While the term "Target" is in the name, it doesn't really have to do
with the LLVM Target library -- this isn't an abstraction which LLVM
targets generally need to implement or extend. It has much more to do
with modeling the various runtime libraries on different OSes and with
different runtime environments. The "target" in this sense is the more
general sense of a target of cross compilation.
This is in preparation for porting this analysis to the new pass
manager.
No functionality changed, and updates inbound for Clang and Polly.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@226078 91177308-0d34-0410-b5e6-96231b3b80d8
utils/sort_includes.py.
I clearly haven't done this in a while, so more changed than usual. This
even uncovered a missing include from the InstrProf library that I've
added. No functionality changed here, just mechanical cleanup of the
include order.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@225974 91177308-0d34-0410-b5e6-96231b3b80d8
These are named following the IEEE-754 names for these
functions, rather than the libm fmin / fmax to avoid
possible ambiguities. Some languages may implement something
resembling fmin / fmax which return NaN if either operand is
to propagate errors. These implement the IEEE-754 semantics
of returning the other operand if either is a NaN representing
missing data.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@220341 91177308-0d34-0410-b5e6-96231b3b80d8
A few minor changes to prevent @llvm.assume from interfering with loop
vectorization. First, treat @llvm.assume like the lifetime intrinsics, which
are scalarized (but don't otherwise interfere with the legality checking).
Second, ignore the cost of ephemeral instructions in the loop (these will go
away anyway during CodeGen).
Alignment assumptions and other uses of @llvm.assume can often end up inside of
loops that should be vectorized (this is not uncommon for assumptions generated
by __attribute__((align_value(n))), for example).
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@219741 91177308-0d34-0410-b5e6-96231b3b80d8
The cost model conservatively assumes that it will always get scalarized and
that's about as good as we can get with the generic TTI; reasoning whether a
shuffle with an efficient lowering is available is hard. We can override that
conservative estimate for some targets in the future.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@209125 91177308-0d34-0410-b5e6-96231b3b80d8
The vectorizer only knows how to vectorize intrinics by widening all operands by
the same factor.
Patch by Tyler Nowicki!
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@205855 91177308-0d34-0410-b5e6-96231b3b80d8
