Add 0.6 release notes.

docs/sphinx/source/releases/release-0.6.rst

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+==============================================================================
+DistArray 0.6 release
+==============================================================================
+
+**Mailing List:** distarray@googlegroups.com
+
+**Documentation:** http://distarray.readthedocs.org
+
+**License:** Three-clause BSD
+
+**Python versions:** 2.7, 3.4, and 3.5
+
+**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.
+
+0.6 Release
+-----------
+
+Noteworthy improvements in this release include:
+
+* a new website, (http://docs.enthought.com/distarray/), with links to
+  DistArray talks and presentations,
+* redistribution for block-distributed (and non-distributed) DistArrays,
+* experimental "quickstart" installation scripts,
+* an easier API for ``Context.apply``
+* expanded examples (parallel Gaussian Elimination by Prashant Mital)
+* compatibility with NumPy 1.9,
+* expanded TravisCI testing on (OS X and Python 3.5),
+* logos by Erick Michaud, and
+* bug-fixes and code improvements
+
+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
+----------------
+
+Planned features include
+
+* array re-distribution capabilities for more distribution types;
+* lazy evaluation and deferred computation for latency hiding;
+* examples of interoperation with Trilinos [Trilinos]_;
+* distributed broadcasting support.
+* 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
+
+.. vim:spell