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

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

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  • 製本 Hardcover:ハードカバー版／ページ数 560 p.
  • 言語 ENG
  • 商品コード 9780123859631
  • DDC分類 006.66

Full Description

GPU 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:

Contents

Part 1: Parallel Algorithms and Data Structures - Paulius Micikevicius, NVIDIA

1 Large-Scale GPU Search

2 Edge v. Node Parallelism for Graph Centrality Metrics

3 Optimizing parallel prefix operations for the Fermi architecture

4 Building an Efficient Hash Table on the GPU

5 An Efficient CUDA Algorithm for the Maximum Network Flow Problem

6 On Improved Memory Access Patterns for Cellular Automata Using CUDA

7 Fast Minimum Spanning Tree Computation on Large Graphs

8 Fast in-place sorting with CUDA based on bitonic sort

Part 2: Numerical Algorithms - Frank Jargstorff, NVIDIA

9 Interval Arithmetic in CUDA

10 Approximating the erfinv Function

11 A Hybrid Method for Solving Tridiagonal Systems on the GPU

12 LU Decomposition in CULA

13 GPU Accelerated Derivative-free Optimization

Part 3: Engineering Simulation - Peng Wang, NVIDIA

14 Large-scale gas turbine simulations on GPU clusters

15 GPU acceleration of rarefied gas dynamic simulations

16 Assembly of Finite Element Methods on Graphics  Processors

17 CUDA implementation of Vertex-Centered, Finite Volume CFD methods on Unstructured Grids with Flow Control Applications

18 Solving Wave Equations on Unstructured Geometries

19 Fast electromagnetic integral equation solvers on graphics processing units (GPUs)

Part 4: Interactive Physics for Games and Engineering Simulation - Richard Tonge, NVIDIA

20 Solving Large Multi-Body Dynamics Problems on the GPU

21 Implicit FEM Solver in CUDA

22 Real-time Adaptive GPU multi-agent path planning

Part 5: Computational Finance - Thomas Bradley, NVIDIA

23 High performance finite difference PDE solvers on GPUs for financial option pricing

24 Identifying and Mitigating Credit Risk using Large-scale Economic Capital Simulations

25 Financial Market Value-at-Risk Estimation using the Monte Carlo Method

Part 6: Programming Tools and Techniques - Cliff Wooley, NVIDIA

26 Thrust: A Productivity-Oriented Library for CUDA

27 GPU Scripting and Code Generation with PyCUDA

28 Jacket: GPU Powered MATLAB Acceleration

29 Accelerating Development and Execution Speed with Just In Time GPU Code Generation

30 GPU Application Development, Debugging, and Performance Tuning with GPU Ocelot

31 Abstraction for AoS and SoA Layout in C++

32 Processing Device Arrays with C++ Metaprogramming

33 GPU Metaprogramming: A Case Study in Biologically-Inspired Machine Vision

34 A Hybridization Methodology for High-Performance Linear Algebra Software for GPUs

35 Dynamic Load Balancing using Work-Stealing

36 Applying software-managed caching and CPU/GPU task scheduling for accelerating dynamic workloads