Extend TableGen instruction selection matcher to improve handling

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

diff --git a/docs/Vectorizers.rst b/docs/Vectorizers.rst
index 98f1451d0de14320ce987895894c8e5cc190d247..e2d3667bc116bcbdfa108a4a209b572ca0fc3167 100644 (file)
--- a/docs/Vectorizers.rst
+++ b/docs/Vectorizers.rst
@@ -2,18 +2,24 @@
 Auto-Vectorization in LLVM
 ==========================
 
 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.
+.. contents::
+   :local:
+
+LLVM has two vectorizers: The :ref:`Loop Vectorizer <loop-vectorizer>`,
+which operates on Loops, and the :ref:`Basic Block Vectorizer
+<bb-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.
+
+.. _loop-vectorizer:

 
 The Loop Vectorizer
 ===================
 
 Usage
 
 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
 
 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


@@ -29,14 +35,35 @@ will only vectorize loops that do not require a major increase in code size.
 
 We plan to enable the Loop Vectorizer by default as part of the LLVM 3.3 release.
 
 
 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
 Features

-^^^^^^^^^
+--------

 
 The LLVM Loop Vectorizer has a number of features that allow it to vectorize
 complex loops.
 
 Loops with unknown trip count
 
 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,
 
 The Loop Vectorizer supports loops with an unknown trip count.
 In the loop below, the iteration ``start`` and ``finish`` points are unknown,


@@ -48,12 +75,12 @@ 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) {
 .. 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;
+    for (int i = start; i < end; ++i)
+      A[i] *= B[i] + K;

   }
 
 Runtime Checks of Pointers
   }
 
 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
 
 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


@@ -64,24 +91,24 @@ 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
 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++
 
   void bar(float *A, float* B, float K, int n) {
 
 .. 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;
+    for (int i = 0; i < n; ++i)
+      A[i] *= B[i] + K;

   }
 
 
 Reductions
   }
 
 
 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
 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++
 reduction operations, such as addition, multiplication, XOR, AND and OR.
 
 .. code-block:: c++


@@ -89,12 +116,14 @@ 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)
   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;
   }
 
     return sum;
   }
 

+We support floating point reduction operations when `-ffast-math` is used.
+

 Inductions
 Inductions

---------------------------
+^^^^^^^^^^

 
 In this example the value of the induction variable ``i`` is saved into an
 array. The Loop Vectorizer knows to vectorize induction variables.
 
 In this example the value of the induction variable ``i`` is saved into an
 array. The Loop Vectorizer knows to vectorize induction variables.


@@ -102,12 +131,12 @@ array. The Loop Vectorizer knows to vectorize induction variables.
 .. code-block:: c++
 
   void bar(float *A, float* B, float K, int n) {
 .. code-block:: c++
 
   void bar(float *A, float* B, float K, int n) {

-   for (int i = 0; i < n; ++i)
-     A[i] = i;
+    for (int i = 0; i < n; ++i)
+      A[i] = i;

   }
 
 If Conversion
   }
 
 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
 
 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


@@ -125,7 +154,7 @@ nesting of IFs, ELSEs and even GOTOs.
   }
 
 Pointer Induction Variables
   }
 
 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.
 
 This example uses the "accumulate" function of the standard c++ library. This
 loop uses C++ iterators, which are pointers, and not integer indices.


@@ -139,7 +168,7 @@ this loop. This feature is important because many C++ programs use iterators.
   }
 
 Reverse Iterators
   }
 
 Reverse Iterators

---------------------------
+^^^^^^^^^^^^^^^^^

 
 The Loop Vectorizer can vectorize loops that count backwards.
 
 
 The Loop Vectorizer can vectorize loops that count backwards.
 


@@ -151,20 +180,20 @@ The Loop Vectorizer can vectorize loops that count backwards.
   }
 
 Scatter / Gather
   }
 
 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++
 
   int foo(int *A, int *B, int n, int k) {
 
 .. code-block:: c++
 
   int foo(int *A, int *B, int n, int k) {

-  for (int i = 0; i < n; ++i)
+    for (int i = 0; i < n; ++i)

       A[i*7] += B[i*k];
   }
 
 Vectorization of Mixed Types
       A[i*7] += B[i*k];
   }
 
 Vectorization of 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
 
 The Loop Vectorizer can vectorize programs with mixed types. The Vectorizer
 cost model can estimate the cost of the type conversion and decide if


@@ -173,12 +202,31 @@ vectorization is profitable.
 .. code-block:: c++
 
   int foo(int *A, char *B, int n, int k) {
 .. code-block:: c++
 
   int foo(int *A, char *B, int n, int k) {

-  for (int i = 0; i < n; ++i)
+    for (int i = 0; i < n; ++i)

       A[i] += 4 * B[i];
   }
 
       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
 Vectorization of function calls

--------------------------------
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

 
 The Loop Vectorize can vectorize intrinsic math functions.
 See the table below for a list of these functions.
 
 The Loop Vectorize can vectorize intrinsic math functions.
 See the table below for a list of these functions.


@@ -194,41 +242,86 @@ See the table below for a list of these functions.
 +-----+-----+---------+
 |fma  |trunc|nearbyint|
 +-----+-----+---------+
 +-----+-----+---------+
 |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
 
 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/>`_.
 `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.
 `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
 
 
 .. 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
 ==========================
 
 Usage
 The Basic Block Vectorizer
 ==========================
 
 Usage

-^^^^^^
+------

 
 The Basic Block Vectorizer is not enabled by default, but it can be enabled
 through clang using the command line flag:
 
 .. code-block:: console
 
 
 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 
+   $ clang -fslp-vectorize file.c

 
 Details
 
 Details

-^^^^^^^
+-------

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