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
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
