GPUs, Cloud and Grids: Distributed Geoprocessing for Speed, Scalability and Better Living

GPUs, Clouds and Grids: Distributed Geoprocessing for Speed, Scalability and Better Living Robert Cheetham 17 February 2011 NC GIS 2011

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Employee-focused Culture
Projects with Social Value

High Performance Geoprocessing

Classic GIS Use Case ...

Close to Center City  Walk to Grocery Store  Nearby Restaurants  Library  Near a Park  Biking / walking distance from our work  Biking distance to fencing club  somewhat important vital very important nice to have somewhat important very important somewhat important Robert’s Rules of Housing

Child Care
Local School Rankings
Farmer's Market
PhillyCarShare
Public Transit
Your Factors might include…

Tax Incentives
Commercial Corridor Health
Public Transit
Car Share
Open Space
Farmers’ Markets
Street Network Density
Recycling Participation
Walkability
Sustainability Factors

Not a new idea… Ian McHarg

Not a new idea … Design with Nature

Not a new idea … Map Algebra

Desktop GIS

x 5 x 2 x 3 x 1 + + + = Generate Output Heat Map

The Web is different from the Desktop
Lots of simultaneous users
Stateless environment
HTML+JS+CSS
Users are less skilled
Users are less patient

ArcGIS Server
Flex, Silverlight and JS API’s
Publish tasks and models
Caching
Optimized MSD files

But wait … there’s a problem
10 – 60 second calculation time
Multiple simultaneous users …
… that are impatient

User Interface version 1

Reports

Sustainable Business Network

Walkability: Walkshed.org

NYC Big Apps Submission

Specific Optimization Goals
New Raster File format
Distributed processing
Binary messaging protocol

Optimization: File Format
Simple - strip out metadata
Limit data type and range
1D arrays are fast to read/write
Assume
Same extent
Same cell size
Same pixel data type
Same cell alignment
Same projection
Azavea Raster Grid (ARG)

Optimization: Distributed Processing
Parallelizable - Local Ops and Focal Ops
Support multiple
Threads
Cores
CPU’s
Machines
Considered
Hadoop
Amazon Map Reduce
Beowolf

Distributed Processing

Binary Messaging Protocol
Started with XML
Binary Protocol Buffer is better
simpler
3 to 10 times smaller
20 to 100 times faster
less ambiguous
a bit easier to use programmatically
Considered
AMF
Google Protocol Buffer

Success!!
Reduced from 10-60 seconds to
<500 milliseconds

Additional [Experimental] Measures
Tiling
Pyramids
EC2 for planned peaks – NYC Big Apps
HTTP file caching - Varnish

Optimizing one process sub-optimizes others
Complex to configure and maintain
One type of operation
No interpolation
No mixing cell sizes
No mixing extents
No mixing projections
No Map Algebra
No ModelBuilder
etc.

High Performance Geoprocessing 2.0
More generic
Cache data – memory is cheaper
New programming technology

OMB Watch: Federal Spending Equity

High Performance Geoprocessing 2.0
Reduced calculation time to
~40ms

GPU geoprocessing research
But wait, there’s more…

GPU geoprocessing research

New languages
CUDA
OpenCL
DirectCompute
Re-write every algorithm
Hardware Diversity
Challenges

We re-wrote a few Map Algebra operations:
Local
Neighborhood
Zonal
Viewshed
etc.
15 – 120x speed improvement
Large grids
Large neighborhoods
Results

Not for the faint of heart

Sea Level Rise

Crime Analysis, Early Warning and Forecasting

Hunch Helper

Risk Forecasting

Animation

Food, Culture and Sustainability

Quick Demo http://commonspace.us

The Future
Clouds of Processors - Google App Engine
Faster is different

Summary
Challenges
Geographic data growth rates are exploding
Size of data sets is growing
Lidar
Raster
Social Media
New form factors that are less powerful
Distributed data sets
Larger numbers of less technical users
New Options
Clouds of processors
Clouds of virtual machines
GPUs

GPUs, Clouds and Grids: Distributed Geoprocessing for Speed, Scalability and Better Living Robert Cheetham 17 February 2011 NC GIS 2011

GPUs, Cloud and Grids: Distributed Geoprocessing for Speed, Scalability and Better Living

by Azavea on Feb 19, 2011

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GPUs, Cloud and Grids: Distributed Geoprocessing for Speed, Scalability and Better Living — Presentation Transcript