Update README

README

-D2O is a Python module for cluster-distributed multi-dimensional numerical
-arrays. It can be regarded as a layer of abstraction between abstract algorithm
-code and actual data distribution logic. The main goal is to achieve usability
-without losing numerical performance and scalability. Therefore D2O's global
-interface is similar to the one of a numpy.ndarray, whereas the cluster node's
-local data is directly accessible, allowing usage in specialized numerical
-high-performance modules. D2O is written in Python which makes it portable and
-easy to use and modify, whereas expensive operations are carried out by
-dedicated external libraries like numpy and mpi4py. Performance-wise in most of
-the serial cases, D2O is comparable to numpy's ndarray but is superior when
-moving to an MPI cluster.
+D2O - Distributed Data Object
+=============================
+
+Summary
+-------
+
+We introduce D2O, a Python module for cluster-distributed multi-dimensional 
+numerical arrays. It acts as a layer of abstraction between the algorithm code 
+and the data-distribution logic. The main goal is to achieve usability without 
+losing numerical performance and scalability. D2O's global interface is similar
+to the one of a `numpy.ndarray`, whereas the cluster node's local data is 
+directly accessible for use in customized high-performance modules. D2O is 
+written in pure Python which makes it portable and easy to use and modify.
+Expensive operations are carried out by dedicated external libraries like 
+`numpy` and `mpi4py`. The performance of D2O is on a par to numpy for serial 
+applications and scales well when moving to an MPI cluster. D2O is open-source 
+software available under the GNU General Public License v3 (GPL-3).