The monolithic interface for instruction costs has been split into
several functions. This is the corresponding change. No functionality
change is intended.
llvm-svn: 166865
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.
llvm-svn: 166836
This is needed so that perl's SHA can be compiled (otherwise
BBVectorize takes far too long to find its fixed point).
I'll try to come up with a reduced test case.
llvm-svn: 166738
This is the first of several steps to incorporate information from the new
TargetTransformInfo infrastructure into BBVectorize. Two things are done here:
1. Target information is used to determine if it is profitable to fuse two
instructions. This means that the cost of the vector operation must not
be more expensive than the cost of the two original operations. Pairs that
are not profitable are no longer considered (because current cost information
is incomplete, for intrinsics for example, equal-cost pairs are still
considered).
2. The 'cost savings' computed for the profitability check are also used to
rank the DAGs that represent the potential vectorization plans. Specifically,
for nodes of non-trivial depth, the cost savings is used as the node
weight.
The next step will be to incorporate the shuffle costs into the DAG weighting;
this will give the edges of the DAG weights as well. Once that is done, when
target information is available, we should be able to dispense with the
depth heuristic.
llvm-svn: 166716
Unreachable blocks can have invalid instructions. For example,
jump threading can produce self-referential instructions in
unreachable blocks. Also, we should not be spending time
optimizing unreachable code. Fixes PR14133.
llvm-svn: 166423
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;
llvm-svn: 166388
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 += ...
llvm-svn: 166387
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.
llvm-svn: 166371
Nadav Rotem