Scalable Input/Output: Achieving System Balance

Overview

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 forAdvanced Computing Research at the California ...

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Overview

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 forAdvanced 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/Ooptimization, 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.

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Meet the Author

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 SupercomputingApplications (NCSA), Director of the National Computational Science Alliance, and Chief Architect,NSF TeraGrid.

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Table of Contents

Preface
1 I/O Characterization and Analysis 1
2 Collective I/O and Large-Scale Data Management 35
3 Building Parallel Database Systems for Multidimensional Data 77
4 ADIO: A Framework for High-Performance, Portable Parallel I/O 111
5 Informed Prefetching of Collective Input/Output Requests 135
6 Compiler Support for Out-of-Core Arrays on Parallel Machines 161
7 CLIP: A Checkpointing Tool for Message Passing Parallel Programs 181
8 Learning to Classify Parallel I/O Access Patterns 201
9 Thread Scheduling for Out-of-Core Applications with a Memory Server 233
10 A Scalability Study of Shared Virtual Memory Systems 255
App Proposal for a Common Parallel File System Programming Interface
References 355
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