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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

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.

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

Part 1: Parallel Algorithms and Data Structures - Paulius Micikevicius, NVIDIA

1 Large-Scale GPU Search

2 Edge v. Node Parallelism for Graph Centrality Metrics

3 Optimizing parallel prefix operations for the Fermi architecture

4 Building an Efficient Hash Table on the GPU

5 An Efficient CUDA Algorithm for the Maximum Network Flow Problem

6 On Improved Memory Access Patterns for Cellular Automata Using CUDA

7 Fast Minimum Spanning Tree Computation on Large Graphs

8 Fast in-place sorting with CUDA based on bitonic sort

Part 2: Numerical Algorithms - Frank Jargstorff, NVIDIA

9 Interval Arithmetic in CUDA

10 Approximating the erfinv Function

11 A Hybrid Method for Solving Tridiagonal Systems on the GPU

12 LU Decomposition in CULA

13 GPU Accelerated Derivative-free Optimization

Part 3: Engineering Simulation - Peng Wang, NVIDIA

14 Large-scale gas turbine simulations on GPU clusters

15 GPU acceleration of rarefied gas dynamic simulations

16 Assembly of Finite Element Methods on Graphics  Processors

17 CUDA implementation of Vertex-Centered, Finite Volume CFD methods on Unstructured Grids with Flow Control Applications

18 Solving Wave Equations on Unstructured Geometries

19 Fast electromagnetic integral equation solvers on graphics processing units (GPUs)

Part 4: Interactive Physics and AI for Games and Engineering Simulation - Richard Tonge, NVIDIA

20 Solving Large Multi-Body Dynamics Problems on the GPU

21 Implicit FEM Solver in CUDA

22 Real-time Adaptive GPU multi-agent path planning

Part 5: Computational Finance - Thomas Bradley, NVIDIA

23 High performance finite difference PDE solvers on GPUs for financial option pricing

24 Identifying and Mitigating Credit Risk using Large-scale Economic Capital Simulations

25 Financial Market Value-at-Risk Estimation using the Monte Carlo Method

Part 6: Programming Tools and Techniques - Cliff Wooley, NVIDIA

26 Thrust: A Productivity-Oriented Library for CUDA

27 GPU Scripting and Code Generation with PyCUDA

28 Jacket: GPU Powered MATLAB Acceleration

29 Accelerating Development and Execution Speed with Just In Time GPU Code Generation

30 GPU Application Development, Debugging, and Performance Tuning with GPU Ocelot

31 Abstraction for AoS and SoA Layout in C++

32 Processing Device Arrays with C++ Metaprogramming

33 GPU Metaprogramming: A Case Study in Biologically-Inspired Machine Vision

34 A Hybridization Methodology for High-Performance Linear Algebra Software for GPUs

35 Dynamic Load Balancing using Work-Stealing

36 Applying software-managed caching and CPU/GPU task scheduling for accelerating dynamic workloads

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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