The doFinalization method checks that the LoopToAliasSetMap is
empty. LICM populates that map as it runs through the loop nest,
deleting the entries for child loops as it goes. However, if a child
loop is deleted by another pass (e.g. unrolling) then the loop will
never be deleted from the map because LICM walks the loop nest to
find entries it can delete.
The fix is to delete the loop from the map and free the alias set
when the loop is deleted from the loop nest.
Differential Revision: http://reviews.llvm.org/D5305
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@218387 91177308-0d34-0410-b5e6-96231b3b80d8
shim between the TargetTransformInfo immutable pass and the Subtarget
via the TargetMachine and Function. Migrate a single call from
BasicTargetTransformInfo as an example and provide shims where TargetMachine
begins taking a Function to determine the subtarget.
No functional change.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@218004 91177308-0d34-0410-b5e6-96231b3b80d8
UBSan complained about using static_cast on the invalid (tombstone, etc.)
pointers used by DenseMap. Use a reinterpret_cast instead.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@217397 91177308-0d34-0410-b5e6-96231b3b80d8
This change teaches LazyValueInfo to use the @llvm.assume intrinsic. Like with
the known-bits change (r217342), this requires feeding a "context" instruction
pointer through many functions. Aside from a little refactoring to reuse the
logic that turns predicates into constant ranges in LVI, the only new code is
that which can 'merge' the range from an assumption into that otherwise
computed. There is also a small addition to JumpThreading so that it can have
LVI use assumptions in the same block as the comparison feeding a conditional
branch.
With this patch, we can now simplify this as expected:
int foo(int a) {
__builtin_assume(a > 5);
if (a > 3) {
bar();
return 1;
}
return 0;
}
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@217345 91177308-0d34-0410-b5e6-96231b3b80d8
This change, which allows @llvm.assume to be used from within computeKnownBits
(and other associated functions in ValueTracking), adds some (optional)
parameters to computeKnownBits and friends. These functions now (optionally)
take a "context" instruction pointer, an AssumptionTracker pointer, and also a
DomTree pointer, and most of the changes are just to pass this new information
when it is easily available from InstSimplify, InstCombine, etc.
As explained below, the significant conceptual change is that known properties
of a value might depend on the control-flow location of the use (because we
care that the @llvm.assume dominates the use because assumptions have
control-flow dependencies). This means that, when we ask if bits are known in a
value, we might get different answers for different uses.
The significant changes are all in ValueTracking. Two main changes: First, as
with the rest of the code, new parameters need to be passed around. To make
this easier, I grouped them into a structure, and I made internal static
versions of the relevant functions that take this structure as a parameter. The
new code does as you might expect, it looks for @llvm.assume calls that make
use of the value we're trying to learn something about (often indirectly),
attempts to pattern match that expression, and uses the result if successful.
By making use of the AssumptionTracker, the process of finding @llvm.assume
calls is not expensive.
Part of the structure being passed around inside ValueTracking is a set of
already-considered @llvm.assume calls. This is to prevent a query using, for
example, the assume(a == b), to recurse on itself. The context and DT params
are used to find applicable assumptions. An assumption needs to dominate the
context instruction, or come after it deterministically. In this latter case we
only handle the specific case where both the assumption and the context
instruction are in the same block, and we need to exclude assumptions from
being used to simplify their own ephemeral values (those which contribute only
to the assumption) because otherwise the assumption would prove its feeding
comparison trivial and would be removed.
This commit adds the plumbing and the logic for a simple masked-bit propagation
(just enough to write a regression test). Future commits add more patterns
(and, correspondingly, more regression tests).
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@217342 91177308-0d34-0410-b5e6-96231b3b80d8
This adds a set of utility functions for collecting 'ephemeral' values. These
are LLVM IR values that are used only by @llvm.assume intrinsics (directly or
indirectly), and thus will be removed prior to code generation, implying that
they should be considered free for certain purposes (like inlining). The
inliner's cost analysis, and a few other passes, have been updated to account
for ephemeral values using the provided functionality.
This functionality is important for the usability of @llvm.assume, because it
limits the "non-local" side-effects of adding llvm.assume on inlining, loop
unrolling, etc. (these are hints, and do not generate code, so they should not
directly contribute to estimates of execution cost).
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@217335 91177308-0d34-0410-b5e6-96231b3b80d8
This adds an immutable pass, AssumptionTracker, which keeps a cache of
@llvm.assume call instructions within a module. It uses callback value handles
to keep stale functions and intrinsics out of the map, and it relies on any
code that creates new @llvm.assume calls to notify it of the new instructions.
The benefit is that code needing to find @llvm.assume intrinsics can do so
directly, without scanning the function, thus allowing the cost of @llvm.assume
handling to be negligible when none are present.
The current design is intended to be lightweight. We don't keep track of
anything until we need a list of assumptions in some function. The first time
this happens, we scan the function. After that, we add/remove @llvm.assume
calls from the cache in response to registration calls and ValueHandle
callbacks.
There are no new direct test cases for this pass, but because it calls it
validation function upon module finalization, we'll pick up detectable
inconsistencies from the other tests that touch @llvm.assume calls.
