce GFLOP/s global memory GPU Computing Gems GPU implementation graph graphics processing graphics processing units grid hardware hash table hybrid input integral Intel iteration kernel layout linear Listing matrix memory access memory bandwidth mesh metaprogramming method multiple nodes number of threads NVIDIA Corporation Ocelot OpenCL operations optimization parallel parameters partition performance prefix sums problem processors programming PyCUDA radix sort random reduction runtime scan scenario Section shared memory SIMD simulation single precision solution solve solver speed-up stencil step stored task techniques template Tesla thread block throughput tridiagonal unstructured grids update values variables vector vertex vertices warp