Auto-Vectorization in LLVM — LLVM 22.1.8 documentation

# Auto-Vectorization in LLVM

LLVM has two vectorizers: The Loop Vectorizer, which operates on Loops, and the 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.

## The Loop Vectorizer

### Usage

The Loop Vectorizer is enabled by default, but it can be disabled through clang using the command line flag:

    $ 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”.

    $ 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-interleave”

    $ clang  -mllvm -force-vector-interleave=2 ...
    $ opt -loop-vectorize -force-vector-interleave=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:

    #pragma clang loop vectorize(enable) interleave(enable)
    while(...) {
      ...
    }

The following example implicitly enables vectorization and interleaving by specifying a vector width and interleaving count:

    #pragma clang loop vectorize_width(2) interleave_count(2)
    for(...) {
      ...
    }

See the Clang language extensions 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. If in addition `-fsave-optimization-record` is provided, multiple causes of vectorization failure may be listed (this behavior might change in the future).

Consider the following loop:

    #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-vectorize` prints the remark:

    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.

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

To ensure line and column numbers are produced include the command line options `-gline-tables-only` and `-gcolumn-info`. See the Clang user manual for details

### 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.

    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.

    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.

    int foo(int *A, int n) {
      unsigned sum = 0;
      for (int i = 0; i < n; ++i)
        sum += A[i] + 5;
      return sum;
    }

Fully vectorizing reductions requires reordering operations, which is problematic for floating-point arithmetic because it is not associative; therefore results may depend on the evaluation order.

Changing floating-point results is implicitly prohibited by the C and C++ standards, therefore LLVM supports vectorizing floating point reductions only when at least the -fassociative-math -fno-signed-zeros -fno-trapping-math subset of -ffast-math is used on most targets. On some targets, such as AArch64 and RISC-V, LLVM can generate ordered reductions that preserve the exact result, enabling limited, standards-compliant vectorization. However, ordered reductions are typically less efficient than traditionally vectorized reductions, therefore enabling floating-point reordering may still result in more performant reductions on these targets.

#### Inductions

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

    void bar(float *A, 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.

    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.

    int baz(int *A, int n) {
      return std::accumulate(A, A + n, 0);
    }

#### Reverse Iterators

The Loop Vectorizer can vectorize loops that count backwards.

    void foo(int *A, 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.

    void 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

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.

    void foo(int *A, char *B, int n) {
      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.

    struct { int A[100], K, B[100]; } Foo;

    void foo() {
      for (int i = 0; i < 100; ++i)
        Foo.A[i] = Foo.B[i] + 100;
    }

#### Vectorization of function calls

The Loop Vectorizer 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

Note that the optimizer may not be able to vectorize math library functions that correspond to these intrinsics if the library calls access external state such as “errno”. To allow better optimization of C/C++ math library functions, use “-fno-math-errno”.

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.

    void foo(float *f) {
      for (int i = 0; i != 1024; ++i)
        f[i] = floorf(f[i]);
    }

Many of these math functions are only vectorizable if the file has been built with a specified target vector library that provides a vector implementation of that math function. Using clang, this is handled by the “-fveclib” command line option with one of the following vector libraries: “Accelerate,libmvec,MASSV,SVML,SLEEF,Darwin_libsystem_m,ArmPL,AMDLIBM”

    $ clang ... -fno-math-errno -fveclib=libmvec file.c

#### 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.

    int foo(int *A, 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.

#### Epilogue Vectorization

When vectorizing a loop, often a scalar remainder (epilogue) loop is necessary to execute tail iterations of the loop if the loop trip count is unknown or it does not evenly divide the vectorization and unroll factors. When the vectorization and unroll factors are large, it’s possible for loops with smaller trip counts to end up spending most of their time in the scalar (rather than the vector) code. In order to address this issue, the inner loop vectorizer is enhanced with a feature that allows it to vectorize epilogue loops with a vectorization and unroll factor combination that makes it more likely for small trip count loops to still execute in vectorized code. The diagram below shows the CFG for a typical epilogue vectorized loop with runtime checks. As illustrated the control flow is structured in a way that avoids duplicating the runtime pointer checks and optimizes the path length for loops that have very small trip counts.

#### Early Exit Vectorization

When vectorizing a loop with a single early exit, the loop blocks following the early exit are predicated and the vector loop will always exit via the latch. The loop terminates with a single BranchOnTwoConds VPInstruction, which takes both the early and latch exiting conditions. If the early exiting condition is true, BranchOnTwoConds exits to an intermediate block (`vector.early.exit` below). This intermediate block is responsible for calculating any exit values of loop-defined variables that are used in the early exit block. If the latch exiting condition is true, BranchOnTwoConds exits to the `middle.block` which selects between the exit block and the scalar remainder loop. Otherwise BranchOnTwoConds continues executing the loop by jumping back to the region header.

BranchOnTwoConds is lowered to a chain of conditional branches after dissolving loop regions:

### Performance

This section shows 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 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.

And Linpack-pc with the same configuration. Result is Mflops, higher is better.

### Ongoing Development Directions

Vectorization Plan

  Modeling the process and upgrading the infrastructure of LLVM’s Loop Vectorizer.

## The SLP Vectorizer

### 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 into vector operations.

    void foo(int a1, int a2, int b1, int b2, int *A) {
      A[0] = a1*(a1 + b1);
      A[1] = a2*(a2 + b2);
      A[2] = a1*(a1 + b1);
      A[3] = a2*(a2 + b2);
    }

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:

    $ clang -fno-slp-vectorize file.c

## The Sandbox Vectorizer

The Sandbox Vectorizer is an experimental framework for building modular vectorization pipelines on top of Sandbox IR, with a focus on ease of testing and ease of development.