XFAIL this test on mingw.

[oota-llvm.git] / docs / Vectorizers.rst

diff --git a/docs/Vectorizers.rst b/docs/Vectorizers.rst
index d565c2122c9a0be711abf71411ec2c4f5fa28b9c..221fb2949f8124f4a8cd883c35e788296d348d84 100644 (file)
--- a/docs/Vectorizers.rst
+++ b/docs/Vectorizers.rst
@@ -7,11 +7,11 @@ Auto-Vectorization in LLVM
 
 LLVM has two vectorizers: The :ref:`Loop Vectorizer <loop-vectorizer>`,
 which operates on Loops, and the :ref:`SLP Vectorizer
 
 LLVM has two vectorizers: The :ref:`Loop Vectorizer <loop-vectorizer>`,
 which operates on Loops, and the :ref:`SLP Vectorizer

-<slp-vectorizer>`, which optimizes straight-line code. These vectorizers
+<slp-vectorizer>`. These vectorizers

 focus on different optimization opportunities and use different techniques.
 The SLP vectorizer merges multiple scalars that are found in the code into
 focus on different optimization opportunities and use different techniques.
 The SLP 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.
+vectors while the Loop Vectorizer widens instructions in loops
+to operate on multiple consecutive iterations.

 
 .. _loop-vectorizer:
 
 
 .. _loop-vectorizer:
 


@@ -302,10 +302,9 @@ Details
 -------
 
 The goal of SLP vectorization (a.k.a. superword-level parallelism) is
 -------
 
 The goal of SLP 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).
+to combine similar independent instructions
+into vector instructions. Memory accesses, arithmetic operations, comparison
+operations, PHI-nodes, can all be vectorized using this technique.

 
 For example, the following function performs very similar operations on its
 inputs (a1, b1) and (a2, b2). The basic-block vectorizer may combine these
 
 For example, the following function performs very similar operations on its
 inputs (a1, b1) and (a2, b2). The basic-block vectorizer may combine these


@@ -318,8 +317,7 @@ into vector operations.
     A[1] = a2*(a2 + b2)/b2 + 50*b2/a2;
   }
 
     A[1] = a2*(a2 + b2)/b2 + 50*b2/a2;
   }
 

-The SLP-vectorizer has two phases, bottom-up, and top-down. The top-down vectorization
-phase is more aggressive, but takes more time to run.
+The SLP-vectorizer processes the code bottom-up, across basic blocks, in search of scalars to combine.

 
 Usage
 ------
 
 Usage
 ------