  Many types of geospatial analyses are computationally complex, involving, for
example, solution processes that require numerous iterations or combinatorial
comparisons. This complexity has motivated the application of high performance
computing (HPC) to a variety of geospatial problems. In many instances, HPC
assumes even greater importance because complexity interacts with rapidly
growing volumes of geospatial information to further impede analysis and
display. This chapter briefly reviews the underlying need for HPC in geospatial
applications and describes different approaches to past implementations. Many
of these applications were developed using hardware systems that had a
relatively short life-span and were implemented in software that was not easily
portable. More promising recent approaches have turned to the use of
distributed resources that includes cyberinfrastructure as well as cloud and
fog computing.
