X-Git-Url: http://plrg.eecs.uci.edu/git/?a=blobdiff_plain;f=docs%2FVectorizers.rst;h=61ebca2bb529f41088385edb1ff310589a71d0a4;hb=1185582dfd542883194d262c5bf92b16e1e037c2;hp=b60e46dbca0b9be92ad1fb211d8b1a9e968bee20;hpb=67a6ec87be54f84e8c8ea6c49583e9303fecef2c;p=oota-llvm.git diff --git a/docs/Vectorizers.rst b/docs/Vectorizers.rst index b60e46dbca0..61ebca2bb52 100644 --- a/docs/Vectorizers.rst +++ b/docs/Vectorizers.rst @@ -2,51 +2,80 @@ 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 `, +which operates on Loops, and the :ref:`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 =================== -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 -fvectorize file.c + $ clang ... -fno-vectorize file.c + +Command line flags +^^^^^^^^^^^^^^^^^^ -We plan to enable the Loop Vectorizer by default as part of the LLVM 3.3 release. +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 +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; + 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 @@ -57,24 +86,24 @@ 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 -of the loop is executed. +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; + 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 +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 +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++ @@ -82,12 +111,14 @@ 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) - sum += A[i] + 5; + 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. @@ -95,12 +126,12 @@ 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; + 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 @@ -118,7 +149,7 @@ nesting of IFs, ELSEs and even GOTOs. } 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. @@ -132,7 +163,7 @@ this loop. This feature is important because many C++ programs use iterators. } Reverse Iterators --------------------------- +^^^^^^^^^^^^^^^^^ The Loop Vectorizer can vectorize loops that count backwards. @@ -144,20 +175,20 @@ 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++ 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 --------------------------- +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 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 +197,31 @@ vectorization is profitable. .. 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]; } +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 --------------------------- +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The Loop Vectorize can vectorize intrinsic math functions. See the table below for a list of these functions. @@ -187,35 +237,75 @@ 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 -^^^^^^^^^^^ +----------- -This section shows the the execution time of Clang on a simple benchmark: -`gcc-loops `._ -This benchmarks is a collection of loops from the GCC autovectorization -`page ` by Dorit Nuzman._ +This section shows the the execution time of Clang on a simple benchmark: +`gcc-loops `_. +This benchmarks is a collection of loops from the GCC autovectorization +`page `_ 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 -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 @@ -223,10 +313,28 @@ 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; + 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