=============================================== DistArray 0.5 release ===============================================
**Mailing list:** distarray@googlegroups.com
**Documentation:** http://distarray.readthedocs.org
**License:** Three-clause BSD
**Python versions:** 2.7, 3.3, and 3.4
**OS support:** *nix and Mac OS X
What is DistArray? ------------------
DistArray aims to bring the ease-of-use of NumPy to data-parallel high-performance computing. It provides distributed multi-dimensional NumPy arrays, distributed ufuncs, and distributed IO capabilities. It can efficiently interoperate with external distributed libraries like Trilinos. DistArray works with NumPy and builds on top of it in a flexible and natural way.
0.5 Release -----------
Noteworthy improvements in this release include:
* closer alignment with NumPy's API, * support for Python 3.4 (existing support for Python 2.7 and 3.3), * a performance-oriented MPI-only mode for deployment on clusters and supercomputers, * a way to register user-defined functions to be callable locally on worker processes, * more consistent naming of sub-packages, * testing with MPICH2 (already tested against OpenMPI), * improved and expanded examples, * installed version testable via ``distarray.test()``, and * performance and scaling improvements.
With this release, DistArray ready for real-world testing and deployment. The project is still evolving rapidly and we appreciate the continued input from the larger scientific-Python community.
Existing features -----------------
DistArray:
* supports NumPy-like slicing, reductions, and ufuncs on distributed multidimensional arrays; * has a client-engine process design -- data resides on the worker processes, commands are initiated from master; * allows full control over what is executed on the worker processes and integrates transparently with the master process; * allows direct communication between workers, bypassing the master process for scalability; * integrates with IPython.parallel for interactive creation and exploration of distributed data; * supports distributed ufuncs (currently without broadcasting); * builds on and leverages MPI via MPI4Py in a transparent and user-friendly way; * has basic support for unstructured arrays; * supports user-controllable array distributions across workers (block, cyclic, block-cyclic, and unstructured) on a per-axis basis; * has a straightforward API to control how an array is distributed; * has basic plotting support for visualization of array distributions; * separates the array’s distribution from the array’s data -- useful for slicing, reductions, redistribution, broadcasting, and other operations; * implements distributed random arrays; * supports ``.npy``-like flat-file IO and hdf5 parallel IO (via ``h5py``); leverages MPI-based IO parallelism in an easy-to-use and transparent way; and * supports the distributed array protocol [protocol]_, which allows independently developed parallel libraries to share distributed arrays without copying, analogous to the PEP-3118 new buffer protocol.
Planned features and roadmap ----------------------------
Near-term features and improvements include:
* array re-distribution capabilities; * lazy evaluation and deferred computation for latency hiding; * interoperation with Trilinos [Trilinos]_; and * distributed broadcasting support.
The longer-term roadmap includes:
* Integration with other packages [petsc]_ that subscribe to the distributed array protocol [protocol]_; * Distributed fancy indexing; * Out-of-core computations; * Support for distributed sorting and other non-trivial distributed algorithms; and * End-user control over communication and temporary array creation, and other performance aspects of distributed computations.
History and funding -------------------
Brian Granger started DistArray as a NASA-funded SBIR project in 2008. Enthought picked it up as part of a DOE Phase II SBIR [SBIR]_ to provide a generally useful distributed array package. It builds on NumPy, MPI, MPI4Py, IPython, IPython.parallel, and interfaces with the Trilinos suite of distributed HPC solvers (via PyTrilinos [Trilinos]_).
This material is based upon work supported by the Department of Energy under Award Number DE-SC0007699.
This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
.. [protocol] http://distributed-array-protocol.readthedocs.org/en/rel-0.10.0/ .. [Trilinos] http://trilinos.org/ .. [petsc] http://www.mcs.anl.gov/petsc/ .. [SBIR] http://www.sbir.gov/sbirsearch/detail/410257