As a GIS practitioner for over ten years I've been subject to all of the same limitations and headaches as my peers. GIS has some powerful capabilities for analysis and research, but boy can it be slow. Many GIS engines have operations that work in active memory, others are limited to working on single files or databases, and we've all experienced the blasted slowness of it all. So how can we get over this hurdle? We've taken major strides in the digital mapping field in the last several years - tiled map interfaces are commonplace online now, and the user experience is far better than the first generation mapping services; analytically we're doing more every day. ESRI's toolbox capabilty in the 9.x series allows them and others to develop special purpose toolsets that perform almost any spatial operation known. But how can we do more. Every GIS practitioner I know wishes that we could perform all these calculations on more and more data at faster and faster speeds. As GIS capability grows, so does the demand for higher resolution data. As a result, we've been at a standstill in terms of processing speed for 5 years or so.
So what's the answer. In the data processing community at large, the answer has clearly been parallel processing of data. When more than one processor can work on a part of the problem in isolation, and then each contributes to the answer, huge speed increases can be gained. These technologies come in many different flavors. First, there's the multi-core, multi-processor architecture. Almost every new computer sold today comes with a
dual core processor, and possibly several of them. In this way, a high-end desktop workstation may have 8 or so processors. Another paradigm is to distribute a job across many computers or nodes, in a
cluster of computers. These clusters are usually co-located and are wired on the same sub-network and are dedicated to processign large complex jobs for users. This is related, but not exactly the same as what many are calling
cloud computing these days, where computers of many different forms are linked together via various services to perfom an overall task or form a c

omposite application. A fourth model for parallel processing is using the
graphics processor on high end graphics cards to perform general purpose computing tasks. This is handy because graphics processors are very specialized and blazing fast for the right tasks. Not every job can be performed on the GPU, but when it can, it's flipping fast, as this
study from the University of Virginia shows. The graphics hardware company
nVidia has even produced several models of hardware add-ons that contain multiple GPUs and are designed to use simple software libraries to make use of this specialized hardware in more generalized ways. Their
Tesla high performance computing product line is another way to get involved in HPC, and is actually the best performance per dollar solution out there.
So, we have some examples of getting more work done in the same amount of time. Who is stepping up to transition this to the geospatial analysis field? It turns out that there are a few pockets of innovators who are migrating geo-processing and analysis to some HPC platforms.
Manifold is the only full featured GIS so far to include support for nVidia's graphics processors. It can perform many raster data transforms and processing like hill shading, slope,

and aspect using the computer's GPU for the bulk of the work in much less time. Most GIS workstations already have high end graphics cards, so this is a very logical and promising direction for GIS to go, and I'm glad to see Manifold taking the lead in this.
For distributed computing, there are a few projects that are making use of multiple connected servers for analyzing expansive spatial data sets. Parallel computing for spatial data is nothing new. Specialized applications for analyzing weather data and processing imagery are well known and proven at this point. But those are specialized applications. Is anyone using parallel architectures for
general purpose GIS? I'll keep this post updated as I find them. Post links in the comments section if you know of any ground-breaking work in this area. This technology has much promise in the coming years.