now that this handles the release / retain calls.
Adds a regression test for that bug (which is a compile-time
regression) and for the last two changes to the IntrusiveRefCntPtr,
especially tests for the memory leak due to copy construction of the
ref-counted object and ensuring that the traits are used for release /
retain calls.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@149411 91177308-0d34-0410-b5e6-96231b3b80d8
BitVector uses the native word size for its internal representation.
That doesn't work well for literal bit masks in source code.
This patch adds BitVector operations to efficiently apply literal bit
masks specified as arrays of uint32_t. Since each array entry always
holds exactly 32 bits, these portable bit masks can be source code
literals, probably produced by TableGen.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@148272 91177308-0d34-0410-b5e6-96231b3b80d8
make VariadicFunction actually be trivial. Do so, and also make it look
more like your standard trivial functor by making it a struct with no
access specifiers. The unit test is updated to initialize its functors
properly.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@146827 91177308-0d34-0410-b5e6-96231b3b80d8
variadic-like functions in C++98. See the comments in the header file
for a more detailed description of how these work. We plan to use these
extensively in the AST matching library. This code and idea were
originally authored by Zhanyong Wan. I've condensed it using macros
to reduce repeatition and adjusted it to fit better with LLVM's ADT.
Thanks to both David Blaikie and Doug Gregor for the review!
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@146729 91177308-0d34-0410-b5e6-96231b3b80d8
was returning incorrect values in rare cases, and incorrectly marking
exact conversions as inexact in some more common cases. Fixes PR11406, and a
missed optimization in test/CodeGen/X86/fp-stack-O0.ll.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@145141 91177308-0d34-0410-b5e6-96231b3b80d8
Based on Horspool's simplified version of Boyer-Moore. We use a constant-sized table of
uint8_ts to keep cache thrashing low, needles bigger than 255 bytes are uncommon anyways.
The worst case is still O(n*m) but we do a lot better on the average case now.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142061 91177308-0d34-0410-b5e6-96231b3b80d8
The APFloat "Zero" test was actually calling the
APFloat(const fltSemantics &, integerPart) constructor, and EXPECT_EQ was
treating 0 and -0 as equal.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@138745 91177308-0d34-0410-b5e6-96231b3b80d8
whatever the size of unsigned is), though this can't actually
occur for any integer value of NUM_NODES.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@136460 91177308-0d34-0410-b5e6-96231b3b80d8
more graphs, like all graphs with 5 nodes or less. With a 32 bit
unsigned type, the maximum is graphs with 6 nodes or less, but that
would take a while to test - 5 nodes or less already requires a few
seconds.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@136354 91177308-0d34-0410-b5e6-96231b3b80d8
This computes every graph with 4 or fewer nodes, and checks that the SCC
class indeed returns exactly the simply connected components reachable
from the initial node.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@136351 91177308-0d34-0410-b5e6-96231b3b80d8
errors like the one corrected by r135261. Migrate all LLVM callers of the old
constructor to the new one.
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vec.insert(vec.begin(), vec[3]);
The issue was that vec[3] returns a reference into the vector, which is invalidated when insert() memmove's the elements down to make space. The method needs to specifically detect and handle this case to correctly match std::vector's semantics.
Thanks to Howard Hinnant for clarifying the correct behavior, and explaining how std::vector solves this problem.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@134554 91177308-0d34-0410-b5e6-96231b3b80d8
Some platforms may treat denormals as zero, on other platforms multiplication
with a subnormal is slower than dividing by a normal.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@128555 91177308-0d34-0410-b5e6-96231b3b80d8
The idea is, that if an ieee 754 float is divided by a power of two, we can
turn the division into a cheaper multiplication. This function sees if we can
get an exact multiplicative inverse for a divisor and returns it if possible.
This is the hard part of PR9587.
I tested many inputs against llvm-gcc's frotend implementation of this
optimization and didn't find any difference. However, floating point is the
land of weird edge cases, so any review would be appreciated.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@128545 91177308-0d34-0410-b5e6-96231b3b80d8
of a constant had a minor typo introduced when copying it from the book, which
caused it to favor negative approximations over positive approximations in many
cases. Positive approximations require fewer operations beyond the multiplication.
In the case of division by 3, we still generate code that is a single instruction
larger than GCC's code.
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may be useful to understand "none", this is not the place for it. Tweak
the fix to Normalize while there: the fix added in 123990 works correctly,
but I like this way better. Finally, now that Triple understands some
non-trivial environment values, teach the unittests about them.
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This implementation already exists as ConnectedVNInfoEqClasses in
LiveInterval.cpp, and it seems to be generally useful to have a light-weight way
of forming equivalence classes of small integers.
IntEqClasses doesn't allow enumeration of the elements in a class.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@122293 91177308-0d34-0410-b5e6-96231b3b80d8
moves the iterator to end(), and it is valid to call it on end().
That means it is valid to call advanceTo() with any monotonic key sequence.
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editing of the current interval.
These methods may cause coalescing, there are corresponding set*Unchecked
methods for editing without coalescing. The non-coalescing methods are useful
for applying monotonic transforms to all keys or values in a map without
accidentally coalescing transformed and untransformed intervals.
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