GPU Computing Gems Jade Edition book cover

GPU Computing Gems Jade Edition

GPU Computing Gems, Jade Edition describes successful application experiences in GPU computing and the techniques that contributed to that success. Divided into five sections, the 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. This book begins with an overview of parallel algorithms and data structures. The first few chapters focus on accelerating database searches, how to leverage the Fermi GPU architecture to further accelerate prefix operations, and GPU implementation of hash tables. The reader is then systematically walked through the fundamental optimization steps when implementing a bandwidth-limited algorithm, GPU-based libraries of numerical algorithms and software products for numerical analysis with dedicated GPU support, and the adoption of GPU computing techniques in production engineering simulation codes. The next chapters discuss 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. The book also proposes an alternative approach that balances computation regardless of node degree variance. This book will be useful to application developers in a wide range of application areas.


Software engineers, programmers, hardware engineers, advanced students

Hardbound, 560 Pages

Published: September 2011

Imprint: Morgan Kaufmann

ISBN: 978-0-12-385963-1


  • It wasn't until recently that parallel [GPU] computing made people realize that there are whole areas in computing science that we can tackle. … When you can do something 10 or 100 times faster, something magical happens and you can do something completely different.

    -Jen-Hsun Huang, CEO, NVIDIA


  • Editors, Reviewers, and Authors


    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 CCC

        Chapter 32 Processing Device Arrays with CCC 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



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