Revert r252366: [Support] Use GetTempDir to get the temporary dir path on Windows.

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

diff --git a/docs/Vectorizers.rst b/docs/Vectorizers.rst
index b60e46dbca0b9be92ad1fb211d8b1a9e968bee20..65c19aa2bc0cbfa4f7c0c9f88a91c2b2779a7368 100644 (file)
--- a/docs/Vectorizers.rst
+++ b/docs/Vectorizers.rst
@@ -2,51 +2,163 @@
 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:`SLP Vectorizer
+<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
+vectors while the Loop Vectorizer widens instructions in loops
+to operate on multiple consecutive iterations.
+
+Both the Loop Vectorizer and the SLP Vectorizer are enabled by default.
+
+.. _loop-vectorizer:

 
 The Loop Vectorizer
 ===================
 
 
 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:
+Usage
+-----
+
+The Loop Vectorizer is enabled by default, but it can be disabled
+through clang using the command line flag:
+
+.. code-block:: console
+
+   $ clang ... -fno-vectorize  file.c
+
+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 ...
+
+Pragma loop hint directives
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The ``#pragma clang loop`` directive allows loop vectorization hints to be
+specified for the subsequent for, while, do-while, or c++11 range-based for
+loop. The directive allows vectorization and interleaving to be enabled or
+disabled. Vector width as well as interleave count can also be manually
+specified. The following example explicitly enables vectorization and
+interleaving:
+
+.. code-block:: c++
+
+  #pragma clang loop vectorize(enable) interleave(enable)
+  while(...) {
+    ...
+  }
+
+The following example implicitly enables vectorization and interleaving by
+specifying a vector width and interleaving count:
+
+.. code-block:: c++
+
+  #pragma clang loop vectorize_width(2) interleave_count(2)
+  for(...) {
+    ...
+  }
+
+See the Clang
+`language extensions
+<http://clang.llvm.org/docs/LanguageExtensions.html#extensions-for-loop-hint-optimizations>`_
+for details.
+
+Diagnostics
+-----------
+
+Many loops cannot be vectorized including loops with complicated control flow,
+unvectorizable types, and unvectorizable calls. The loop vectorizer generates
+optimization remarks which can be queried using command line options to identify
+and diagnose loops that are skipped by the loop-vectorizer.
+
+Optimization remarks are enabled using:
+
+``-Rpass=loop-vectorize`` identifies loops that were successfully vectorized.
+
+``-Rpass-missed=loop-vectorize`` identifies loops that failed vectorization and
+indicates if vectorization was specified.
+
+``-Rpass-analysis=loop-vectorize`` identifies the statements that caused
+vectorization to fail.
+
+Consider the following loop:
+
+.. code-block:: c++
+
+  #pragma clang loop vectorize(enable)
+  for (int i = 0; i < Length; i++) {
+    switch(A[i]) {
+    case 0: A[i] = i*2; break;
+    case 1: A[i] = i;   break;
+    default: A[i] = 0;
+    }
+  }
+
+The command line ``-Rpass-missed=loop-vectorized`` prints the remark:
+
+.. code-block:: console
+
+  no_switch.cpp:4:5: remark: loop not vectorized: vectorization is explicitly enabled [-Rpass-missed=loop-vectorize]
+
+And the command line ``-Rpass-analysis=loop-vectorize`` indicates that the
+switch statement cannot be vectorized.

 
 .. code-block:: console
 
 
 .. code-block:: console
 

-   $ clang -fvectorize file.c
+  no_switch.cpp:4:5: remark: loop not vectorized: loop contains a switch statement [-Rpass-analysis=loop-vectorize]
+    switch(A[i]) {
+    ^

 
 

-We plan to enable the Loop Vectorizer by default as part of the LLVM 3.3 release.
+To ensure line and column numbers are produced include the command line options
+``-gline-tables-only`` and ``-gcolumn-info``. See the Clang `user manual
+<http://clang.llvm.org/docs/UsersManual.html#options-to-emit-optimization-reports>`_
+for details

 
 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,
 and the Loop Vectorizer has a mechanism to vectorize loops that do not start
 
 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
+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) {
 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;
+    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


@@ -57,24 +169,24 @@ 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
 the loop. Normally, this would prevent vectorization, but the vectorizer can

-detect that ‘sum’ is a reduction variable. The variable ‘sum’ becomes a vector
+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
 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++


@@ -82,12 +194,14 @@ 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.


@@ -95,12 +209,12 @@ 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


@@ -118,7 +232,7 @@ 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.


@@ -132,7 +246,7 @@ 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.
 


@@ -144,20 +258,23 @@ 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++
 
 
 .. 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];
+  int foo(int * A, int * B, int n) {
+    for (intptr_t i = 0; i < n; ++i)
+        A[i] += B[i * 4];

   }
 
   }
 

+In many situations the cost model will inform LLVM that this is not beneficial
+and LLVM will only vectorize such code if forced with "-mllvm -force-vector-width=#".
+

 Vectorization of Mixed Types
 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


@@ -166,12 +283,31 @@ 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.


@@ -187,35 +323,75 @@ 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: 
-`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 
-`page <http://gcc.gnu.org/projects/tree-ssa/vectorization.html>` by Dorit Nuzman._
+This section shows 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
+`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 at -O3, running on a Sandybridge.
-The Y-axis shows time in msec. Lower is better.
+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.

 
 .. image:: gcc-loops.png
 
 
 .. image:: gcc-loops.png
 

-The Basic Block Vectorizer
-==========================
+And Linpack-pc with the same configuration. Result is Mflops, higher is better.

 
 

-The Basic Block Vectorizer is not enabled by default, but it can be enabled
-through clang using the command line flag:
+.. image:: linpack-pc.png

 
 

-.. code-block:: console
+.. _slp-vectorizer:

 
 

-   $ clang -fslp-vectorize file.c 
+The SLP Vectorizer
+==================

 
 

-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). 
+Details
+-------
+
+The goal of SLP vectorization (a.k.a. superword-level parallelism) is
+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


@@ -223,10 +399,28 @@ into vector operations.
 
 .. code-block:: c++
 
 
 .. 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;
+  void foo(int a1, int a2, int b1, int b2, int *A) {
+    A[0] = a1*(a1 + b1)/b1 + 50*b1/a1;
+    A[1] = a2*(a2 + b2)/b2 + 50*b2/a2;

   }
 
   }
 

+The SLP-vectorizer processes the code bottom-up, across basic blocks, in search of scalars to combine.
+
+Usage
+------
+
+The SLP Vectorizer is enabled by default, but it can be disabled
+through clang using the command line flag:
+
+.. code-block:: console
+
+   $ clang -fno-slp-vectorize file.c
+
+LLVM has a second basic block vectorization phase
+which is more compile-time intensive (The BB vectorizer). This optimization
+can be enabled through clang using the command line flag:
+
+.. code-block:: console
+
+   $ clang -fslp-vectorize-aggressive file.c