This pass will be used by follow-up commits that make use of @llvm.assume.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@217334 91177308-0d34-0410-b5e6-96231b3b80d8
This provides an implementation of CFL alias analysis (including some
supporting data structures). Currently, we don't have any extremely fancy
features, sans some interprocedural analysis (i.e. no field sensitivity, etc.),
and we do best sitting behind BasicAA + TBAA. In such a configuration, we take
~0.6-0.8% of total compile time, and give ~7-8% NoAlias responses to queries
TBAA and BasicAA couldn't answer when bootstrapping LLVM. In testing this on
other projects, we've seen up to 10.5% of queries dropped by BasicAA+TBAA
answered with NoAlias by this algorithm.
Patch by George Burgess IV (with minor modifications by me -- mostly adapting
some BasicAA tests), thanks!
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Take a StringRef instead of a "const char *".
Take a "std::error_code &" instead of a "std::string &" for error.
A create static method would be even better, but this patch is already a bit too
big.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@216393 91177308-0d34-0410-b5e6-96231b3b80d8
This patch adds support to recognize division by uniform power of 2 and modifies the cost table to vectorize division by uniform power of 2 whenever possible.
Updates Cost model for Loop and SLP Vectorizer.The cost table is currently only updated for X86 backend.
Thanks to Hal, Andrea, Sanjay for the review. (http://reviews.llvm.org/D4971)
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@216371 91177308-0d34-0410-b5e6-96231b3b80d8
I added wrapping to the CFGPrinter a while back so the -view-cfg
output is actually viewable. I've since enountered very long mangled
names with the same problem, so I'm slightly tweaking this code to
work in that case.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@216087 91177308-0d34-0410-b5e6-96231b3b80d8
Add header guards to files that were missing guards. Remove #endif comments
as they don't seem common in LLVM (we can easily add them back if we decide
they're useful)
Changes made by clang-tidy with minor tweaks.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@215558 91177308-0d34-0410-b5e6-96231b3b80d8
it breaks the modules builds (where CallGraph.h can be quite reasonably
transitively included by an unimported portion of a module, and CallGraph.cpp
not linked in), and appears to have been entirely redundant since PR780 was
fixed back in 2008.
If this breaks anything, please revert; I have only tested this with a single
configuration, and it's possible that this is still somehow fixing something
(though I doubt it, since no other similar file uses this mechanism any more).
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Some types, such as 128-bit vector types on AArch64, don't have any callee-saved registers. So if a value needs to stay live over a callsite, it must be spilled and refilled. This cost is now taken into account.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@214859 91177308-0d34-0410-b5e6-96231b3b80d8
This commit adds scoped noalias metadata. The primary motivations for this
feature are:
1. To preserve noalias function attribute information when inlining
2. To provide the ability to model block-scope C99 restrict pointers
Neither of these two abilities are added here, only the necessary
infrastructure. In fact, there should be no change to existing functionality,
only the addition of new features. The logic that converts noalias function
parameters into this metadata during inlining will come in a follow-up commit.
What is added here is the ability to generally specify noalias memory-access
sets. Regarding the metadata, alias-analysis scopes are defined similar to TBAA
nodes:
!scope0 = metadata !{ metadata !"scope of foo()" }
!scope1 = metadata !{ metadata !"scope 1", metadata !scope0 }
!scope2 = metadata !{ metadata !"scope 2", metadata !scope0 }
!scope3 = metadata !{ metadata !"scope 2.1", metadata !scope2 }
!scope4 = metadata !{ metadata !"scope 2.2", metadata !scope2 }
Loads and stores can be tagged with an alias-analysis scope, and also, with a
noalias tag for a specific scope:
... = load %ptr1, !alias.scope !{ !scope1 }
... = load %ptr2, !alias.scope !{ !scope1, !scope2 }, !noalias !{ !scope1 }
When evaluating an aliasing query, if one of the instructions is associated
with an alias.scope id that is identical to the noalias scope associated with
the other instruction, or is a descendant (in the scope hierarchy) of the
noalias scope associated with the other instruction, then the two memory
accesses are assumed not to alias.
Note that is the first element of the scope metadata is a string, then it can
be combined accross functions and translation units. The string can be replaced
by a self-reference to create globally unqiue scope identifiers.
[Note: This overview is slightly stylized, since the metadata nodes really need
to just be numbers (!0 instead of !scope0), and the scope lists are also global
unnamed metadata.]
Existing noalias metadata in a callee is "cloned" for use by the inlined code.
This is necessary because the aliasing scopes are unique to each call site
(because of possible control dependencies on the aliasing properties). For
example, consider a function: foo(noalias a, noalias b) { *a = *b; } that gets
inlined into bar() { ... if (...) foo(a1, b1); ... if (...) foo(a2, b2); } --
now just because we know that a1 does not alias with b1 at the first call site,
and a2 does not alias with b2 at the second call site, we cannot let inlining
these functons have the metadata imply that a1 does not alias with b2.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@213864 91177308-0d34-0410-b5e6-96231b3b80d8
In order to enable the preservation of noalias function parameter information
after inlining, and the representation of block-level __restrict__ pointer
information (etc.), additional kinds of aliasing metadata will be introduced.
