--- /dev/null
+==========================
+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.
+
+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:
+
+.. code-block:: console
+
+ $ clang -fvectorize -O3 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.
+
+We plan to enable the Loop Vectorizer by default as part of the LLVM 3.3 release.
+
+Features
+--------
+
+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
+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) {
+ for (int i = start; i < end; ++i)
+ A[i] *= B[i] + K;
+ }
+
+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
+written before they are read from array B.
+
+Some programmers use the 'restrict' keyword to notify the compiler that the
+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.
+
+.. 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;
+ }
+
+
+Reductions
+^^^^^^^^^^
+
+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
+reduction operations, such as addition, multiplication, XOR, AND and OR.
+
+.. code-block:: c++
+
+ int foo(int *A, int *B, int n) {
+ unsigned sum = 0;
+ for (int i = 0; i < n; ++i)
+ sum += A[i] + 5;
+ return sum;
+ }
+
+Inductions
+^^^^^^^^^^
+
+In this example the value of the induction variable ``i`` is saved into an
+array. The Loop Vectorizer knows to vectorize induction variables.
+
+.. code-block:: c++
+
+ void bar(float *A, float* B, float K, int n) {
+ for (int i = 0; i < n; ++i)
+ A[i] = i;
+ }
+
+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
+control flow in the innermost loop. The innermost loop may contain complex
+nesting of IFs, ELSEs and even GOTOs.
+
+.. code-block:: c++
+
+ int foo(int *A, int *B, int n) {
+ unsigned sum = 0;
+ for (int i = 0; i < n; ++i)
+ if (A[i] > B[i])
+ sum += A[i] + 5;
+ return sum;
+ }
+
+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.
+The Loop Vectorizer detects pointer induction variables and can vectorize
+this loop. This feature is important because many C++ programs use iterators.
+
+.. code-block:: c++
+
+ int baz(int *A, int n) {
+ return std::accumulate(A, A + n, 0);
+ }
+
+Reverse Iterators
+^^^^^^^^^^^^^^^^^
+
+The Loop Vectorizer can vectorize loops that count backwards.
+
+.. code-block:: c++
+
+ int foo(int *A, int *B, int n) {
+ for (int i = n; i > 0; --i)
+ A[i] +=1;
+ }
+
+Scatter / Gather
+^^^^^^^^^^^^^^^^
+
+The Loop Vectorizer can vectorize code that becomes scatter/gather
+memory accesses.
+
+.. 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];
+ }
+
+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
+vectorization is profitable.
+
+.. code-block:: c++
+
+ int foo(int *A, char *B, int n, int k) {
+ for (int i = 0; i < n; ++i)
+ A[i] += 4 * B[i];
+ }
+
+Vectorization of function calls
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The Loop Vectorize can vectorize intrinsic math functions.
+See the table below for a list of these functions.
+
++-----+-----+---------+
+| pow | exp | exp2 |
++-----+-----+---------+
+| sin | cos | sqrt |
++-----+-----+---------+
+| log |log2 | log10 |
++-----+-----+---------+
+|fabs |floor| ceil |
++-----+-----+---------+
+|fma |trunc|nearbyint|
++-----+-----+---------+
+
+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.
+
+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
+
+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
+
+ $ clang -fslp-vectorize file.c
+
+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
+(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
+into vector operations.
+
+.. 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;
+ }
+
+
+++ /dev/null
-==========================
-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.
-
-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:
-
-.. code-block:: console
-
- $ clang -fvectorize -O3 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.
-
-We plan to enable the Loop Vectorizer by default as part of the LLVM 3.3 release.
-
-Features
---------
-
-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
-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) {
- for (int i = start; i < end; ++i)
- A[i] *= B[i] + K;
- }
-
-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
-written before they are read from array B.
-
-Some programmers use the 'restrict' keyword to notify the compiler that the
-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.
-
-.. 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;
- }
-
-
-Reductions
-^^^^^^^^^^
-
-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
-reduction operations, such as addition, multiplication, XOR, AND and OR.
-
-.. code-block:: c++
-
- int foo(int *A, int *B, int n) {
- unsigned sum = 0;
- for (int i = 0; i < n; ++i)
- sum += A[i] + 5;
- return sum;
- }
-
-Inductions
-^^^^^^^^^^
-
-In this example the value of the induction variable ``i`` is saved into an
-array. The Loop Vectorizer knows to vectorize induction variables.
-
-.. code-block:: c++
-
- void bar(float *A, float* B, float K, int n) {
- for (int i = 0; i < n; ++i)
- A[i] = i;
- }
-
-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
-control flow in the innermost loop. The innermost loop may contain complex
-nesting of IFs, ELSEs and even GOTOs.
-
-.. code-block:: c++
-
- int foo(int *A, int *B, int n) {
- unsigned sum = 0;
- for (int i = 0; i < n; ++i)
- if (A[i] > B[i])
- sum += A[i] + 5;
- return sum;
- }
-
-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.
-The Loop Vectorizer detects pointer induction variables and can vectorize
-this loop. This feature is important because many C++ programs use iterators.
-
-.. code-block:: c++
-
- int baz(int *A, int n) {
- return std::accumulate(A, A + n, 0);
- }
-
-Reverse Iterators
-^^^^^^^^^^^^^^^^^
-
-The Loop Vectorizer can vectorize loops that count backwards.
-
-.. code-block:: c++
-
- int foo(int *A, int *B, int n) {
- for (int i = n; i > 0; --i)
- A[i] +=1;
- }
-
-Scatter / Gather
-^^^^^^^^^^^^^^^^
-
-The Loop Vectorizer can vectorize code that becomes scatter/gather
-memory accesses.
-
-.. 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];
- }
-
-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
-vectorization is profitable.
-
-.. code-block:: c++
-
- int foo(int *A, char *B, int n, int k) {
- for (int i = 0; i < n; ++i)
- A[i] += 4 * B[i];
- }
-
-Vectorization of function calls
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-The Loop Vectorize can vectorize intrinsic math functions.
-See the table below for a list of these functions.
-
-+-----+-----+---------+
-| pow | exp | exp2 |
-+-----+-----+---------+
-| sin | cos | sqrt |
-+-----+-----+---------+
-| log |log2 | log10 |
-+-----+-----+---------+
-|fabs |floor| ceil |
-+-----+-----+---------+
-|fma |trunc|nearbyint|
-+-----+-----+---------+
-
-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.
-
-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
-
-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
-
- $ clang -fslp-vectorize file.c
-
-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
-(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
-into vector operations.
-
-.. 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;
- }
-
-
HowToUseInstrMappings
SystemLibrary
SourceLevelDebugging
- Vectorizers
+ AutoVectorization
WritingAnLLVMBackend
GarbageCollection
WritingAnLLVMPass
This document describes the design and philosophy behind the LLVM
source-level debugger.
-* :doc:`Vectorization in LLVM <Vectorizers>`
+* :doc:`AutoVectorization`
This document describes the current status of vectorization in LLVM.
projects / oota-llvm.git / commitdiff