python-ipyparallel-4.1.0-1.lbn19.noarch

IPyParallel is IPython’s sophisticated and powerful architecture for parallel and distributed computing. This architecture abstracts out parallelism in a very general way, which enables IPython to support many different styles of parallelism including:

Single program, multiple data (SPMD) parallelism. Multiple program, multiple data (MPMD) parallelism. Message passing using MPI. Task farming. Data parallel. Combinations of these approaches. Custom user defined approaches.

Most importantly, IPython enables all types of parallel applications to be developed, executed, debugged and monitored interactively. Hence, the I in IPython. The following are some example usage cases for IPython:

Quickly parallelize algorithms that are embarrassingly parallel using a number of simple approaches. Many simple things can be parallelized interactively in one or two lines of code. Steer traditional MPI applications on a supercomputer from an IPython session on your laptop. Analyze and visualize large datasets (that could be remote and/or distributed) interactively using IPython and tools like matplotlib/TVTK. Develop, test and debug new parallel algorithms (that may use MPI) interactively. Tie together multiple MPI jobs running on different systems into one giant distributed and parallel system. Start a parallel job on your cluster and then have a remote collaborator connect to it and pull back data into their local IPython session for plotting and analysis. Run a set of tasks on a set of CPUs using dynamic load balancing.