This metadata needs to be carried around in AliasAnalysis::Location objects
(and MMOs at the SDAG level), and so we need to generalize the current scheme
(which is hard-coded to just one TBAA MDNode*).
This commit introduces only the necessary refactoring to allow for the
introduction of other aliasing metadata types, but does not actually introduce
any (that will come in a follow-up commit). What it does introduce is a new
AAMDNodes structure to hold all of the aliasing metadata nodes associated with
a particular memory-accessing instruction, and uses that structure instead of
the raw MDNode* in AliasAnalysis::Location, etc.
No functionality change intended.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@213859 91177308-0d34-0410-b5e6-96231b3b80d8
As it turns out, the capture tracker named CaptureBefore used by AA, and now
available via the PointerMayBeCapturedBefore function, would have been
more-aptly named CapturedBeforeOrAt, because it considers captures at the
instruction provided. This is not always what one wants, and it is difficult to
get the strictly-before behavior given only the current interface. This adds an
additional parameter which controls whether or not you want to include
captures at the provided instruction. The default is not to include the
instruction provided, so that 'Before' matches its name.
No functionality change intended.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@213582 91177308-0d34-0410-b5e6-96231b3b80d8
There were two generally-useful CaptureTracker classes defined in LLVM: the
simple tracker defined in CaptureTracking (and made available via the
PointerMayBeCaptured utility function), and the CapturesBefore tracker
available only inside of AA. This change moves the CapturesBefore tracker into
CaptureTracking, generalizes it slightly (by adding a ReturnCaptures
parameter), and makes it generally available via a PointerMayBeCapturedBefore
utility function.
This logic will be needed, for example, to perform noalias function parameter
attribute inference.
No functionality change intended.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@213519 91177308-0d34-0410-b5e6-96231b3b80d8
The ability to identify function locals will exist outside of BasicAA (for
example, logic for inferring noalias function arguments will need this), so
make this concept generally accessible without code duplication.
No functionality change.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@213514 91177308-0d34-0410-b5e6-96231b3b80d8
This reverts, "r213024 - Revert r212572 "improve BasicAA CS-CS queries", it
causes PR20303." with a fix for the bug in pr20303. As it turned out, the
relevant code was both wrong and over-conservative (because, as with the code
it replaced, it would return the overall ModRef mask even if just Ref had been
implied by the argument aliasing results). Hopefully, this correctly fixes both
problems.
Thanks to Nick Lewycky for reducing the test case for pr20303 (which I've
cleaned up a little and added in DSE's test directory). The BasicAA test has
also been updated to check for this error.
Original commit message:
BasicAA contains knowledge of certain intrinsics, such as memcpy and memset,
and uses that information to form more-accurate answers to CallSite vs. Loc
ModRef queries. Unfortunately, it did not use this information when answering
CallSite vs. CallSite queries.
Generically, when an intrinsic takes one or more pointers and the intrinsic is
marked only to read/write from its arguments, the offset/size is unknown. As a
result, the generic code that answers CallSite vs. CallSite (and CallSite vs.
Loc) queries in AA uses UnknownSize when forming Locs from an intrinsic's
arguments. While BasicAA's CallSite vs. Loc override could use more-accurate
size information for some intrinsics, it did not do the same for CallSite vs.
CallSite queries.
This change refactors the intrinsic-specific logic in BasicAA into a generic AA
query function: getArgLocation, which is overridden by BasicAA to supply the
intrinsic-specific knowledge, and used by AA's generic implementation. This
allows the intrinsic-specific knowledge to be used by both CallSite vs. Loc and
CallSite vs. CallSite queries, and simplifies the BasicAA implementation.
Currently, only one function, Mac's memset_pattern16, is handled by BasicAA
(all the rest are intrinsics). As a side-effect of this refactoring, BasicAA's
getModRefBehavior override now also returns OnlyAccessesArgumentPointees for
this function (which is an improvement).
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@213219 91177308-0d34-0410-b5e6-96231b3b80d8
Implementation is small now -- the interesting logic was moved to
`BranchProbability` a while ago. Move it into `bfi_detail` and get rid
of the related TODOs.
I was originally planning to define it within `BlockFrequencyInfoImpl`
(or `BFIIBase`), but it seems cleaner in a namespace. Besides,
`isPodLike` needs to be specialized before `BlockMass` can be used in
some of the other data structures, and there isn't a clear way to do
that.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@212866 91177308-0d34-0410-b5e6-96231b3b80d8
BasicAA contains knowledge of certain intrinsics, such as memcpy and memset,
and uses that information to form more-accurate answers to CallSite vs. Loc
ModRef queries. Unfortunately, it did not use this information when answering
CallSite vs. CallSite queries.
Generically, when an intrinsic takes one or more pointers and the intrinsic is
marked only to read/write from its arguments, the offset/size is unknown. As a
result, the generic code that answers CallSite vs. CallSite (and CallSite vs.
Loc) queries in AA uses UnknownSize when forming Locs from an intrinsic's
arguments. While BasicAA's CallSite vs. Loc override could use more-accurate
size information for some intrinsics, it did not do the same for CallSite vs.
CallSite queries.
