We live in exciting times.

Computing power continues to grow at an exponential rate, and is well characterized by Moore’s Law (if you are looking for a graph more recent than 1965, try Wikipedia).  This means that computing power is moving in many directions.  The rise of laptops, notebooks, tablets, and smartphones are a testament to the increasing computing power of microprocessors.  They are getting faster, smaller, lighter, more power efficient, and sprouting more cores.

Despite this accelerating computing power, however, on some of our projects, we’ve seen how many heavy-duty analytical computing tasks remain too costly (in terms of computing time) to be run on the web with more than a small number of users.  However, by distributing the computation across multiple processors and machines, we have found it is possible to improve both the scalability and speed of some geographic data processing tasks.  For one such task, a weighted raster overlay operationg, we have been able to accelerate the process enough to make a scalable web application possible.  Azavea’s DecisionTree framework, developed with support from an SBIR grant from the US Department of Agriculture.

With this experience developing distributing geoprocessing algorithms, we have recently been taking a look at technologies that will enable us to make similar types of performance and scalability improvements.  One technology that we believe has great promise for bringing these processes to the web is General Purpose Computing on the Graphics Processing Unit (GPGPU).

GPGPU leverages the microprocessors that power many modern graphics cards.  NVidia and ATI are the largest players in the high performance video adapter field, and they both have GPU computing libraries that run on their video adapter hardware.

GPU’s are accelerating everything.

GPU’s are powerful for general purpose computing not just because of their clock speed, but because there are just so many multiprocessors on today’s GPU graphics cards.  While a quad-core CPU is a high-end processor for most servers, today’s high-end graphics cards have 100, 200 and 500 or more cores and are capable of gigaFLOPS double precision processing power (NVidia, ATI, respectively).  And these numbers are doing nothing but going up.

A few ways of comparing just what that means:

• a handheld calculator runs at about 10 FLOPS (not giga-, just plain FLOPS, one billionth of a gigaFLOP).
• by the time you blink your eye, 154 gigaFLOPS have occurred on the NVidia Tesla C2070.
• by the time a hummingbird flaps it’s wings, 10.3 gigaFLOPS have occurred on the same card.
• by the time one FLOP has occurred on the same card, your voice has only traveled through 0.64 μm of air (human hair ranges from 17-181 μm thick)

In addition to processors and processing speed, GPU cards have fast, specialized memory access.  They have a limited amount of local memory, but if you can figue out a way to use it efficiently, your memory access is on the order of 100x faster than conventional memory.

The combination of more processors and faster memory means that if you can discretize or parallelize the type of work that you want to perform, you can get radical speed improvements.

GIS on the GPU.

That’s all well and good, but how can GPGPU be used for GIS?  We are not the only ones thinking about this, but the answer depends on what kind of analysis you want to do.  We have been focusing our research on a few types of MapAlgebra operations, and our preliminary investigations have shown that all types of MapAlgebra operations can benefit from processing on the GPU.  In addition, we believe substantial improvements can be made in some types of vector processing with a few likely candidates would be:

• Vector-to-raster and raster-to-vector conversion
• Network analysis
• Network routing
• Transformations of geometric collections

All of these optimizations have the potential of reducing the computing time for heavy duty GIS operations from hours to minutes, and therefore minutes to seconds.  With that kind of speedup, the “attention threshold” of the web can be achieved.  It now becomes possible to run more complex GIS tasks in a web environment, bringing more computing power to the masses.

These changes won’t change the world right away, but it will make GIS analysis more interactive, responsive, and efficient.  Just imagine if you could complete any given task in your day in 1/10th the time (think Dash, from the Incredibles).