Skip navigation

Daniel A. Reed

Daniel A. Reed holds the Edward William and Jane Marr Gutgsell Professorship at the University of Illinois at Urbana-Champaign. He is also Director of the National Center for Supercomputing Applications (NCSA), Director of the National Computational Science Alliance, and Chief Architect, NSF TeraGrid.

Titles by This Author

Message-Based Parallel Processing

High-performance message-based supercomputers have only recently emerged from the research laboratory. The commercial manufacturing of such products as the Intel iPSC, the Ametek s/14, the NCUBE/ten, and the FPS T Series - all based on multicomputer network technology - has sparked lively interest in high-performance computation, and particularly in the message-passing paradigm for parallel computation.

This book makes readily available information on many aspects of the design and use of multicomputer networks, including machine organization, system software, and application programs. It provides an introduction to the field for students and researchers and a survey of important recent results for practicing engineers. The emphasis throughout is on design principles and techniques; however, there are also descriptions and comparison of research and commercial machines and case studies of specific applications.

Multicomputer Networks covers such major design areas as communication hardware, operating systems, fault tolerance, algorithms, and the selection of network topologies. The authors present results in each of these areas, emphasizing analytic models of interconnection networks, VLSI constraints and communication, communication paradigms and hardware support, multicomputer operating systems, and applications for distributed simulation and for partial differential equations. They survey the hardware designs and the available software and present a comparative performance study of existing machines.

Titles by This Editor

Achieving System Balance
Edited by Daniel A. Reed

As we enter the "decade of data," the disparity between the vast amount of data storage capacity (measurable in terabytes and petabytes) and the bandwidth available for accessing it has created an input/output bottleneck that is proving to be a major constraint on the effective use of scientific data for research. Scalable Input/Output is a summary of the major research results of the Scalable I/O Initiative, launched by Paul Messina, then Director of the Center for Advanced Computing Research at the California Institute of Technology, to explore software and algorithmic solutions to the I/O imbalance. The contributors explore techniques for I/O optimization, including: I/O characterization to understand application and system I/O patterns; system checkpointing strategies; collective I/O and parallel database support for scientific applications; parallel I/O libraries and strategies for file striping, prefetching, and write behind; compilation strategies for out-of-core data access; scheduling and shared virtual memory alternatives; network support for low-latency data transfer; and parallel I/O application programming interfaces.