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GPU Computing Gems Jade Edition
 
 

GPU Computing Gems Jade Edition, 1st Edition

 
GPU Computing Gems Jade Edition, 1st Edition,Wen-mei Hwu,ISBN9780123859631
 
 
 

W Hwu   

Morgan Kaufmann

9780123859631

9780123859648

560

240 X 197

Leading minds in GPGPU share cutting-edge parallel computing techniques that increase the speed of scientific innovation

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Key Features

  • 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

Description

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.

Readership

Software engineers, programmers, hardware engineers, advanced students

Wen-mei Hwu

Wen-mei Hwu: CTO of MulticoreWare, and is a professor at University of Illinois at Urbana-Champaign specializing in compiler design, computer architecture, computer microarchitecture, and parallel processing. He currently holds the Walter J. ("Jerry") Sanders III-Advanced Micro Devices Endowed Chair in Electrical and Computer Engineering in the Coordinated Science Laboratory. He is a PI for the petascale Blue Waters system, is co-director of the Intel and Microsoft funded Universal Parallel Computing Research Center (UPCRC), and PI for the world's first NVIDIA CUDA Center of Excellence. At the Illinois Coordinated Science Lab, Dr. Hwu leads the IMPACT Research Group and is director of the OpenIMPACT project - which has delivered new compiler and computer architecture technologies to the computer industry since 1987. He previously edited GPU Computing Gems, a similar work focusing on NVIDIA CUDA.

Affiliations and Expertise

CTO of MulticoreWare and professor specializing in compiler design, computer architecture, microarchitecture, and parallel processing, University of Illinois at Urbana-Champaign

View additional works by Wen-mei W. Hwu

GPU Computing Gems Jade Edition, 1st Edition

Editors, Reviewers, and Authors Introduction 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 Index

Quotes and reviews

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

 
 

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