This flag replaces inline instrumentation for checks and origin stores with
calls into MSan runtime library. This is a workaround for PR17409.
Disabled by default.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@206585 91177308-0d34-0410-b5e6-96231b3b80d8
Reality is that we're never going to copy one of these. Supporting this
was becoming a nightmare because nothing even causes it to compile most
of the time. Lots of subtle errors built up that wouldn't have been
caught by any "normal" testing.
Also, make the move assignment actually work rather than the bogus swap
implementation that would just infloop if used. As part of that, factor
out the graph pointer updates into a helper to share between move
construction and move assignment.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@206583 91177308-0d34-0410-b5e6-96231b3b80d8
implementation of the SpecificBumpPtrAllocator -- we have to actually
move the subobject. =] Noticed when using this code more directly.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@206582 91177308-0d34-0410-b5e6-96231b3b80d8
LazyCallGraph. This is the start of the whole point of this different
abstraction, but it is just the initial bits. Here is a run-down of
what's going on here. I'm planning to incorporate some (or all) of this
into comments going forward, hopefully with better editing and wording.
=]
The crux of the problem with the traditional way of building SCCs is
that they are ephemeral. The new pass manager however really needs the
ability to associate analysis passes and results of analysis passes with
SCCs in order to expose these analysis passes to the SCC passes. Making
this work is kind-of the whole point of the new pass manager. =]
So, when we're building SCCs for the call graph, we actually want to
build persistent nodes that stick around and can be reasoned about
later. We'd also like the ability to walk the SCC graph in more complex
ways than just the traditional postorder traversal of the current CGSCC
walk. That means that in addition to being persistent, the SCCs need to
be connected into a useful graph structure.
However, we still want the SCCs to be formed lazily where possible.
These constraints are quite hard to satisfy with the SCC iterator. Also,
using that would bypass our ability to actually add data to the nodes of
the call graph to facilite implementing the Tarjan walk. So I've
re-implemented things in a more direct and embedded way. This
immediately makes it easy to get the persistence and connectivity
correct, and it also allows leveraging the existing nodes to simplify
the algorithm. I've worked somewhat to make this implementation more
closely follow the traditional paper's nomenclature and strategy,
although it is still a bit obtuse because it isn't recursive, using
an explicit stack and a tail call instead, and it is interruptable,
resuming each time we need another SCC.
The other tricky bit here, and what actually took almost all the time
and trials and errors I spent building this, is exactly *what* graph
structure to build for the SCCs. The naive thing to build is the call
graph in its newly acyclic form. I wrote about 4 versions of this which
did precisely this. Inevitably, when I experimented with them across
various use cases, they became incredibly awkward. It was all
implementable, but it felt like a complete wrong fit. Square peg, round
hole. There were two overriding aspects that pushed me in a different
direction:
1) We want to discover the SCC graph in a postorder fashion. That means
the root node will be the *last* node we find. Using the call-SCC DAG
as the graph structure of the SCCs results in an orphaned graph until
we discover a root.
2) We will eventually want to walk the SCC graph in parallel, exploring
distinct sub-graphs independently, and synchronizing at merge points.
This again is not helped by the call-SCC DAG structure.
The structure which, quite surprisingly, ended up being completely
natural to use is the *inverse* of the call-SCC DAG. We add the leaf
SCCs to the graph as "roots", and have edges to the caller SCCs. Once
I switched to building this structure, everything just fell into place
elegantly.
Aside from general cleanups (there are FIXMEs and too few comments
overall) that are still needed, the other missing piece of this is
support for iterating across levels of the SCC graph. These will become
useful for implementing #2, but they aren't an immediate priority.
Once SCCs are in good shape, I'll be working on adding mutation support
for incremental updates and adding the pass manager that this analysis
enables.
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This commit was attributed to a different person from the person who
posted the patch to the list, and the person who posted it the list
claimed when they did that they were not the author, but that the author
was yet a third person. I don't know what is going on here, but
reverting until the attribution is clear and the author has explicitly
contributed the patch.
Also, the review hasn't really involved any of the MC maintainers and
that seems questionable too.
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Covers quite a few extra instructions (like any of the max/min ones
which were broken until recently on ARM64).
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Code mostly copied from AArch64, just tidied up a trifle and plumbed
into the ARM64 way of doing things.
This also enables the AArch64 tests which inspired the previous
untested commits.
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A vector extract followed by a dup can become a single instruction even if the
types don't match. AArch64 handled this in ISelLowering, but a few reasonably
simple patterns can take care of it in TableGen, so that's where I've put it.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@206573 91177308-0d34-0410-b5e6-96231b3b80d8
Tests will be coming very shortly when all the optimisations needed to
support AArch64's neon-copy.ll file are committed.
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Tests will be committed shortly when all optimisations needed to
support AArch64's neon-copy.ll file are supported.
