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docs: Cleanup trailing whitespace.

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Sean Silva 2012-12-20 22:59:36 +00:00
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docs/Vectorizers.rst
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@ -70,7 +70,7 @@ 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.
of the loop is executed.
.. code-block:: c++
@ -83,11 +83,11 @@ of the loop is executed.
Reductions
^^^^^^^^^^
In this example the ``sum`` variable is used by consecutive iterations of
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
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++
@ -95,7 +95,7 @@ reduction operations, such as addition, multiplication, XOR, AND and OR.
int foo(int *A, int *B, int n) {
unsigned sum = 0;
for (int i = 0; i < n; ++i)
sum += A[i] + 5;
sum += A[i] + 5;
return sum;
}
@ -159,8 +159,8 @@ The Loop Vectorizer can vectorize loops that count backwards.
Scatter / Gather
^^^^^^^^^^^^^^^^
The Loop Vectorizer can vectorize code that becomes scatter/gather
memory accesses.
The Loop Vectorizer can vectorize code that becomes scatter/gather
memory accesses.
.. code-block:: c++
@ -204,13 +204,13 @@ See the table below for a list of these functions.
Performance
-----------
This section shows the the execution time of Clang on a simple benchmark:
This section shows the the execution time of Clang on a simple benchmark:
`gcc-loops <http://llvm.org/viewvc/llvm-project/test-suite/trunk/SingleSource/UnitTests/Vectorizer/>`_.
This benchmarks is a collection of loops from the GCC autovectorization
This benchmarks is a collection of loops from the GCC autovectorization
`page <http://gcc.gnu.org/projects/tree-ssa/vectorization.html>`_ by Dorit Nuzman.
The chart below compares GCC-4.7, ICC-13, and Clang-SVN with and without loop vectorization at -O3, tuned for "corei7-avx", running on a Sandybridge iMac.
The Y-axis shows the time in msec. Lower is better. The last column shows the geomean of all the kernels.
The Y-axis shows the time in msec. Lower is better. The last column shows the geomean of all the kernels.
.. image:: gcc-loops.png
:width: 100%
@ -228,7 +228,7 @@ through clang using the command line flag:
.. code-block:: console
$ clang -fslp-vectorize file.c
$ clang -fslp-vectorize file.c
Details
-------
@ -237,7 +237,7 @@ 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).
(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