Update the docs about the fact that the loop vectorizer is enabled by default for...

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

diff --git a/docs/Vectorizers.rst b/docs/Vectorizers.rst
index 61b72092414210bc689d2d064e1f8bae86f70104..2c5604496b3c8a2e9605f8fae9fc7caeb3a9c8d4 100644 (file)
--- a/docs/Vectorizers.rst
+++ b/docs/Vectorizers.rst
@@ -21,19 +21,35 @@ The Loop Vectorizer
 Usage
 -----
 
-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:
+LLVM's Loop Vectorizer is now enabled by default for -O3.
+The vectorizer can be disabled using the command line:
 
 .. code-block:: console
 
-   $ clang -fvectorize -O3 file.c
+   $ clang ... -fno-vectorize  file.c
 
-If the ``-fvectorize`` flag is used then the loop vectorizer will be enabled
-when running with ``-O3``, ``-O2``. When ``-Os`` is used, the loop vectorizer
-will only vectorize loops that do not require a major increase in code size.
+At this point the loop vectorizer is only enabled for -O3, and will not work for -O2 or -Os.
 
-We plan to enable the Loop Vectorizer by default as part of the LLVM 3.3 release.
+Command line flags
+^^^^^^^^^^^^^^^^^^
+
+The loop vectorizer uses a cost model to decide on the optimal vectorization factor
+and unroll factor. However, users of the vectorizer can force the vectorizer to use
+specific values. Both 'clang' and 'opt' support the flags below.
+
+Users can control the vectorization SIMD width using the command line flag "-force-vector-width".
+
+.. code-block:: console
+
+  $ clang  -mllvm -force-vector-width=8 ...
+  $ opt -loop-vectorize -force-vector-width=8 ...
+
+Users can control the unroll factor using the command line flag "-force-vector-unroll"
+
+.. code-block:: console
+
+  $ clang  -mllvm -force-vector-unroll=2 ...
+  $ opt -loop-vectorize -force-vector-unroll=2 ...
 
 Features
 --------
@@ -99,6 +115,8 @@ reduction operations, such as addition, multiplication, XOR, AND and OR.
     return sum;
   }
 
+We support floating point reduction operations when `-ffast-math` is used.
+
 Inductions
 ^^^^^^^^^^
 
@@ -159,8 +177,8 @@ 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 a sequence of scalar instructions 
+that scatter/gathers memory.
 
 .. code-block:: c++
 
@@ -183,6 +201,25 @@ vectorization is profitable.
       A[i] += 4 * B[i];
   }
 
+Global Structures Alias Analysis
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Access to global structures can also be vectorized, with alias analysis being
+used to make sure accesses don't alias. Run-time checks can also be added on
+pointer access to structure members.
+
+Many variations are supported, but some that rely on undefined behaviour being
+ignored (as other compilers do) are still being left un-vectorized.
+
+.. code-block:: c++
+
+  struct { int A[100], K, B[100]; } Foo;
+
+  int foo() {
+    for (int i = 0; i < 100; ++i)
+      Foo.A[i] = Foo.B[i] + 100;
+  }
+
 Vectorization of function calls
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -200,6 +237,45 @@ See the table below for a list of these functions.
 +-----+-----+---------+
 |fma  |trunc|nearbyint|
 +-----+-----+---------+
+|     |     | fmuladd |
++-----+-----+---------+
+
+The loop vectorizer knows about special instructions on the target and will
+vectorize a loop containing a function call that maps to the instructions. For
+example, the loop below will be vectorized on Intel x86 if the SSE4.1 roundps
+instruction is available.
+
+.. code-block:: c++
+
+  void foo(float *f) {
+    for (int i = 0; i != 1024; ++i)
+      f[i] = floorf(f[i]);
+  }
+
+Partial unrolling during vectorization
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Modern processors feature multiple execution units, and only programs that contain a
+high degree of parallelism can fully utilize the entire width of the machine. 
+The Loop Vectorizer increases the instruction level parallelism (ILP) by 
+performing partial-unrolling of loops.
+
+In the example below the entire array is accumulated into the variable 'sum'.
+This is inefficient because only a single execution port can be used by the processor.
+By unrolling the code the Loop Vectorizer allows two or more execution ports
+to be used simultaneously.
+
+.. code-block:: c++
+
+  int foo(int *A, int *B, int n) {
+    unsigned sum = 0;
+    for (int i = 0; i < n; ++i)
+        sum += A[i];
+    return sum;
+  }
+
+The Loop Vectorizer uses a cost model to decide when it is profitable to unroll loops.
+The decision to unroll the loop depends on the register pressure and the generated code size. 
 
 Performance
 -----------
@@ -214,6 +290,10 @@ The Y-axis shows the time in msec. Lower is better. The last column shows the ge
 
 .. image:: gcc-loops.png
 
+And Linpack-pc with the same configuration. Result is Mflops, higher is better.
+
+.. image:: linpack-pc.png
+
 .. _bb-vectorizer:
 
 The Basic Block Vectorizer