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ARM64 was scalarizing some vector comparisons which don't quite map to
AArch64's compare and mask instructions. AArch64's approach of sacrificing a
little efficiency to emulate them with the limited set available was better, so
I ported it across.
More "inspired by" than copy/paste since the backend's internal expectations
were a bit different, but the tests were invaluable.
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I enhanced it a little in the process. The decision shouldn't really be beased
on whether a BUILD_VECTOR is a splat: any set of constants will do the job
provided they're related in the correct way.
Also, the BUILD_VECTOR could be any operand of the incoming AND nodes, so it's
best to check for all 4 possibilities rather than assuming it'll be the RHS.
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It's not actually used to handle C or C++ ABI rules on ARM64, but could well be
emitted by other language front-ends, so it's as well to have a sensible
implementation.
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Previously module verification was always enabled, with no way to turn it off.
As of this commit, module verification is on by default in Debug builds, and off
by default in release builds. The default behaviour can be overridden by calling
setVerifyModules(bool) on the JIT instance (this works for both the old JIT, and
MCJIT).
<rdar://problem/16150008>
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Use scalar BFE with constant shift and offset when possible.
This is complicated by the fact that the scalar version packs
the two operands of the vector version into one.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@206558 91177308-0d34-0410-b5e6-96231b3b80d8
Rewrite the shared implementation of BlockFrequencyInfo and
MachineBlockFrequencyInfo entirely.
The old implementation had a fundamental flaw: precision losses from
nested loops (or very wide branches) compounded past loop exits (and
convergence points).
The @nested_loops testcase at the end of
test/Analysis/BlockFrequencyAnalysis/basic.ll is motivating. This
function has three nested loops, with branch weights in the loop headers
of 1:4000 (exit:continue). The old analysis gives non-sensical results:
Printing analysis 'Block Frequency Analysis' for function 'nested_loops':
---- Block Freqs ----
entry = 1.0
for.cond1.preheader = 1.00103
for.cond4.preheader = 5.5222
for.body6 = 18095.19995
for.inc8 = 4.52264
for.inc11 = 0.00109
for.end13 = 0.0
The new analysis gives correct results:
Printing analysis 'Block Frequency Analysis' for function 'nested_loops':
block-frequency-info: nested_loops
- entry: float = 1.0, int = 8
- for.cond1.preheader: float = 4001.0, int = 32007
- for.cond4.preheader: float = 16008001.0, int = 128064007
- for.body6: float = 64048012001.0, int = 512384096007
- for.inc8: float = 16008001.0, int = 128064007
- for.inc11: float = 4001.0, int = 32007
- for.end13: float = 1.0, int = 8
Most importantly, the frequency leaving each loop matches the frequency
entering it.
The new algorithm leverages BlockMass and PositiveFloat to maintain
precision, separates "probability mass distribution" from "loop
scaling", and uses dithering to eliminate probability mass loss. I have
unit tests for these types out of tree, but it was decided in the review
to make the classes private to BlockFrequencyInfoImpl, and try to shrink
them (or remove them entirely) in follow-up commits.
The new algorithm should generally have a complexity advantage over the
old. The previous algorithm was quadratic in the worst case. The new
algorithm is still worst-case quadratic in the presence of irreducible
control flow, but it's linear without it.
The key difference between the old algorithm and the new is that control
flow within a loop is evaluated separately from control flow outside,
limiting propagation of precision problems and allowing loop scale to be
calculated independently of mass distribution. Loops are visited
bottom-up, their loop scales are calculated, and they are replaced by
pseudo-nodes. Mass is then distributed through the function, which is
now a DAG. Finally, loops are revisited top-down to multiply through
the loop scales and the masses distributed to pseudo nodes.
There are some remaining flaws.
- Irreducible control flow isn't modelled correctly. LoopInfo and
MachineLoopInfo ignore irreducible edges, so this algorithm will
fail to scale accordingly. There's a note in the class
documentation about how to get closer. See also the comments in
test/Analysis/BlockFrequencyInfo/irreducible.ll.
- Loop scale is limited to 4096 per loop (2^12) to avoid exhausting
the 64-bit integer precision used downstream.
- The "bias" calculation proposed on llvmdev is *not* incorporated
here. This will be added in a follow-up commit, once comments from
this review have been handled.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@206548 91177308-0d34-0410-b5e6-96231b3b80d8
Summary:
This prevents the discriminator generation pass from triggering if
the DWARF version being used in the module is prior to 4.
Reviewers: echristo, dblaikie
CC: llvm-commits
Differential Revision: http://reviews.llvm.org/D3413
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Change the command line vector-insertion.ll to explicitly set the neon syntax
to apple so that buildbots that default to other syntaxes won't fail.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@206502 91177308-0d34-0410-b5e6-96231b3b80d8
Having i128 as a legal type complicates the legalization phase. v4i32
is already a legal type, so we will use that instead.
This fixes several piglit tests.
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