Machine Learning Using Clusters of Computers

Machine learning using large data sets is a computationally intensive process. One technique that offers an inexpensive opportunity to reduce the wall time for machine learning is to perform the learning in parallel. Raising the level of abstraction of the parallelization to the application level allows existing learning algorithms to be used without modification while providing the potential for a significant reduction in wall time. An MPI-shell is needed to partition and distribute the data to accommodate this higher level of abstraction. Executing this shell on a cluster of computers offers the potential for significant speedups with respect to processors as well as parallel I/O. A method for combining the results (obtained by applying the learning to each partition of the data) must be identified and require minimal time with respect to the training time of a partition of data.