Add README

README.rst

+Loopy lets you easily generate the tedious, complicated code that is necessary
+to get good performance out of GPUs and multi-core CPUs.
+
+Loopy's core idea is that a computation should be described simply and then
+*transformed* into a version that gets high performance. This transformation
+takes place under user control, from within Python.
+
+It can capture the following types of optimizations:
+
+* Vector and multi-core parallelism in the OpenCL/CUDA model
+* Data layout transformations (structure of arrays to array of structures)
+* Loopy Unrolling
+* Loop tiling with efficient handling of boundary cases
+* Prefetching/copy optimizations
+* Instruction level parallelism
+* and many more
+
+Loopy targets array-type computations, such as the following:
+
+* dense linear algebra,
+* convolutions,
+* n-body interactions,
+* PDE solvers, such as finite element, finite difference, and
+  Fast-Multipole-type computations
+
+It is not (and does not want to be) a general-purpose programming language.
+
+Loopy is licensed under the liberal `MIT license
+<http://en.wikipedia.org/wiki/MIT_License>`_ and free for commercial, academic,
+and private use. To use Loopy, you just need `pyopencl
+<http://mathema.tician.de/software/pyopencl>`_.
+
+Places on the web related to Loopy:
+
+* `Python package index <http://pypi.python.org/pypi/loopy`_ (download releases) Note the extra '.' in the PyPI identifier!
+
+* `Documentation <http://documen.tician.de/loopy>`_ (read how things work)
+  .. image:: https://badge.fury.io/py/loo.py.png
+      :target: http://pypi.python.org/pypi/loo.py
+* `Github <http://github.com/pyopencl/loopy`_ (get latest source code, file bugs)
+* `Wiki <http://wiki.tiker.net/Loopy`_ (read installation tips, get examples, read FAQ)