High-Performance Small-Scale Raster Map Projection Empowered by Cyberinfrastructure

This chapter reports on the merging of geospatial data transformation, high-performance computing (HPC), and cyberinfrastructure (CI) domains for map projection transformation through performance profiling and tuning of pRasterBlaster, a parallel map projection transformation program. pRasterBlaster is built on the desktop version of mapIMG. Profiling was employed in an effort to identify and resolve computational bottlenecks that could prevent the program from scaling to thousands of processors for map projection on large raster datasets. Performance evaluation of a parallel program is critical to achieving projection transformation as factors such as the number of processors, overhead of communications, and input/output (I/O) all contribute to efficiency in an HPC environment. Flaws in the workload distribution algorithm, in this reported work, could hardly be observed when the number of processors was small. Without being exposed to large-scale supercomputers through software integration efforts, such flaws might remain unidentified. Overall, the two computational bottlenecks highlighted in this chapter, workload distribution and data-dependent load balancing, showed that in order to produce scalable code, profiling is an important process and scaling tests are necessary to identify bottlenecks that are otherwise difficult to discover.