GPU Computing Gems, Jade Edition

  • 10h 41m
  • Wen-mei W. Hwu (ed)
  • Elsevier Science and Technology Books, Inc.
  • 2012

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

In this Book

  • Introduction
  • Large-Scale GPU Search
  • Edge v. Node Parallelism for Graph Centrality Metrics
  • Optimizing Parallel Prefix Operations for the Fermi Architecture
  • Building an Efficient Hash Table on the GPU
  • Efficient CUDA Algorithms for the Maximum Network Flow Problem
  • Optimizing Memory Access Patterns for Cellular Automata on GPUs
  • Fast Minimum Spanning Tree Computation
  • Comparison-Based In-Place Sorting with CUDA
  • Interval Arithmetic in CUDA
  • Approximating the erfinv Function
  • A Hybrid Method for Solving Tridiagonal Systems on the GPU
  • Accelerating CULA Linear Algebra Routines with Hybrid GPU and Multicore Computing
  • GPU Accelerated Derivative-Free Mesh Optimization
  • Large-Scale Gas Turbine Simulations on GPU Clusters
  • GPU Acceleration of Rarefied Gas Dynamic Simulations
  • Application of Assembly of Finite Element Methods on Graphics Processors for Real-Time Elastodynamics
  • CUDA Implementation of Vertex-Centered, Finite Volume CFD Methods on Unstructured Grids with Flow Control Applications
  • Solving Wave Equations on Unstructured Geometries
  • Fast Electromagnetic Integral Equation Solvers on Graphics Processing Units
  • Solving Large Multibody Dynamics Problems on the GPU
  • Implicit FEM Solver on GPU for Interactive Deformation Simulation
  • Real-Time Adaptive GPU Multiagent Path Planning
  • Pricing Financial Derivatives with High Performance Finite Difference Solvers on GPUs
  • Large-Scale Credit Risk Loss Simulation
  • Monte Carlo–Based Financial Market Value-at-Risk Estimation on GPUs
  • Thrust—A Productivity-Oriented Library for CUDA
  • GPU Scripting and Code Generation with PyCUDA
  • Jacket—GPU Powered MATLAB Acceleration
  • Accelerating Development and Execution Speed with Just-in-Time GPU Code Generation
  • GPU Application Development, Debugging, and Performance Tuning with GPU Ocelot
  • Abstraction for AoS and SoA Layout in C++
  • Processing Device Arrays with C++ Metaprogramming
  • GPU Metaprogramming—A Case Study in Biologically Inspired Machine Vision
  • A Hybridization Methodology for High-Performance Linear Algebra Software for GPUs
  • Dynamic Load Balancing Using Work-Stealing
  • Applying Software-Managed Caching and CPU/GPU Task Scheduling for Accelerating Dynamic Workloads
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