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 scientific-Python community.
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 Elemental,
- want the interactivity of IPython and the performance of MPI.
DistArray is designed to work with other packages that implement the Distributed Array Protocol.