Merge pull request #491 from enthought/feature/release-0.4-notes

Draft release notes for 0.4.

Author: bgrant

docs/source/releases/release-0.4.rst

@@ -0,0 +1,126 @@
+==============================================================================
+DistArray 0.4 development release
+==============================================================================
+
+**Documentation:** http://distarray.readthedocs.org
+
+**License:** Three-clause BSD
+
+**Python versions:** 2.7 and 3.3
+
+**OS support:** \*nix and Mac OS X
+
+What is DistArray?
+------------------
+
+DistArray aims to bring the strengths of NumPy to data-parallel
+high-performance computing. It provides distributed multi-dimensional
+NumPy-like arrays and distributed ufuncs, distributed IO capabilities, and can
+integrate with external distributed libraries like Trilinos. DistArray works
+with NumPy and builds on top of it in a flexible and natural way.
+
+0.4 Release
+-----------
+
+This is the third development release. 
+
+Noteworthy improvements in 0.4 include:
+
+* basic slicing support;
+* significant performance enhancements;
+* reduction methods now support boolean arrays;
+* an IPython notebook that demos basic functionality; and
+* many bug fixes, API improvements, and refactorings.
+
+DistArray is nearly ready for real-world use.  The project is evolving rapidly
+and input from the larger scientific-Python community is very valuable and
+helps drive development.
+
+Existing features
+-----------------
+
+DistArray:
+
+* has a client-engine (or master-worker) process design -- data resides on the
+  worker processes, and 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;
+* supports NumPy-like multidimensional arrays;
+* 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:
+
+* MPI-only communication for performance and deployment on clusters and
+  supercomputers;
+* array re-distribution capabilities;
+* interoperation with Trilinos [Trilinos]_;
+* expanded tutorials, examples, and other introductory material; and
+* distributed broadcasting support.
+
+The longer-term roadmap includes:
+
+* Lazy evaluation and deferred computation for latency hiding;
+* 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