1st Edition

Designing Scientific Applications on GPUs

Edited By

Raphael Couturier




ISBN 9781466571624
Published November 21, 2013 by Chapman and Hall/CRC
498 Pages 118 B/W Illustrations

USD $220.00

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Book Description

Many of today’s complex scientific applications now require a vast amount of computational power. General purpose graphics processing units (GPGPUs) enable researchers in a variety of fields to benefit from the computational power of all the cores available inside graphics cards.

Understand the Benefits of Using GPUs for Many Scientific Applications

Designing Scientific Applications on GPUs shows you how to use GPUs for applications in diverse scientific fields, from physics and mathematics to computer science. The book explains the methods necessary for designing or porting your scientific application on GPUs. It will improve your knowledge about image processing, numerical applications, methodology to design efficient applications, optimization methods, and much more.

Everything You Need to Design/Port Your Scientific Application on GPUs

The first part of the book introduces the GPUs and Nvidia’s CUDA programming model, currently the most widespread environment for designing GPU applications. The second part focuses on significant image processing applications on GPUs. The third part presents general methodologies for software development on GPUs and the fourth part describes the use of GPUs for addressing several optimization problems. The fifth part covers many numerical applications, including obstacle problems, fluid simulation, and atomic physics models. The last part illustrates agent-based simulations, pseudorandom number generation, and the solution of large sparse linear systems for integer factorization. Some of the codes presented in the book are available online.

Table of Contents

PRESENTATION OF GPUs
Presentation of the GPU Architecture and the Cuda Environment Raphaël Couturier
Introduction
Brief history of video card
GPGPU
Architecture of current GPUs
Kinds of parallelism
Cuda multithreading
Memory hierarchy

Introduction to Cuda Raphaël Couturier
Introduction
First example
Second example: using CUBLAS
Third example: matrix-matrix multiplication

IMAGE PROCESSING
Setting up the Environment
Gilles Perrot
Data transfers, memory management
Performance measurements

Implementing a Fast Median Filter Gilles Perrot
Introduction
Median filtering
NVidia GPU tuning recipes
A 3x3 median filter: using registers
A 5x5 and more median filter

Implementing an Efficient Convolution Operation on GPU Gilles Perrot
Overview
Definition
Implementation
Separable convolution

SOFTWARE DEVELOPMENT
Development of Software Components for Heterogeneous Many-Core Architectures Stefan L. Glimberg, Allan P. Engsig-Karup, Allan S. Nielsen, and Bernd Dammann
Software development for heterogeneous
Heterogeneous library design for PDE solvers
Model problems
Optimization strategies for multi-GPU systems

Development Methodologies for GPU and Cluster of GPUs Sylvain Contassot-Vivier, Stephane Vialle, and Jens Gustedt
Introduction
General scheme of synchronous code with computation/communication overlapping in GPU clusters
General scheme of asynchronous parallel code with computation/communication overlapping
Perspective: A unifying programming model

OPTIMIZATION
GPU-Accelerated Tree-Based Exact Optimization Methods
Imen Chakroun and Nouredine Melab
Introduction
Branch-and-bound (B&B) algorithm
Parallel B&B algorithms
The flowshop scheduling problem
GPU-accelerated B&B based on the parallel tree exploration (GPU-PTE-BB)
GPU-accelerated B&B based on the parallel evaluation of bounds (GPU-PEB-BB)
Thread divergence
Memory access optimization
Experiments

Parallel GPU-Accelerated Metaheuristics Malika Mehdi, Ahcène Bendjoudi, Lakhdar Loukil, and Nouredine Melab
Introduction
Combinatorial optimization
Parallel models for metaheuristics
Challenges for the design of GPU-based metaheuristics
State-of-the-art parallel metaheuristics on GPUs
Frameworks for metaheuristics on GPUs
Case study: Accelerating large neighborhood LS method on GPUs for solving the Q3AP

Linear Programming on a GPU: A Case Study Xavier Meyer, Bastien Chopard, and Paul Albuquerque
Introduction
Simplex algorithm
B&B algorithm
CUDA considerations
Implementations
Performance model
Measurements and analysis

