LLVM has two vectorizers: The :ref:`Loop Vectorizer <loop-vectorizer>`,
which operates on Loops, and the :ref:`SLP Vectorizer
-<slp-vectorizer>`, which optimizes straight-line code. These vectorizers
+<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 the original loop
-to operate on multiple consecutive loop iterations.
+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:
Usage
-----
-LLVM's Loop Vectorizer is now enabled by default for -O3.
-We plan to enable parts of the Loop Vectorizer on -O2 and -Os in future releases.
-The vectorizer can be disabled using the command line:
+The Loop Vectorizer is enabled by default, but it can be disabled
+through clang using the command line flag:
.. code-block:: console
.. 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
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-------
The goal of SLP 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).
+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
A[1] = a2*(a2 + b2)/b2 + 50*b2/a2;
}
-The SLP-vectorizer has two phases, bottom-up, and top-down. The top-down vectorization
-phase is more aggressive, but takes more time to run.
+The SLP-vectorizer processes the code bottom-up, across basic blocks, in search of scalars to combine.
Usage
------
-The SLP Vectorizer is not enabled by default, but it can be enabled
+The SLP Vectorizer is enabled by default, but it can be disabled
through clang using the command line flag:
.. code-block:: console
- $ clang -fslp-vectorize file.c
+ $ 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
$ clang -fslp-vectorize-aggressive file.c
-
-The SLP vectorizer is in early development stages but can already vectorize
-and accelerate many programs in the LLVM test suite.
-
-======================= ============
-Benchmark Name Gain
-======================= ============
-Misc/flops-7 -32.70%
-Misc/matmul_f64_4x4 -23.23%
-Olden/power -21.45%
-Misc/flops-4 -14.90%
-ASC_Sequoia/AMGmk -13.85%
-TSVC/LoopRerolling-flt -11.76%
-Misc/flops-6 -9.70%
-Misc/flops-5 -8.54%
-Misc/flops -8.12%
-TSVC/NodeSplitting-dbl -6.96%
-Misc-C++/sphereflake -6.74%
-Ptrdist/yacr2 -6.31%
-======================= ============
-