GPU Computing Gems Jade Edition

<p>Part 1: Parallel Algorithms and Data Structures – Paulius Micikevicius, NVIDIA </p> <p>1 Large-Scale GPU Search</p> <p>2 Edge v. Node Parallelism for Graph Centrality Metrics</p> <p>3 Optimizing parallel prefix operations for the Fermi architecture</p> <p>4 Building an Efficient Hash Table on the GPU</p> <p>5 An Efficient CUDA Algorithm for the Maximum Network Flow Problem</p> <p>6 On Improved Memory Access Patterns for Cellular Automata Using CUDA</p> <p>7 Fast Minimum Spanning Tree Computation on Large Graphs</p> <p>8 Fast in-place sorting with CUDA based on bitonic sort</p> <p>Part 2: Numerical Algorithms – Frank Jargstorff, NVIDIA </p> <p>9 Interval Arithmetic in CUDA</p> <p>10 Approximating the erfinv Function</p> <p>11 A Hybrid Method for Solving Tridiagonal Systems on the GPU</p> <p>12 LU Decomposition in CULA</p> <p>13 GPU Accelerated Derivative-free Optimization</p> <p>Part 3: Engineering Simulation – Peng Wang, NVIDIA </p> <p>14 Large-scale gas turbine simulations on GPU clusters</p> <p>15 GPU acceleration of rarefied gas dynamic simulations</p> <p>16 Assembly of Finite Element Methods on Graphics  Processors</p> <p>17 CUDA implementation of Vertex-Centered, Finite Volume CFD methods on Unstructured Grids with Flow Control Applications</p> <p>18 Solving Wave Equations on Unstructured Geometries</p> <p>19 Fast electromagnetic integral equation solvers on graphics processing units (GPUs)</p> <p>Part 4: Interactive Physics for Games and Engineering Simulation – Richard Tonge, NVIDIA </p> <p>20 Solving Large Multi-Body Dynamics Problems on the GPU</p> <p>21 Implicit FEM Solver in CUDA</p> <p>22 Real-time Adaptive GPU multi-agent path planning</p> <p>Part 5: Computational Finance – Thomas Bradley, NVIDIA </p> <p>23 High performance finite difference PDE solvers on GPUs for financial option pricing</p> <p>24 Identifying and Mitigating Credit Risk using Large-scale Economic Capital Simulations</p> <p>25 Financial Market Value-at-Risk Estimation using the Monte Carlo Method</p> <p>Part 6: Programming Tools and Techniques – Cliff Wooley, NVIDIA </p> <p>26 Thrust: A Productivity-Oriented Library for CUDA</p> <p>27 GPU Scripting and Code Generation with PyCUDA</p> <p>28 Jacket: GPU Powered MATLAB Acceleration</p> <p>29 Accelerating Development and Execution Speed with Just In Time GPU Code Generation</p> <p>30 GPU Application Development, Debugging, and Performance Tuning with GPU Ocelot</p> <p>31 Abstraction for AoS and SoA Layout in C++</p> <p>32 Processing Device Arrays with C++ Metaprogramming</p> <p>33 GPU Metaprogramming: A Case Study in Biologically-Inspired Machine Vision</p> <p>34 A Hybridization Methodology for High-Performance Linear Algebra Software for GPUs</p> <p>35 Dynamic Load Balancing using Work-Stealing</p> <p>36 Applying software-managed caching and CPU/GPU task scheduling for accelerating dynamic workloads</p>