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Scientific Programming
Volume 10, Issue 4, Pages 271-289

Applying Scheduling and Tuning to On-line Parallel Tomography

Shava Smallen,1 Henri Casanova,2,3 and Francine Berman2,3

1Computer Science Department, Indiana University, Bloomington, IN 47404-7104, USA
2Computer Science and Engineering Department, University of California, San Diego, 9500 Gilman Dr, La Jolla, CA 92093-0114, USA
3San Diego Supercomputer Center, 9500 Gilman Dr, La Jolla, CA 92093-0505, USA

Received 26 November 2002; Accepted 26 November 2002

Copyright © 2002 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Tomography is a popular technique to reconstruct the three-dimensional structure of an object from a series of two-dimensional projections. Tomography is resource-intensive and deployment of a parallel implementation onto Grid platforms has been studied in previous work. In this work, we address on-line execution of the application where computation is performed as data is collected from an on-line instrument. The goal is to compute incremental 3-D reconstructions that provide quasi-real-time feedback to the user. We model on-line parallel tomography as a tunable application: trade-offs between resolution of the reconstruction and frequency of feedback can be used to accommodate various resource availabilities. We demonstrate that application scheduling/tuning can be framed as multiple constrained optimization problems and evaluate our methodology in simulation. Our results show that prediction of dynamic network performance is key to efficient scheduling and that tunability allows for production runs of on-line parallel tomography in Computational Grid environments.