This change refactors the intrinsic-specific logic in BasicAA into a generic AA
query function: getArgLocation, which is overridden by BasicAA to supply the
intrinsic-specific knowledge, and used by AA's generic implementation. This
allows the intrinsic-specific knowledge to be used by both CallSite vs. Loc and
CallSite vs. CallSite queries, and simplifies the BasicAA implementation.
Currently, only one function, Mac's memset_pattern16, is handled by BasicAA
(all the rest are intrinsics). As a side-effect of this refactoring, BasicAA's
getModRefBehavior override now also returns OnlyAccessesArgumentPointees for
this function (which is an improvement).
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@212572 91177308-0d34-0410-b5e6-96231b3b80d8
This is and always was strong community consensus. Make this clear in the header
in case newcomers may not be aware.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@212570 91177308-0d34-0410-b5e6-96231b3b80d8
string_ostream is a safe and efficient string builder that combines opaque
stack storage with a built-in ostream interface.
small_string_ostream<bytes> additionally permits an explicit stack storage size
other than the default 128 bytes to be provided. Beyond that, storage is
transferred to the heap.
This convenient class can be used in most places an
std::string+raw_string_ostream pair or SmallString<>+raw_svector_ostream pair
would previously have been used, in order to guarantee consistent access
without byte truncation.
The patch also converts much of LLVM to use the new facility. These changes
include several probable bug fixes for truncated output, a programming error
that's no longer possible with the new interface.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@211749 91177308-0d34-0410-b5e6-96231b3b80d8
ScaledNumber has been cleaned up enough to pull out of BFI now. Still
work to do there (tests for shifting, bloated printing code, etc.), but
it seems clean enough for its new home.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@211562 91177308-0d34-0410-b5e6-96231b3b80d8
This patch adds support to recognize patterns such as fadd,fsub,fadd,fsub.../add,sub,add,sub... and
vectorizes them as vector shuffles if they are profitable.
These patterns of vector shuffle can later be converted to instructions such as addsubpd etc on X86.
Thanks to Arnold and Hal for the reviews. http://reviews.llvm.org/D4015
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@211339 91177308-0d34-0410-b5e6-96231b3b80d8
Start extracting helper functions out of -block-freq's `UnsignedFloat`
into `Support/ScaledNumber.h` with the eventual goal of moving and
renaming the class to `ScaledNumber`.
The bike shed about names is still being painted, but I'm going with
this for now.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@211333 91177308-0d34-0410-b5e6-96231b3b80d8
Summary:
With this patch, range metadata can be added to call/invoke including
IntrinsicInst. Previously, it could only be added to load.
Rename computeKnownBitsLoad to computeKnownBitsFromRangeMetadata because
range metadata is not only used by load.
Update the language reference to reflect this change.
Test Plan:
Add several tests in range-2.ll to confirm the verifier is happy with
having range metadata on call/invoke.
Add two tests in AddOverFlow.ll to confirm annotating range metadata to
call/invoke can benefit InstCombine.
Reviewers: meheff, nlewycky, reames, hfinkel, eliben
Reviewed By: eliben
Subscribers: llvm-commits
Differential Revision: http://reviews.llvm.org/D4187
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@211281 91177308-0d34-0410-b5e6-96231b3b80d8
It includes a pass that rewrites all indirect calls to jumptable functions to pass through these tables.
This also adds backend support for generating the jump-instruction tables on ARM and X86.
Note that since the jumptable attribute creates a second function pointer for a
function, any function marked with jumptable must also be marked with unnamed_addr.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@210280 91177308-0d34-0410-b5e6-96231b3b80d8
No functional change is intended: instead of relying on the delinearization to
come up with the base pointer as a remainder of the divisions in the
delinearization, we just compute it from the array access and use that value.
We substract the base pointer from the SCEV to be delinearized and that
simplifies the work of the delinearizer.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@209692 91177308-0d34-0410-b5e6-96231b3b80d8
The delinearization is needed only to remove the non linearity induced by
expressions involving multiplications of parameters and induction variables.
There is no problem in dealing with constant times parameters, or constant times
an induction variable.
For this reason, the current patch discards all constant terms and multipliers
before running the delinearization algorithm on the terms. The only thing
remaining in the term expressions are parameters and multiply expressions of
parameters: these simplified term expressions are passed to the array shape
recognizer that will not recognize constant dimensions anymore: these will be
recognized as different strides in parametric subscripts.
The only important special case of a constant dimension is the size of elements.
Instead of relying on the delinearization to infer the size of an element,
compute the element size from the base address type. This is a much more precise
way of computing the element size than before, as we would have mixed together
the size of an element with the strides of the innermost dimension.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@209691 91177308-0d34-0410-b5e6-96231b3b80d8
we do not use the information from SCEVAddRecExpr to compute the shape of the array,
so a better place for this function is in ScalarEvolution.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@208456 91177308-0d34-0410-b5e6-96231b3b80d8
To compute the dimensions of the array in a unique way, we split the
delinearization analysis in three steps:
- find parametric terms in all memory access functions
- compute the array dimensions from the set of terms
- compute the delinearized access functions for each dimension
The first step is executed on all the memory access functions such that we
gather all the patterns in which an array is accessed. The second step reduces
all this information in a unique description of the sizes of the array. The
third step is delinearizing each memory access function following the common
description of the shape of the array computed in step 2.
