docs init supported, numpy

Author: Ehsan Totoni

docs/source/supported.rst

@@ -3,4 +3,89 @@
 Supported Python Programs
 =========================
 
-The `@hpat.jit` decorator...
+HPAT compiles and parallelizes the functions annotated with the `@hpat.jit`
+decorator. Therefore, file I/O and computations on large datasets should be
+inside the jitted functions. The supported data structures for large datasets
+are `Numpy <http://www.numpy.org/>`_ arrays and
+`Pandas <http://pandas.pydata.org/>`_ dataframes.
+
+Enabling Parallelization
+------------------------
+
+To enable parallelization, HPAT needs to recognize the large datasets and their
+computation in the program. Hence, only the supported data-parallel operators of
+Numpy and Pandas can be used for large datasets and computations. The sequential
+computation on small data can be any code that
+`Numba supports <http://numba.pydata.org/numba-doc/latest/index.html>`_.
+
+Supported Numpy Functions
+-------------------------
+
+Below is the list of the data-parallel Numpy operators that HPAT supports.
+
+1. Numpy `element-wise` or `point-wise` array operations:
+
+    * Unary operators: ``+`` ``-`` ``~``
+    * Binary operators: ``+`` ``-`` ``*`` ``/`` ``/?`` ``%`` ``|`` ``>>`` ``^``
+        ``<<`` ``&`` ``**`` ``//``
+    * Comparison operators: ``==`` ``!=`` ``<`` ``<=`` ``>`` ``>=``
+    * data-parallel math operations: ``add``, ``subtract``, ``multiply``,
+        ``divide``, ``logaddexp``, ``logaddexp2``, ``true_divide``,
+        ``floor_divide``, ``negative``, ``power``, ``remainder``,
+        ``mod``, ``fmod``, ``abs``, ``absolute``, ``fabs``, ``rint``, ``sign``,
+        ``conj``, ``exp``, ``exp2``, ``log``, ``log2``, ``log10``, ``expm1``,
+        ``log1p``, ``sqrt``, ``square``, ``reciprocal``, ``conjugate``
+    * Trigonometric functions: ``sin``, ``cos``, ``tan``, ``arcsin``,
+        ``arccos``, ``arctan``, ``arctan2``, ``hypot``, ``sinh``, ``cosh``,
+        ``tanh``, ``arcsinh``, ``arccosh``, ``arctanh``, ``deg2rad``,
+        ``rad2deg``, ``degrees``, ``radians``
+    * Bit manipulation functions: ``bitwise_and``, ``bitwise_or``,
+        ``bitwise_xor``, ``bitwise_not``, ``invert``, ``left_shift``,
+        ``right_shift``
+
+2. Numpy reduction functions ``sum`` and ``prod``.
+
+3. Numpy array creation functions ``zeros``, ``ones``
+
+4. Random number generator functions: ``rand``, ``randn``,
+    ``ranf``, ``random_sample``, ``sample``, ``random``,
+    ``standard_normal``, ``chisquare``, ``weibull``, ``power``, ``geometric``,
+    ``exponential``, ``poisson``, ``rayleigh``, ``normal``, ``uniform``,
+    ``beta``, ``binomial``, ``f``, ``gamma``, ``lognormal``, ``laplace``,
+    ``randint``, ``triangular``.
+
+4. Numpy ``dot`` function between a matrix and a vector, or two vectors.
+
+Optional arguments are not supported unless if explicitly mentioned here.
+For operations on multi-dimensional arrays, automatic broadcast of
+dimensions of size 1 is not supported.
+
+
+Explicit Parallel Loops
+-----------------------
+
+Sometimes a program cannot be written in terms of data-parallel operators easy
+and explicit parallel loops are required.
+In this case, one can use HPAT's ``prange`` instead of ``range`` to specify that a
+loop can be parallelized. The user is required to make sure that the loop does
+not have cross iteration dependencies except the supported reductions.
+Currently, only sum using the ``+=`` operator is supported.
+The example below demonstrates a parallel loop with a
+reduction::
+
+    from HPAT import jit, prange
+    @jit
+    def prange_test(n):
+        A = np.random.ranf(n)
+        s = 0
+        for i in prange(len(A)):
+            s += A[i]
+        return s
+
+Supported Pandas Functions
+--------------------------
+
+File I/O
+--------
+
+Currently, HPAT only supports I/O for the `HDF5 <http://www.h5py.org/>`_ format.