  The map-reduce parallel programming model has become extremely popular in the
big data community. Many big data workloads can benefit from the enhanced
performance offered by supercomputers. LLMapReduce provides the familiar
map-reduce parallel programming model to big data users running on a
supercomputer. LLMapReduce dramatically simplifies map-reduce programming by
providing simple parallel programming capability in one line of code.
LLMapReduce supports all programming languages and many schedulers. LLMapReduce
can work with any application without the need to modify the application.
Furthermore, LLMapReduce can overcome scaling limits in the map-reduce parallel
programming model via options that allow the user to switch to the more
efficient single-program-multiple-data (SPMD) parallel programming model. These
features allow users to reduce the computational overhead by more than 10x
compared to standard map-reduce for certain applications. LLMapReduce is widely
used by hundreds of users at MIT. Currently LLMapReduce works with several
schedulers such as SLURM, Grid Engine and LSF.
