Think globally, act locally.
DistArray provides general multidimensional NumPy-like distributed arrays to
Python. It intends to bring the strengths of NumPy to data-parallel
high-performance computing. DistArray has a similar API to NumPy.
DistArray is ready for real-world testing and deployment; however, the project
is still evolving rapidly, and we appreciate continued input from the
DistArray is for users who
- know and love Python and NumPy,
- want to scale NumPy to larger distributed datasets,
- want to interactively play with distributed data but also
- want to run batch-oriented distributed programs;
- want an easier way to drive and coordinate existing MPI-based codes,
- have a lot of data that may already be distributed,
- want a global view (“think globally”) with local control (“act locally”),
- need to tap into existing parallel libraries like Trilinos, PETSc, or
- want the interactivity of IPython and the performance of MPI.
DistArray is designed to work with other packages that implement the
Distributed Array Protocol.
Please see our documentation at readthedocs (or in the docs directory) for
more, or ask us questions on our mailing list. Pull requests gladly accepted.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.