Applications for this toolbox are found in scientific computing.

pMapper finds efficient maps – in other words, it automatically assigns blocks of data to processing elements. The toolbox requires an underlying global array library or library that implements operations on parallel arrays. pMapper uses a two-phase mapping architecture: first, it collects data about the performance of the algorithm. This timing data is then used to generate maps for how to assign arrays to different processors. pMapper uses ‘lazy evaluation’, meaning that it delays execution until necessary. This approach enables pMapper to have the greatest possible amount of information about the program. pMapper also uses global optimization to ensure that the entire algorithm is running as efficiently as possible, rather than any individual component.  pMapper has been tested on many benchmark algorithms and achieved speedup factors between 2.6 and 17, dramatically increasing algorithm performance. By improving efficiency without requiring detailed changes to the code, this toolbox will both improve performance and save time for software engineers.