Raster Map Projection Transformation Using a Virtual System to Interactively Share Computing Resources

1. Raster Map Projection Transformation Using a Virtual System to Interactively Share Computing Resources  Michael P. Finn CyberGIS Toolkit Session 1130 – 1230, 06 Aug

2. Collaborators/ Co-Authors • Yan Liu • CyberInfrastructure and Geospatial Information (CIGI) Laboratory, University of Illinois at Urbana-Champaign (UIUC) • David M. Mattli • USGS, Center of Excellence for Geospatial Information Science (CEGIS) • Babak Behzad • UIUC, Department of Computer Science   • Kristina H. Yamamoto • USGS, CEGIS  • Anand Padmanabhan • UIUC, CIGI Laboratory • Michael Stramel • USGS, CEGIS

3. Outline • Overview/objectives • Motivation for HPC-Research • pRasterBlaster – a primer and the early days • Current Implementation/ Program Components • Performance • Status

4. Overview • Parallel computing for raster (map projection, other) • Raster was born to be parallelized • Use a large amount of computing power for a relatively short period of time • CEGIS parallel computing as part of the National Science Foundation CyberGIS Grant

5. Objectives • Develop a rapid raster projection transformation and Web service using HPC technology -- pRasterBlaster • Provide a software package that better handles known problems for wide use in the modeling community

6. Motivation for HPC-Research Raster processing too slow or even impossible on desktop machines for large datasets mapIMG Example: Re-projecting a 1GB raster dataset can take 45-60 minutes Solution: Solve problems using multiple processors Create user-centric Web 2.0 interface that hides non-important-to-user, complex HPC implementation details

7. pRasterBlaster Primer • mapIMG in HPC environment • Map projection/ reprojection for raster datasets • Rigorous geometry handling and novel resampling • Resampling options for categorical data and population counts (also standard continuous data resampling methods)

8. pRasterBlaster (the Early Days) • Very similar to mapIMG (V. 03) • Row-wise decomposition • I/O occurred directly in program inner loop • Disadvantages • Inefficient I/O caused by too many I/O requests • Changing I/O implementation difficult • Difficulty with multiple processors writing to a shared file system

9. Program Evaluation • Wall time vs. speedup • Amdahl’s Law: calculate maximum speedup • Communications overhead • Communications between processors is SLOW • See Mattli and Others, Poster: pRasterBlaster: Fast, Accurate Raster Reprojection, Tuesday 1830 • Bottlenecks; Liu and Others Briefing, Computational Performance Profiling of the USGS pRasterBlaster Map Reprojection Software, Next • Solving bottlenecks • Behzad Parallel Spatial Data I/O Library Briefing at 1200

10. Current Implementation (1.0) • I/O and reprojection separated into stages • I/O can be batched and reordered • Partitioning more flexible • Partition can be less than a row

11. Version 1.0 Components • Prologue • Open input files, calculate size of output, calculate partitions, create output and temp files • Reprojection – for each partition in parallel • Read input to memory, iterate through output pixels and reproject, write partition to temporary files • Epilogue • Write final output by aggregating temporary files from all computing nodes

12. Performance Observations • Evaluations ongoing • Close teamwork between CEGIS and CIGI • The overhead of aggregating output files is very high • Likely to adopt CIGI suggestion/ work • Related to creating a new, high performance I/O based on netCDF-4/ HDF-5 parallel functions • Need more testing in other environments

13. Recent Accomplishments/ Current Status • pRasterBlaster development continuing • See Usery Plenary Address, CyberGIS Progress and Perspectives in the Context of USGS, Wednesday 1115 – 1215

14. References • Atkins, D. E., K. K. Droegemeier, et al. (2003). Revolutionizing Science and Engineering Through Cyberinfrastructure: Report of the National Science Foundation Blue-Ribbon Advisory Panel on Cyberinfrastructure. Arlington, VA, National Science Foundation. • Behzad, Babak, Yan Liu, Eric Shook, Michael P. Finn, David M. Mattli, and Shaowen Wang (2012).A Performance Profiling Strategy for High-Performance Map Re-Projection of Coarse-Scale Spatial Raster Data.Abstract accepted for presentation at the Auto-Carto 2012, A Cartography and Geographic Information Society Research Symposium, Columbus, OH. • Wang, Shaowen and Yan Liu (2009) TeraGrid GIScience Gateway: Bridging cyberinfrastructure and GIScience. International Journal of Geographical Information Science, Volume 23, Number 5, May, pages 631-656. • Wang, Shaowen, Yan Liu, Nancy Wilkins-Diehr, and Stuart Martin (2009) SimpleGrid toolkit: Enabling geosciences gateways to cyberinfrastructure. Computers and Geosciences, Volume 35, Number 12, December, pages 2283-2294. • Williams, Michael S., Michael P. Finn, and Robert A. Buehler (2006). An Open Source, Object-Oriented General Cartographic Transformation Program (GCTP). Abstract presented at the International Society for Photogrammetry and Remote Sensing Commission IV Symposium on Geospatial Databases for Sustainable Development, Goa, India.

15. Science Related to This Project • Michael P. Finn is a Co-Editor (with Sergio Rey, U. of Arizona (another CyberGIS Grant Collaborator)) of a Special Issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964) on "Spatial Data Infrastructures, Cyberinfrastructure, and e-Science for GIScience."See http://www.mdpi.com/journal/ijgi/special_issues/spatial_data/. •  Finn, Michael P., Yan Liu, David M. Mattli, Babak Behzad,Kristina H. Yamamoto, Qingfeng (Gene) Guan, Eric Shook, Anand Padmanabhan (2012). High-Performance Small-Scale Raster Map Projection Transformation Using a Virtual System to Interactively Share Computing Resources and Data. Paper in work for a chapter in CyberGIS: Fostering a New Wave of Geospatial Discovery and Innovation, Shaowen Wang and Michael F. Goodchild, editors. Springer-Verlag.  • Behzad, Babak, Yan Liu, Eric Shook, Michael P. Finn, David M. Mattli, and Shaowen Wang (2012).A Performance Profiling Strategy for High-Performance Map Re-Projection of Coarse-Scale Spatial Raster Data.Abstract accepted for presentation at the Auto-Carto 2012, A Cartography and Geographic Information Society Research Symposium, Columbus, OH. • Finn, Michael P., Yan Liu, David M. Mattli, Qingfeng (Gene) Guan, Kristina H. Yamamoto, Eric Shook and Babak Behzad(2012). pRasterBlaster: High-Performance Small-Scale Raster Map Projection Transformation Using the Extreme Science and Engineering Discovery Environment. Abstract accepted for presentation at the XXII International Society for Photogrammetry & Remote Sensing Congress, Melbourne, Australia.  • Finn, Michael P., E. Lynn Usery, and Laura Woodard (2012). Adding a Tutorial to a Map Projections Decision Support System. Paper in work for submission as a U. S. Geological Survey Open-File Report. • Finn, Michael P., Daniel R. Steinwand, Jason R. Trent, E. Lynn Usery, Robert A. Buehler, David Mattli, and Kristina H. Yamamoto (2012). An Implementation of MapImage, a Program for Handling Map Projections of Small Scale Geospatial Raster Data. Paper in revision for the journal Cartographic Perspectives.

16. Plans for Near-Term • Release pRasterBlaster 1.5 • Finish libRasterBlaster • Release dRasterBlaster • Final Decisions on User Processes/ Interface(s)

17. Raster Map Projection Transformation Using a Virtual System to Interactively Share Computing Resources  QUESTIONS? CyberGIS Toolkit Session 1130 – 1230, 06 Aug