Abstract
The advent of parallel computing technology and low-cost computing hardware has facilitated the adoption of high-performance computing tools for the analysis of sedimentation data. Over the past 15 years, we have developed the UltraScan software (Demeler et al., http://ultrascan.uthscsa.edu) to support sedimentation analysis, experimental design, and data management. We describe here recent extensions and advances in methodology that have been adapted in UltraScan. High-performance computing methods implemented on parallel supercomputers utilizing grid computing technology are used to analyze sedimentation experiments at much higher resolution than was previously possible. We discuss the implementation of parallel computing in three novel algorithms used in UltraScan for modeling of sedimentation velocity experiments and provide guidelines for effective data analysis.
| Original language | English |
|---|---|
| Pages (from-to) | 139-148 |
| Number of pages | 10 |
| Journal | Colloid and Polymer Science |
| Volume | 286 |
| Issue number | 2 |
| DOIs | |
| State | Published - Feb 2008 |
Funding
We would like to thank Josh Wilson, Yu Ning, and Bruce Dubbs for contributions to the web interface code. This research has been supported by NSF Grant DBI-9974819, NIH Grant 1 R01 RR022200-01A1, and the San Antonio Life Science Institute with Grant #10001642, all to B.D. The parallel calculations were performed on the Linux cluster at the Bioinformatics Core Facility at the University of Texas Health Science Center and on the Lonestar cluster at TACC through NSF Teragrid Allocation # TG-MCB070038. We gratefully acknowledge support by the Robert J. Kleberg Jr. and Helen C. Kleberg Foundation.
| Funder number |
|---|
| 10001642 |
| DBI-9974819 |
| 1 R01 RR022200-01A1 |
Keywords
- Genetic algorithms
- MPI
- Monte Carlo
- Two-dimensional spectrum analysis