1. Rapid Raster Projection Transformation and Web Service Using High-performance Computing Technology
2009 AAG Annual Meeting
Las Vegas, NV
March 25th, 2009
Qingfeng (Gene) Guan
Michael P. Finn
E. Lynn Usery
David M. Mattli
Center of Excellence for Geospatial Information Science
U.S. Geological Survey
Rolla, MO

2. Contents Motivations Parallelizing raster projection transformation
Static load-balancing
Dynamic load-balancing
pRPL – parallel Raster Processing programming Library
Conclusions

3. Motivations Massive data volumes Re-sampling method Example:
High resolutions
Large areas
Easily 500MB+
Re-sampling method
Multiple projection transformations for each output pixel (center and corners)
Example:
Global land-cover at 30-sec resolution, 21,600X43,200 pixels, 900 MB
Geographic → Mollweide
Laptop, Intel Pentinum M 1.5 GHz, 1.25 GB RAM
45 minutes 10 seconds!!

4. Inverse Projection Transformation

5. Motivations
Problem: High computational intensity v.s. demand for rapid projection (web) service
Solution: High-performance computing technologies
Parallel computing

6. Parallel approach for raster data
Raster is born to be parallelized
A raster dataset is essentially a matrix of values, each of which represents the attribute of the corresponding cell of the field
A matrix can be easily partitioned into sub-matrices and assigned onto multiple processors so that the sub-matrices can be processed simultaneously

7. Parallelizing Projection Transformation
Output image is decomposed
Minimum Bounding Rectangles (MBRs) of sub-input-images are computed using the MBRs of sub-output-images

8. Parallelizing Projection Transformation
Static Load-balancing

9. Parallelizing Projection Transformation
Dynamic Load-balancing
Reduced granularity
Master reads sub-input-images and distributes them in response to requests
Workers/Slavers request for new tasks (MBRs of sub-output-images & corresponding sub-input-images)

10. Parallelizing Projection Transformation
Dynamic Load-balancing

11. pRPL: parallel Raster Processing Library
An open-source general-purpose parallel Raster Processing programming Library
Encapsulates complex parallel computing utilities and routines specifically for raster processing
Enables the implementation of parallel raster-processing algorithms without requiring a deep understanding of parallel computing and programming
Possible usage
Massive-volume geographic raster processing
Image (including remote sensing imagery) processing
Cellular Automata (CA) and Agent-based Modeling (ABM)
Freely downloadable and open source

12. pRPL (cont.) Object-Oriented programming style
Written in C++
Built upon the Message Passing Interface (MPI)
Provides Transparent Parallelism
Supports almost all types of raster-based processing
Local-scope
Neighborhood-scope
Regional-scope
Global-scope

13. pRPL 2.0 – under development
Supports dynamic load-balancing for data parallelism
Master-worker formation
Master
Reads data dynamnically
Distributes the initial subsets to the workers
Maintains the task farm which contains the remaining subsets of data
Sends the subsets to the workers in respond to requests
Receives completed output subsets from workers
Workers/Slavers
Initially assigned with some subsets of data
Request for more data when finish the assigned subsets
Receive new input subsets to process from the master
Submit completed output subsets to the master

14. Conclusions
Massive-volume raster projection transformation needs high-performance computing technology
Dynamic load-balancing technique improves performance
Reduces the I/O overhead and the requirement for memery space
Improves the utilitization rate (efficiency) of a heterogenerous parallel computing system
pRPL reduces the devolopment complexity of a parallel raster-based processing

15. Thank You! Questions & Comments?

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