DOC: Add a webpage that describes the loop and bb vectorizers.

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+==========================
+Auto-Vectorization in LLVM
+==========================
+
+LLVM has two vectorizers: The *Loop Vectorizer*, which operates on Loops,
+and the *Basic Block Vectorizer*, which optimizes straight-line code. These
+vectorizers focus on different optimization opportunities and use different
+techniques. The BB vectorizer merges multiple scalars that are found in the
+code into vectors while the Loop Vectorizer widens instructions in the
+original loop to operate on multiple consecutive loop iterations.
+
+The Loop Vectorizer
+===================
+
+LLVM’s Loop Vectorizer is now available and will be useful for many people.
+It is not enabled by default, but can be enabled through clang using the
+command line flag:
+
+.. code-block:: console
+
+   $ clang -fvectorize file.c
+
+We plan to enable the Loop Vectorizer by default as part of the LLVM 3.3 release.
+
+Features
+^^^^^^^^^
+
+The LLVM Loop Vectorizer has a number of features that allow it to vectorize
+complex loops.
+
+Loops with unknown trip count
+------------------------------
+
+The Loop Vectorizer supports loops with an unknown trip count.
+In the loop below, the iteration ``start`` and ``finish`` points are unknown,
+and the Loop Vectorizer has a mechanism to vectorize loops that do not start
+at zero. In this example, ‘n’ may not be a multiple of the vector width, and
+the vectorizer has to execute the last few iterations as scalar code. Keeping
+a scalar copy of the loop increases the code size.
+
+.. code-block:: c++
+
+  void bar(float *A, float* B, float K, int start, int end) {
+   for (int i = start; i < end; ++i)
+     A[i] *= B[i] + K;
+  }
+
+Runtime Checks of Pointers
+--------------------------
+
+In the example below, if the pointers A and B point to consecutive addresses,
+then it is illegal to vectorize the code because some elements of A will be
+written before they are read from array B.
+
+Some programmers use the 'restrict' keyword to notify the compiler that the
+pointers are disjointed, but in our example, the Loop Vectorizer has no way of
+knowing that the pointers A and B are unique. The Loop Vectorizer handles this
+loop by placing code that checks, at runtime, if the arrays A and B point to
+disjointed memory locations. If arrays A and B overlap, then the scalar version
+of the loop is executed. 
+
+.. code-block:: c++
+
+  void bar(float *A, float* B, float K, int n) {
+   for (int i = 0; i < n; ++i)
+     A[i] *= B[i] + K;
+  }
+
+
+Reductions
+--------------------------
+
+In this example the ``sum`` variable is used by consecutive iterations of 
+the loop. Normally, this would prevent vectorization, but the vectorizer can
+detect that ‘sum’ is a reduction variable. The variable ‘sum’ becomes a vector
+of integers, and at the end of the loop the elements of the array are added
+together to create the correct result. We support a number of different 
+reduction operations, such as addition, multiplication, XOR, AND and OR.
+
+.. code-block:: c++
+
+  int foo(int *A, int *B, int n) {
+    unsigned sum = 0;
+    for (int i = 0; i < n; ++i)
+        sum += A[i] + 5;
+    return sum;
+  }
+
+Inductions
+--------------------------
+
+In this example the value of the induction variable ``i`` is saved into an
+array. The Loop Vectorizer knows to vectorize induction variables.
+
+.. code-block:: c++
+
+  void bar(float *A, float* B, float K, int n) {
+   for (int i = 0; i < n; ++i)
+     A[i] = i;
+  }
+
+If Conversion
+--------------------------
+
+The Loop Vectorizer is able to "flatten" the IF statement in the code and
+generate a single stream of instructions. The Loop Vectorizer supports any
+control flow in the innermost loop. The innermost loop may contain complex
+nesting of IFs, ELSEs and even GOTOs.
+
+.. code-block:: c++
+
+  int foo(int *A, int *B, int n) {
+    unsigned sum = 0;
+    for (int i = 0; i < n; ++i)
+      if (A[i] > B[i])
+        sum += A[i] + 5;
+    return sum;
+  }
+
+Pointer Induction Variables
+--------------------------
+
+This example uses the "accumulate" function of the standard c++ library. This
+loop uses C++ iterators, which are pointers, and not integer indices.
+The Loop Vectorizer detects pointer induction variables and can vectorize
+this loop. This feature is important because many C++ programs use iterators.
+
+.. code-block:: c++
+
+  int baz(int *A, int n) {
+    return std::accumulate(A, A + n, 0);
+  }
+
+Reverse Iterators
+--------------------------
+
+The Loop Vectorizer can vectorize loops that count backwards.
+
+.. code-block:: c++
+
+  int foo(int *A, int *B, int n) {
+    for (int i = n; i > 0; --i)
+      A[i] +=1;
+  }
+
+Scatter / Gather
+--------------------------
+
+The Loop Vectorizer can generate code diverging memory indices that result in
+scatter/gather memory accesses.
+
+.. code-block:: c++
+
+  int foo(int *A, int *B, int n, int k) {
+  for (int i = 0; i < n; ++i)
+      A[i*7] += B[i*k];
+  }
+
+Vectorization of programs with Mixed Types
+--------------------------
+
+The Loop Vectorizer can vectorize programs with mixed types. The Vectorizer
+cost model can estimate the cost of the type conversion and decide if
+vectorization is profitable.
+
+.. code-block:: c++
+
+  int foo(int *A, char *B, int n, int k) {
+  for (int i = 0; i < n; ++i)
+      A[i] += 4 * B[i];
+  }
+
+Vectorization of function calls
+--------------------------
+
+The Loop Vectorize can vectorize intrinsic math functions.
+See the table below for a list of these functions.
+
++-----+-----+---------+
+| pow | exp |  exp2   |
++-----+-----+---------+
+| sin | cos |  sqrt   |
++-----+-----+---------+
+| log |log2 |  log10  |
++-----+-----+---------+
+|fabs |floor|  ceil   |
++-----+-----+---------+
+|fma  |trunc|nearbyint|
++-----+-----+---------+
+
+The Basic Block Vectorizer
+==========================
+
+The Basic Block Vectorizer is not enabled by default, but it can be enabled
+through clang using the command line flag:
+
+.. code-block:: console
+
+   $ clang -fslp-vectorize file.c 
+
+The goal of basic-block vectorization (a.k.a. superword-level parallelism) is
+to combine similar independent instructions within simple control-flow regions
+into vector instructions. Memory accesses, arithemetic operations, comparison
+operations and some math functions can all be vectorized using this technique
+(subject to the capabilities of the target architecture). 
+
+For example, the following function performs very similar operations on its
+inputs (a1, b1) and (a2, b2). The basic-block vectorizer may combine these
+into vector operations.
+
+.. code-block:: c++
+
+  int foo(int a1, int a2, int b1, int b2) {
+    int r1 = a1*(a1 + b1)/b1 + 50*b1/a1;
+    int r2 = a2*(a2 + b2)/b2 + 50*b2/a2;
+    return r1 + r2;
+  }
+
+