This rewrite of the delinearization pass also solves a problem we had with the
previous implementation: because the previous algorithm was by induction on the
structure of the SCEV, it would not correctly recognize the shape of the array
when the memory access was not following the nesting of the loops: for example,
see polly/test/ScopInfo/multidim_only_ivs_3d_reverse.ll
; void foo(long n, long m, long o, double A[n][m][o]) {
;
; for (long i = 0; i < n; i++)
; for (long j = 0; j < m; j++)
; for (long k = 0; k < o; k++)
; A[i][k][j] = 1.0;
Starting with this patch we no longer delinearize access functions that do not
contain parameters, for example in test/Analysis/DependenceAnalysis/GCD.ll
;; for (long int i = 0; i < 100; i++)
;; for (long int j = 0; j < 100; j++) {
;; A[2*i - 4*j] = i;
;; *B++ = A[6*i + 8*j];
these accesses will not be delinearized as the upper bound of the loops are
constants, and their access functions do not contain SCEVUnknown parameters.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@208232 91177308-0d34-0410-b5e6-96231b3b80d8
operations on the call graph. This one forms a cycle, and while not as
complex as removing an internal edge from an SCC, it involves
a reasonable amount of work to find all of the nodes newly connected in
a cycle.
Also somewhat alarming is the worst case complexity here: it might have
to walk roughly the entire SCC inverse DAG to insert a single edge. This
is carefully documented in the API (I hope).
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just connects an SCC to one of its descendants directly. Not much of an
impact. The last one is the hard one -- connecting an SCC to one of its
ancestors, and thereby forming a cycle such that we have to merge all
the SCCs participating in the cycle.
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of SCCs in the SCC DAG. Exercise them in the big graph test case. These
will be especially useful for establishing invariants in insertion
logic.
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removal. We can't just blindly increment (or decrement) the adapted
iterator when the value is null because doing so can walk past the end
(or beginning) and keep inspecting the value. The fix I've implemented
is to restrict this further to a forward iterator and add an end
iterator to the members (replacing a member that had become dead when
I switched to the adaptor base!) and using that to stop the iteration.
I'm not entirely pleased with this solution. I feel like forward
iteration is too restrictive. I wasn't even happy about bidirectional
iteration. It also makes the iterator objects larger and the iteration
loops more complex. However, I also don't really like the other
alternative that seems obvious: a sentinel node. I'm still hoping to
come up with a more elegant solution here, but this at least fixes the
MSan and Valgrind errors on this code.
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edge entirely within an existing SCC. Shockingly, making the connected
component more connected is ... a total snooze fest. =]
Anyways, its wired up, and I even added a test case to make sure it
pretty much sorta works. =D
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bits), and discover that it's totally broken. Yay tests. Boo bug. Fix
the basic edge removal so that it works by nulling out the removed edges
rather than actually removing them. This leaves the indices valid in the
map from callee to index, and preserves some of the locality for
iterating over edges. The iterator is made bidirectional to reflect that
it now has to skip over null entries, and the skipping logic is layered
onto it.
As future work, I would like to track essentially the "load factor" of
the edge list, and when it falls below a threshold do a compaction.
An alternative I considered (and continue to consider) is storing the
callees in a doubly linked list where each element of the list is in
a set (which is essentially the classical linked-hash-table
datastructure). The problem with that approach is that either you need
to heap allocate the linked list nodes and use pointers to them, or use
a bucket hash table (with even *more* linked list pointer overhead!),
etc. It's pretty easy to get 5x overhead for values that are just
pointers. So far, I think punching holes in the vector, and periodic
compaction is likely to be much more efficient overall in the space/time
tradeoff.
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Since `BlockMass` is an implementation detail and there are no current
users of this, delete `BlockMass::operator*=(BlockMass)`. I might need
this when I try to strip out `UnsignedFloat`, but I can pull it back in
at that point.
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Summary:
This calls emitOptimizationRemark from the loop unroller and vectorizer
at the point where they make a positive transformation. For the
vectorizer, it reports vectorization and interleave factors. For the
loop unroller, it reports all the different supported types of
unrolling.
Subscribers: llvm-commits
Differential Revision: http://reviews.llvm.org/D3456
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requiring full control over the various parameters to the std::iterator
concept / trait thing. This is a precursor for adjusting these things to
where you can write a bidirectional iterator wrapping a random access
iterator with custom increment and decrement logic.
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This reverts commit r207287, reapplying r207286.
I'm hoping that declaring an explicit struct and instantiating
`addBlockEdges()` directly works around the GCC crash from r207286.
This is a lot more boilerplate, though.
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API requirements much more obvious.
The key here is that there are two totally different use cases for
mutating the graph. Prior to doing any SCC formation, it is very easy to
mutate the graph. There may be users that want to do small tweaks here,
and then use the already-built graph for their SCC-based operations.
This method remains on the graph itself and is documented carefully as
being cheap but unavailable once SCCs are formed.
Once SCCs are formed, and there is some in-flight DFS building them, we
have to be much more careful in how we mutate the graph. These mutation
operations are sunk onto the SCCs themselves, which both simplifies
things (the code was already there!) and helps make it obvious that
these interfaces are only applicable within that context. The other
primary constraint is that the edge being mutated is actually related to
the SCC on which we call the method. This helps make it obvious that you
cannot arbitrarily mutate some other SCC.
