Summary

Until now, research and development results in high-performance heterogeneous computing were dispersed over various collections of papers, providing a fragmented picture of the field. The first book to present the state of the art in this rapidly developing area of computer science, High Performance Heterogeneous Computing provides computer scientists, applied mathematicians, software engineers, scientific and applied programmers, graduate and postgraduate students, and researchers with a complete overview of software for parallel and distributed programming for heterogeneous networks, including parallel programming systems, distributed programming systems, and grid-based systems.

Author Biography

Alexey L. Lastovetsky, PhD, is a Senior Lecturer at the School of Computer Science and Informatics at the University College Dublin (UCD), where he is also the founder and Director of the Heterogeneous Computing Laboratory. His main research interests include algorithms, models, and programming tools for high performance heterogeneous computing. He is the author of mpC, the first parallel programming language for heterogeneous networks of computers. He has published over ninety technical papers in refereed journals and edited books and proceedings for international conferences. He is also the author of Parallel Computing on Heterogeneous Networks (Wiley).

Jack J. Dongarra, PhD, is University Distinguished Professor of Computer Science in the Electrical Engineering and Computer Science Department at the University of Tennessee and serves as Distinguished Research Staff in the Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL). He is a Turing Fellow in the Computer Science and Mathematics Schools at the University of Manchester and an Adjunct Professor in the Computer Science Department at Rice University. He specializes in numerical algorithms in linear algebra, parallel computing, the use of advanced computer architectures, programming methodology, and tools for parallel computers. His research includes the development, testing, and documentation of high quality mathematical software. He has published approximately 200 articles, papers, reports, or technical memoranda and is the coauthor of several books. He is a member of the IEEE and is the recipient of the IEEE Sid Fernbach Award and the first IEEE Medal of Excellence in Scalable Computing.

Introduction
Heterogeneous Platforms: Taxonomy, Typical Uses And Programming Issues.
Heterogeneous platforms and their uses.
Taxonomy of heterogeneous platforms
Vendor designed heterogeneous systems
Heterogeneous clusters
Local network of computers
Global network of computers
Grid based systems
Other heterogeneous platforms
Typical uses of heterogeneous platforms
Traditional use
Parallel computing
Distributed computing
Programming issues.
Performance
Fault tolerance
Arithmetic heterogeneity
Performance Models Of Heterogeneous Platforms And Design Of Heterogeneous Algorithms
Distribution of computations with constant performance models of heterogeneous processors.
Simplest constant performance model of heterogeneous processors and optimal distribution of independent units of computation with this model
Data distribution problems with constant performance models of heterogeneous processors
Partitioning well ordered sets with constant performance models of heterogeneous processors
Partitioning matrices with constant performance models of heterogeneous processors
Distribution of computations with non-constant performance models of heterogeneous processors.
Functional performance model of heterogeneous processors
Data partitioning with the functional performance model of heterogeneous processors
Other non-constant performance models of heterogeneous processors
Step-wise functional model
Functional model with limits on task size
Band model
Communication performance models for high performance heterogeneous computing.
Modeling the communication performance for scientific computing: the scope of interest
Communication models for parallel computing on heterogeneous clusters
Communication performance models for local and global networks of computers
Performance analysis of heterogeneous algorithms.
Efficiency analysis of heterogeneous algorithms
Scalability analysis of heterogeneous algorithms
Performance: Implementation And Software
Implementation issues.
Portable implementation of heterogeneous algorithms and self-adaptable applications
Performance models of heterogeneous platforms: estimation of parameters
Estimation of constant performance models of heterogeneous processors
Estimation of functional and band performance models of heterogeneous processors
Benchmarking of communication operations
Performance models of heterogeneous algorithms and their use in applications and programming systems
Implementation of homogeneous algorithms for heterogeneous platforms
Programming systems for high performance heterogeneous computing.
Parallel programming systems for heterogeneous platforms
Traditional parallel programming systems
Message passing programming systems
MPI
PVM
Comparison between PVM and MPI
Linda
HPF
Heterogeneous parallel programming systems
Distributed programming systems
NetSolve
Nimrod
Java
GridRPC
Applications
Numerical linear algebra software for heterogeneous clusters.
Heterogeneous PBLAS: introduction and user interface
Heterogeneous PBLAS: software design
Experiments with HeteroPBLAS
Parallel processing of remotely sensed hyperspectral images on heterogeneous clusters.
Hyperspectral imaging: introduction and parallel techniques
A parallel algorithm for analysis of hyperspectral images and its implementation for heterogeneous clusters
Experiments with the heterogeneous hyperspectral imaging application
Conclusion
Simulation of the evolution of clusters of galaxies on heterogeneous computational Grids.
Hydropad: a simulator of galaxies? evolution
Enabling Hydropad for Grid computing
GridRPC implementation of Hydropad
Experiments with GridSolve-enabled Hydropad
SmartGridSolve and Hydropad
SmartGridSolve implementation of Hydropad
Experiments with the SmartGridSolve-enabled Hydropad
Conclusion
Future Trends
Future trends in computing.
Introduction
Computational resources
Complex and heterogeneous parallel systems
Intel-ization of the processor landscape
New architectures on the horizon
Applications
Software
Some important concepts for the future
Heterogeneous hardware environments
Software architecture
Open source
New applications
Verification and validation
Data
2009 and beyond
Bibliography
Appendices
Appendix to Chapter 3
A.1
A.2
Appendix to Chapter 4
Proof of Proposition 4.1
Proof of Proposition 4.2
Proof of Proposition 4.3
Functional optimization problem with optimal solution, locally non-optimal
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