140 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 6 Douglas Thain University of Notre Dame, USA Michael Albrecht University of Notre Dame, USA Hoang Bui University of Notre Dame, USA Peter Bui University of Notre Dame, USA Rory Carmichael University of Notre Dame, USA Scott Emrich University of Notre Dame, USA Data Intensive Computing with Clustered Chirp Servers ABSTRACT Over the last few decades, computing performance, memory capacity, and disk storage have all increased by many orders of magnitude. However, I/O performance has not increased at nearly the same pace: a disk arm movement is still measured in milliseconds, and disk I/O throughput is still measured in megabytes per second. If one wishes to build computer systems that can store and process petabytes of data, they must have large numbers of disks and the corresponding I/O paths and memory capacity to support the desired data rate. A cost effcient way to accomplish this is by clustering large numbers of commodity machines together. This chapter presents Chirp as a building block for clustered data intensive scientifc computing. Chirp was originally designed as a lightweight fle server for grid computing and was used as a “personal” fle server. The authors explore building systems with very high I/O capacity using commodity storage devices by tying together multiple Chirp servers. Several real-life applications such as the GRAND Data Analysis Grid, the Biometrics Research Grid, and the Biocompute Facility use Chirp as their fundamental building block, but provide different services and interfaces appropriate to their target communities. Patrick Flynn University of Notre Dame, USA DOI: 10.4018/978-1-61520-971-2.ch006