I've tried to write much more complete documentation for the interesting
mutation API -- intra-SCC edge removal. Currently one aspect of this
documentation is a lie (the result list of SCCs) but we also don't even
have tests for that API. =[ I'm going to add tests and fix it to match
the documentation next.
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them, just skip over any DFS-numbered nodes when finding the next root
of a DFS. This allows the entry set to just be a vector as we populate
it from a uniqued source. It also removes the possibility for a linear
scan of the entry set to actually do the removal which can make things
go quadratic if we get unlucky.
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makes working through the worklist much cleaner, and makes it possible
to avoid the 'bool-to-continue-the-outer-loop' hack. Not a huge
difference, but I think this is approaching as polished as I can make
it.
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processed in the DFS out of the stack completely. Keep it exclusively in
a variable. Re-shuffle some code structure to make this easier. This can
have a very dramatic effect in some cases because call graphs tend to
look like a high fan-out spanning tree. As a consequence, there are
a large number of leaf nodes in the graph, and this technique causes
leaf nodes to never even go into the stack. While this only reduces the
max depth by 1, it may cause the total number of round trips through the
stack to drop by a lot.
Now, most of this isn't really relevant for the incremental version. =]
But I wanted to prototype it first here as this variant is in ways more
complex. As long as I can get the code factored well here, I'll next
make the primary walk look the same. There are several refactorings this
exposes I think.
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a helper function. Also factor the other two places where we did the
same thing into the helper function. =] Much cleaner this way. NFC.
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Previously, irreducible backedges were ignored. With this commit,
irreducible SCCs are discovered on the fly, and modelled as loops with
multiple headers.
This approximation specifies the headers of irreducible sub-SCCs as its
entry blocks and all nodes that are targets of a backedge within it
(excluding backedges within true sub-loops). Block frequency
calculations act as if we insert a new block that intercepts all the
edges to the headers. All backedges and entries to the irreducible SCC
point to this imaginary block. This imaginary block has an edge (with
even probability) to each header block.
The result is now reasonable enough that I've added a number of
testcases for irreducible control flow. I've outlined in
`BlockFrequencyInfoImpl.h` ways to improve the approximation.
<rdar://problem/14292693>
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Move a lot of the loop-related logic that was sprinkled around the code
into `LoopData`.
<rdar://problem/14292693>
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Remove the concepts of "forward" and "general" mass distributions, which
was wrong. The split might have made sense in an early version of the
algorithm, but it's definitely wrong now.
<rdar://problem/14292693>
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Make `getPackagedNode()` a member function of
`BlockFrequencyInfoImplBase` so that it's available for templated code.
<rdar://problem/14292693>
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Continue refactoring to make `LoopData` first-class. Here I'm making
the `LoopData` hierarchy explicit, instead of bouncing back and forth
with `WorkingData`. This simplifies the logic and better matches the
`LoopInfo` design. (Eventually, `LoopInfo` should be restructured so
that it supports this pass, and `LoopData` can be removed.)
<rdar://problem/14292693>
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As pointed out by David Blaikie in code review, a `std::list<T>` is
simpler than a `std::vector<std::unique_ptr<T>>`. Another option is a
`std::deque<T>` (which allocates in chunks), but I'd like to leave open
the option of inserting in the middle of the sequence for handling
irreducible control flow on the fly.
<rdar://problem/14292693>
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algorithm here: http://dl.acm.org/citation.cfm?id=177301.
The idea of isolating the roots has even more relevance when using the
stack not just to implement the DFS but also to implement the recursive
step. Because we use it for the recursive step, to isolate the roots we
need to maintain two stacks: one for our recursive DFS walk, and another
of the nodes that have been walked. The nice thing is that the latter
will be half the size. It also fixes a complete hack where we scanned
backwards over the stack to find the next potential-root to continue
processing. Now that is always the top of the DFS stack.
While this is a really nice improvement already (IMO) it further opens
the door for two important simplifications:
1) De-duplicating some of the code across the two different walks. I've
actually made the duplication a bit worse in some senses with this
patch because the two are starting to converge.
2) Dramatically simplifying the loop structures of both walks.
I wanted to do those separately as they'll be essentially *just* CFG
restructuring. This patch on the other hand actually uses different
datastructures to implement the algorithm itself.
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a SmallPtrSet. Currently, there is no need for stable iteration in this
dimension, and I now thing there won't need to be going forward.
If this is ever re-introduced in any form, it needs to not be
a SetVector based solution because removal cannot be linear. There will
be many SCCs with large numbers of parents. When encountering these, the
incremental SCC update for intra-SCC edge removal was quadratic due to
linear removal (kind of).
I'm really hoping we can avoid having an ordering property here at all
though...
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than functions. So far, this access pattern is *much* more common. It
seems likely that any user of this interface is going to have nodes at
the point that they are querying the SCCs.
No functionality changed.
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This implements the core functionality necessary to remove an edge from
the call graph and correctly update both the basic graph and the SCC
structure. As part of that it has to run a tiny (in number of nodes)
Tarjan-style DFS walk of an SCC being mutated to compute newly formed
SCCs, etc.
