mirror of
https://github.com/c64scene-ar/llvm-6502.git
synced 2024-08-22 10:29:35 +00:00
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@171873 91177308-0d34-0410-b5e6-96231b3b80d8
309 lines
9.2 KiB
ReStructuredText
309 lines
9.2 KiB
ReStructuredText
==========================
|
|
Auto-Vectorization in LLVM
|
|
==========================
|
|
|
|
.. 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
|
|
-----
|
|
|
|
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.
|
|
|
|
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
|
|
--------
|
|
|
|
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;
|
|
}
|
|
|
|
We support floating point reduction operations when `-ffast-math` is used.
|
|
|
|
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 a sequence of scalar instructions
|
|
that scatter/gathers memory.
|
|
|
|
.. 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|
|
|
+-----+-----+---------+
|
|
| | | fmuladd |
|
|
+-----+-----+---------+
|
|
|
|
|
|
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
|
|
-----------
|
|
|
|
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
|
|
|
|
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 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;
|
|
}
|
|
|
|
|