Before we could vectorize PHINodes scanning successors was a good way of finding candidates. Now we can vectorize the phinodes which is simpler.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@186139 91177308-0d34-0410-b5e6-96231b3b80d8
This is a complete re-write if the bottom-up vectorization class.
Before this commit we scanned the instruction tree 3 times. First in search of merge points for the trees. Second, for estimating the cost. And finally for vectorization.
There was a lot of code duplication and adding the DCE exposed bugs. The new design is simpler and DCE was a part of the design.
In this implementation we build the tree once. After that we estimate the cost by scanning the different entries in the constructed tree (in any order). The vectorization phase also works on the built tree.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@185774 91177308-0d34-0410-b5e6-96231b3b80d8
To support this we have to insert 'extractelement' instructions to pick the right lane.
We had this functionality before but I removed it when we moved to the multi-block design because it was too complicated.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@185230 91177308-0d34-0410-b5e6-96231b3b80d8
Untill now we detected the vectorizable tree and evaluated the cost of the
entire tree. With this patch we can decide to trim-out branches of the tree
that are not profitable to vectorizer.
Also, increase the max depth from 6 to 12. In the worse possible case where all
of the code is made of diamond-shaped graph this can bring the cost to 2**10,
but diamonds are not very common.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@184681 91177308-0d34-0410-b5e6-96231b3b80d8
Rewrote the SLP-vectorization as a whole-function vectorization pass. It is now able to vectorize chains across multiple basic blocks.
It still does not vectorize PHIs, but this should be easy to do now that we scan the entire function.
I removed the support for extracting values from trees.
We are now able to vectorize more programs, but there are some serious regressions in many workloads (such as flops-6 and mandel-2).
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@184647 91177308-0d34-0410-b5e6-96231b3b80d8
We collect gather sequences when we vectorize basic blocks. Gather sequences are excellent
hints for vectorization of other basic blocks.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@184444 91177308-0d34-0410-b5e6-96231b3b80d8
The type <3 x i8> is a common in graphics and we want to be able to vectorize it.
This changes accelerates bullet by 12% and 471_omnetpp by 5%.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@184317 91177308-0d34-0410-b5e6-96231b3b80d8
We are not working on a DAG and I ran into a number of problems when I enabled the vectorizations of 'diamond-trees' (trees that share leafs).
* Imroved the numbering API.
* Changed the placement of new instructions to the last root.
* Fixed a bug with external tree users with non-zero lane.
* Fixed a bug in the placement of in-tree users.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@182508 91177308-0d34-0410-b5e6-96231b3b80d8
The external user does not have to be in lane #0. We have to save the lane for each scalar so that we know which vector lane to extract.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@181674 91177308-0d34-0410-b5e6-96231b3b80d8
For example:
bar() {
int a = A[i];
int b = A[i+1];
B[i] = a;
B[i+1] = b;
foo(a); <--- a is used outside the vectorized expression.
}
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@181648 91177308-0d34-0410-b5e6-96231b3b80d8
This commit adds the infrastructure for performing bottom-up SLP vectorization (and other optimizations) on parallel computations.
The infrastructure has three potential users:
1. The loop vectorizer needs to be able to vectorize AOS data structures such as (sum += A[i] + A[i+1]).
2. The BB-vectorizer needs this infrastructure for bottom-up SLP vectorization, because bottom-up vectorization is faster to compute.
3. A loop-roller needs to be able to analyze consecutive chains and roll them into a loop, in order to reduce code size. A loop roller does not need to create vector instructions, and this infrastructure separates the chain analysis from the vectorization.
This patch also includes a simple (100 LOC) bottom up SLP vectorizer that uses the infrastructure, and can vectorize this code:
void SAXPY(int *x, int *y, int a, int i) {
x[i] = a * x[i] + y[i];
x[i+1] = a * x[i+1] + y[i+1];
x[i+2] = a * x[i+2] + y[i+2];
x[i+3] = a * x[i+3] + y[i+3];
}
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@179117 91177308-0d34-0410-b5e6-96231b3b80d8