This is *very rough* and a WIP. I have a bunch of FIXMEs for code
cleanup that will reduce the boilerplate in this change substantially.
I also have a bunch of simplifications to various parts of both
algorithms that I want to make, but first I'd like to have a more
holistic picture. Ideally, I'd also like more testing. I'll probably add
quite a few more unit tests as I go here to cover the various different
aspects and corner cases of removing edges from the graph.
Still, this is, so far, successfully updating the SCC graph in-place
without disrupting the identity established for the existing SCCs even
when we do challenging things like delete the critical edge that made an
SCC cycle at all and have to reform things as a tree of smaller SCCs.
Getting this to work is really critical for the new pass manager as it
is going to associate significant state with the SCC instance and needs
it to be stable. That is also the motivation behind the return of the
newly formed SCCs. Eventually, I'll wire this all the way up to the
public API so that the pass manager can use it to correctly re-enqueue
newly formed SCCs into a fresh postorder traversal.
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up the stack finishing the exploration of each entries children before
we're finished in addition to accounting for their low-links. Added
a unittest that really hammers home the need for this with interlocking
cycles that would each appear distinct otherwise and crash or compute
the wrong result. As part of this, nuke a stale fixme and bring the rest
of the implementation still more closely in line with the original
algorithm.
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parents of an SCC, and add a lookup method for finding the SCC for
a given function. These aren't used yet, but will be used shortly in
some unit tests I'm adding and are really part of the broader intended
interface for the analysis.
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resisted this for too long. Just with the basic testing here I was able
to exercise the analysis in more detail and sift out both type signature
bugs in the API and a bug in the DFS numbering. All of these are fixed
here as well.
The unittests will be much more important for the mutation support where
it is necessary to craft minimal mutations and then inspect the state of
the graph. There is just no way to do that with a standard FileCheck
test. However, unittesting these kinds of analyses is really quite easy,
especially as they're designed with the new pass manager where there is
essentially no infrastructure required to rig up the core logic and
exercise it at an API level.
As a minor aside about the DFS numbering bug, the DFS numbering used in
LCG is a bit unusual. Rather than numbering from 0, we number from 1,
and use 0 as the sentinel "unvisited" state. Other implementations often
use '-1' for this, but I find it easier to deal with 0 and it shouldn't
make any real difference provided someone doesn't write silly bugs like
forgetting to actually initialize the DFS numbering. Oops. ;]
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into a helper function. I plan to re-use it for doing incremental
DFS-based updates to the SCCs when we mutate the call graph.
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the Callee list. This is going to be quite important to prevent removal
from going quadratic. No functionality changed at this point, this is
one of the refactoring patches I've broken out of my initial work toward
mutation updates of the call graph.
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Store pointers directly to loops inside the nodes. This could have been
done without changing the type stored in `std::vector<>`. However,
rather than computing the number of loops before constructing them
(which `LoopInfo` doesn't provide directly), I've switched to a
`vector<unique_ptr<LoopData>>`.
This adds some heap overhead, but the number of loops is typically
small.
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define below all header includes in the lib/CodeGen/... tree. While the
current modules implementation doesn't check for this kind of ODR
violation yet, it is likely to grow support for it in the future. It
also removes one layer of macro pollution across all the included
headers.
Other sub-trees will follow.
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Change `PositiveFloat` to `UnsignedFloat`, and fix some of the comments
to indicate that it's disappearing eventually.
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This reverts commit r206707, reapplying r206704. The preceding commit
to CalcSpillWeights should have sorted out the failing buildbots.
<rdar://problem/14292693>
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LazyCallGraph analysis framework. Wire it up all the way through the opt
driver and add some very basic testing that we can build pass pipelines
including these components. Still a lot more to do in terms of testing
that all of this works, but the basic pieces are here.
There is a *lot* of boiler plate here. It's something I'm going to
actively look at reducing, but I don't have any immediate ideas that
don't end up making the code terribly complex in order to fold away the
boilerplate. Until I figure out something to minimize the boilerplate,
almost all of this is based on the code for the existing pass managers,
copied and heavily adjusted to suit the needs of the CGSCC pass
management layer.
The actual CG management still has a bunch of FIXMEs in it. Notably, we
don't do *any* updating of the CG as it is potentially invalidated.
I wanted to get this in place to motivate the new analysis, and add
update APIs to the analysis and the pass management layers in concert to
make sure that the *right* APIs are present.
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This reverts commit r206677, reapplying my BlockFrequencyInfo rewrite.
I've done a careful audit, added some asserts, and fixed a couple of
bugs (unfortunately, they were in unlikely code paths). There's a small
chance that this will appease the failing bots [1][2]. (If so, great!)
If not, I have a follow-up commit ready that will temporarily add
-debug-only=block-freq to the two failing tests, allowing me to compare
the code path between what the failing bots and what my machines (and
the rest of the bots) are doing. Once I've triggered those builds, I'll
revert both commits so the bots go green again.
[1]: http://bb.pgr.jp/builders/ninja-x64-msvc-RA-centos6/builds/1816
[2]: http://llvm-amd64.freebsd.your.org/b/builders/clang-i386-freebsd/builds/18445
<rdar://problem/14292693>
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This reverts commit r206666, as planned.
