1. Modeling Big Data Execution speed limited by: –Model complexity –Software Efficiency –Spatial and temporal extent and resolution –Data size & access speed –Hardware performance

2. Combinatorials If it takes 1 hour to run processing on Humboldt County: 10,495 km² –Entire US: 39 days

3. Vector or Array Computing Super computers of the 60’s, 70’s, and 80’s Harvard Architecture: –Separate program and data –2^n Processors execute the same program on different data Vector arithmetic Limited flexibility

4. Instructions and data share memory More flexible Allows for one task to be divided into many processes and executed individually CPU Von Neumann Architecture ALU Cache RAM I/O InstructionsData

5. Applications and Services

6. Multiprocessing Multiple processors (CPUs) working on the same task Processes –Applications: have a UI –Services: Run in background –Can be executed individually –See “Task Manager” Processes can have multiple “threads”

7. Processes

8. Threads A process can have lots of threads –ArcGIS now can have 2, one for the GUI and one for a geoprocessing task Obtain a portion of the CPU cycles Must “sleep” or can lockup Share access to memory, disk, I/O

9. Distributed Processing Task must be broken up into processes that can be run independently or sequentially Typically: –Command line-driven –Scripts or compiled programs –R, Python, C++, Java, PHP, etc.

10. Distributed Processing Grid – distributed computing Beowulf – lots of simple computer boards (motherboards) Condor – software to share free time on computers “The Cloud?” – web-based “services”. Should allow submittal of processes in the future.

11. Trends Processors are not getting faster The internet is not getting faster RAM continues to decrease in price Hard discs continue to increase in size –Solid State Drives available Number of “Cores” continues to increase

12. Future Computers? 128k cores, lots of “cache” Multi-terabyte RAM Terabyte SSD Drives 100s of terabyte hard discs? Allows for: –Large datasets in RAM (multi-terabyte) –Event larger datasets on “hard disks” –Lots of tasks to run simultaneously

13. Reality Check Whether through local processing or distributed processing: –We will need to “parallelize” spatial analysis in the future to manage: Larger datasets Larger modeling extends and finer resolution Move complex models Desire: –Break-up processing into “chunks” that can be each executed somewhat independently of each other

14. Challenge Having all the software you need on the computer you are executing the task on –Virtual Application: Entire computer disk image sent to another computer –All required software installed. Often easier to manage your own cluster –Programs installed “once” –Shared hard disc access –Communication between threads

15. Software ArcGIS: installation, licensing, processing makes it almost impossible to use Quantum, GRASS: installation make it challenging FWTools, C++ applications, Use standard language libraries and functions to avoid compatibility problems

16. Data Issues Break data along natural lines: –Different species –Different time slices Window spatial data –Oversized Vector data: size typically not an issue Raster data: size is an issue

17. Windowing Spatial Data Raster arithmetic is natural –Each pixel result is only dependent on one pixel in the source raster 12 23 129 1310 1311 1513 + =

18. Windowing Spatial Data N x N filters: –Needs to use oversized windows 1220233440 1523303139 1522293040 1420282938 1319253237 Columns Rows

19. Windowing Spatial Data Others are problematic: –Viewsheds –Stream networks –Spatial simulations ScienceDirect.com