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If the arrays are found to be disjoint then we run the vectorized version of
the loop. If they are not, we run the scalar code.
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bitcasts cost zero.
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This is important for loops in the LAPACK test-suite.
These loops start at 1 because they are auto-converted from fortran.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@167084 91177308-0d34-0410-b5e6-96231b3b80d8
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is a single pointer.
Speedup SciMark by 1%
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needs to start at all-one
while XOR, and OR need to start at zero.
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return the type of the split result.
2. Change the maximum vectorization width from 4 to 8.
3. A test for both.
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Add getCostXXX calls for different families of opcodes, such as casts, arithmetic, cmp, etc.
Port the LoopVectorizer to the new API.
The LoopVectorizer now finds instructions which will remain uniform after vectorization. It uses this information when calculating the cost of these instructions.
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that only run if the target is present.
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Patch by Paul Redmond <paul.redmond@intel.com>.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@166649 91177308-0d34-0410-b5e6-96231b3b80d8
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git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@166620 91177308-0d34-0410-b5e6-96231b3b80d8
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and c++ news.
PR14158.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@166491 91177308-0d34-0410-b5e6-96231b3b80d8
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Fix by Shivarama Rao <Shivarama.Rao@amd.com>
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generate a scalar-cond select (i1 as selector).
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We used a SCEV to detect that A[X] is consecutive. We assumed that X was
the induction variable. But X can be any expression that uses the induction
for example: X = i + 2;
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This is important for nested-loop reductions such as :
In the innermost loop, the induction variable does not start with zero:
for (i = 0 .. n)
for (j = 0 .. m)
sum += ...
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PR14134.
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A[B[i]]+=x.
If the pointer is consecutive then it is safe to read and write. If the pointer is non-loop-consecutive then
it is unsafe to vectorize it because we may hit an ordering issue.
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For example:
for (i=0; i<n; i++)
sum += A[i] + B[i] + i;
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for (i=0; i<n; i++){
a[i] = b[i+1] + c[i+3];
}
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