GPU Computing Gems, Jade Edition

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

Table of Contents

SECTION 1 Parallel Algorithms and Data Structures
Chapter 1 Large-Scale GPU Search
Chapter 2 Edge v. Node Parallelism for Graph Centrality Metrics
Chapter 3 Optimizing Parallel Prefix Operations for the Fermi Architecture
Chapter 4 Building an Efficient Hash Table on the GPU
Chapter 5 Efficient CUDA Algorithms for the Maximum Network Flow Problem
Chapter 6 Optimizing Memory Access Patterns for Cellular Automata on GPUs
Chapter 7 Fast Minimum Spanning Tree Computation
Chapter 8 Comparison-Based In-Place Sorting with CUDA

SECTION 2 Numerical Algorithms
Chapter 9 Interval Arithmetic in CUDA
Chapter 10 Approximating the erfinv Function
Chapter 11 A Hybrid Method for Solving Tridiagonal Systems on the GPU
Chapter 12 Accelerating CULA Linear Algebra Routines with Hybrid GPU and Multicore Computing
Chapter 13 GPU Accelerated Derivative-Free Mesh Optimization

SECTION 3 Engineering Simulation
Chapter 14 Large-Scale Gas Turbine Simulations on GPU Clusters
Chapter 15 GPU Acceleration of Rarefied Gas Dynamic Simulations
Chapter 16 Application of Assembly of Finite Element Methods on Graphics Processors for Real-Time Elastodynamics
Chapter 17 CUDA Implementation of Vertex-Centered, Finite Volume CFD Methods on Unstructured Grids with Flow Control Applications
Chapter 18 Solving Wave Equations on Unstructured Geometries
Chapter 19 Fast Electromagnetic Integral Equation Solvers on Graphics Processing Units

SECTION 4 Interactive Physics and AI for Games and Engineering Simulation
Chapter 20 Solving Large Multibody Dynamics Problems on the GPU
Chapter 21 Implicit FEM Solver on GPU for Interactive Deformation Simulation
Chapter 22 Real-Time Adaptive GPU Multiagent Path Planning

SECTION 5 Computational Finance
Chapter 23 Pricing Financial Derivatives with High Performance Finite Difference Solvers on GPUs
Chapter 24 Large-Scale Credit Risk Loss Simulation
Chapter 25 Monte Carlo–Based Financial Market Value-at-Risk Estimation on GPUs

SECTION 6 Programming Tools and Techniques
Chapter 26 Thrust: A Productivity-Oriented Library for CUDA
Chapter 27 GPU Scripting and Code Generation with PyCUDA
Chapter 28 Jacket: GPU Powered MATLAB Acceleration
Chapter 29 Accelerating Development and Execution Speed with Just-in-Time GPU Code Generation
Chapter 30 GPU Application Development, Debugging, and Performance Tuning with GPU Ocelot
Chapter 31 Abstraction for AoS and SoA Layout in C++
Chapter 32 Processing Device Arrays with C++ Metaprogramming
Chapter 33 GPU Metaprogramming: A Case Study in Biologically Inspired Machine Vision
Chapter 34 A Hybridization Methodology for High-Performance Linear Algebra Software for GPUs
Chapter 35 Dynamic Load Balancing Using Work-Stealing
Chapter 36 Applying Software-Managed Caching and CPU/GPU Task Scheduling for Accelerating Dynamic Workloads