NUMERICAL APPLICATIONS
Fast Hydrodynamics on Heterogeneous Many-Core Hardware
Allan P. Engsig-Karup, Stefan L. Glimberg, Allan S. Nielsen, and Ole Lindberg
On hardware trends and challenges in scientific applications
On modeling paradigms for highly nonlinear and dispersive water waves
Governing equations
The numerical model
Properties of the numerical model
Numerical experiments

Parallel Monotone Spline Interpolation and Approximation on GPUs Gleb Beliakov and Shaowu Liu
Introduction
Monotone splines
Smoothing noisy data via parallel isotone regression

Solving Linear Systems with GMRES and CG Methods on GPU Clusters Lilia Ziane Khodja, Raphaël Couturier, and Jacques Bahi
Introduction
Krylov iterative methods
Parallel implementation on a GPU cluster
Experimental results

Solving Sparse Nonlinear Systems of Obstacle Problems on GPU Clusters Lilia Ziane Khodja, Raphaël Couturier, Jacques Bahi, Ming Chau, and Pierre Spitéri
Introduction
Obstacle problems
Parallel iterative method
Parallel implementation on a GPU cluster
Experimental tests on a GPU cluster
Red-black ordering technique

Ludwig: Multiple GPUs for a Fluid Lattice Boltzmann Application Alan Gray and Kevin Stratford
Introduction
Background
Single GPU implementation
Multiple GPU implementation
Moving solid particles

Numerical Validation and GPU Performance in Atomic Physics Rachid Habel, Pierre Fortin, Fabienne Jézéquel, Jean-Luc Lamotte, and Stan Scott
Introduction
2DRMP and the PROP program
Numerical validation of PROP in single precision
Toward a complete deployment of PROP on GPUs
Performance results
Propagation of multiple concurrent energies on GPU

GPU-Accelerated Envelope-Following Method Xuexin Liu, Sheldon Xiang-Dong Tan, Hai Wang, and Hao Yu
Introduction
The envelope-following method in a nutshell
New parallel envelope-following method
Numerical examples

OTHER
Implementing Multi-Agent Systems on GPU
Guillaume Laville, Christophe Lang, Bénédicte Herrmann, Laurent Philippe, Kamel Mazouzi, and Nicolas Marilleau
Introduction
Running agent-based simulations
A first practical example
Second example
Analysis and recommendations

Pseudorandom Number Generator on GPU Raphaël Couturier and Christophe Guyeux
Introduction
Basic reminders
Toward efficiency and improvement for CI PRNG
Experiments

Solving Large Sparse Linear Systems for Integer Factorization on GPUs Bertil Schmidt and Hoang-Vu Dang
Introduction
Block Wiedemann algorithm
SpMV OVER GF(2) for NFS matrices using existing formats on GPUs
A hybrid format for SpMV on GPUs
SCOO for single-precision floating-point matrices
Performance evaluation

Index

A Bibliography appears at the end of each chapter.

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Editor(s)

Biography

Raphaël Couturier is a professor of computer science at the University of Franche-Comte and vice head of the Computer Science Department at FEMTO-ST Institute. He has co-authored over 80 articles in peer-reviewed journals and conferences. He received a Ph.D. from Henri Poincaré University. His research interests include parallel and distributed computation, numerical algorithms, GPU and FPGA computing, and asynchronous iterative algorithms.

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Raphael Couturier

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Reviews

"This book covers not only the knowledge of GPU and CUDA programming, but also provides successful real applications in many domains, including signal processing, image processing, physics, and artificial intelligence. The most recent research outcome and the most recent progress of GPU architectures are included, such as multi-GPU programming and GPU clusters. I believe it is a very good reference for GPU and CUDA parallel programming courses as it provides detailed illustration of the architectures of GPU, programming principles of CUDA, CUDA libraries for algebra, and a series of real applications. In addition, it will definitely contribute to the progress of research in CUDA-enabled parallel computing."
—Professor Ying Liu, School of Computer and Control, University of Chinese Academy of Sciences