Still stumped on why the bots are failing. Sanitizer bots haven't
turned anything up. If anyone can help me debug either of the failures
(referenced in r206666) I'll owe them a beer. (In the meantime, I'll be
auditing my patch for undefined behaviour.)
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This reverts commit r206628, reapplying r206622 (and r206626).
Two tests are failing only on buildbots [1][2]: i.e., I can't reproduce
on Darwin, and Chandler can't reproduce on Linux. Asan and valgrind
don't tell us anything, but we're hoping the msan bot will catch it.
So, I'm applying this again to get more feedback from the bots. I'll
leave it in long enough to trigger builds in at least the sanitizer
buildbots (it was failing for reasons unrelated to my commit last time
it was in), and hopefully a few others.... and then I expect to revert a
third time.
[1]: http://bb.pgr.jp/builders/ninja-x64-msvc-RA-centos6/builds/1816
[2]: http://llvm-amd64.freebsd.your.org/b/builders/clang-i386-freebsd/builds/18445
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This reverts commit r206622 and the MSVC fixup in r206626.
Apparently the remotely failing tests are still failing, despite my
attempt to fix the nondeterminism in r206621.
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This reverts commit r206556, effectively reapplying commit r206548 and
its fixups in r206549 and r206550.
In an intervening commit I've added target triples to the tests that
were failing remotely [1] (but passing locally). I'm hoping the mystery
is solved? I'll revert this again if the tests are still failing
remotely.
[1]: http://bb.pgr.jp/builders/ninja-x64-msvc-RA-centos6/builds/1816
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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.
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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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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.
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to a more normal move operation on the graph itself. The definition
already got removed, but I missed the declaration.
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This will become necessary to build up the SCC iterators and SCC
definitions. Moving it now so that subsequent diffs are incremental.
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graph. This simplifies the custom move constructor operation to one of
walking the graph and updating the 'up' pointers to point to the new
location of the graph. Switch the nodes from a reference to a pointer
for the 'up' edge to facilitate this.
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It doesn't work. I'm still cleaning up all the places where I blindly
followed this pattern. There are more to come in this code too.
As a benefit, this lets the default copy and move operations Just Work.
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Moves redundant template parameters into an implementation detail of
BlockFrequencyInfoImpl.
No functionality change.
<rdar://problem/14292693>
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This is a shared implementation class for BlockFrequencyInfo and
MachineBlockFrequencyInfo, not for BlockFrequency, a related (but
distinct) class.
No functionality change.
<rdar://problem/14292693>
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Also updated as many loops as I could find using df_begin/idf_begin -
strangely I found no uses of idf_begin. Is that just used out of tree?
Also a few places couldn't use df_begin because either they used the
member functions of the depth first iterators or had specific ordering
constraints (I added a comment in the latter case).
Based on a patch by Jim Grosbach. (Jim - you just had iterator_range<T>
where you needed iterator_range<idf_iterator<T>>)
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In preparation for an upcoming commit implementing unrolling preferences for
x86, this adds additional fields to the UnrollingPreferences structure:
- PartialThreshold and PartialOptSizeThreshold - Like Threshold and
OptSizeThreshold, but used when not fully unrolling. These are necessary
because we need different thresholds for full unrolling from those used when
partially unrolling (the full unrolling thresholds are generally going to be
larger).
- MaxCount - A cap on the unrolling factor when partially unrolling. This can
be used by a target to prevent the unrolled loop from exceeding some
resource limit independent of the loop size (such as number of branches).
There should be no functionality change for any in-tree targets.
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Implement Pass::releaseMemory() in BlockFrequencyInfo and
MachineBlockFrequencyInfo. Just delete the private implementation when
not in use. Switch to a std::unique_ptr to make the logic more clear.
<rdar://problem/14292693>
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Extend the target hook to take also the operand index into account when
calculating the cost of the constant materialization.
Related to <rdar://problem/16381500>
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This commit extends the coverage of the constant hoisting pass, adds additonal
debug output and updates the function names according to the style guide.
Related to <rdar://problem/16381500>
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as well. I don't see any particular need but it imposes no cost to
support it and it makes the API cleaner.
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the stack of the analysis group because they are all immutable passes.
This is made clear by Craig's recent work to use override
systematically -- we weren't overriding anything for 'finalizePass'
because there is no such thing.
This is kind of a lame restriction on the API -- we can no longer push
and pop things, we just set up the stack and run. However, I'm not
invested in building some better solution on top of the existing
(terrifying) immutable pass and legacy pass manager.
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This compiles with no changes to clang/lld/lldb with MSVC and includes
overloads to various functions which are used by those projects and llvm
which have OwningPtr's as parameters. This should allow out of tree
projects some time to move. There are also no changes to libs/Target,
which should help out of tree targets have time to move, if necessary.
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to ensure we don't mess up any of the overrides. Necessary for cleaning
up the Value use iterators and enabling range-based traversing of use
lists.
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a bit surprising, as the class is almost entirely abstracted away from
any particular IR, however it encodes the comparsion predicates which
mutate ranges as ICmp predicate codes. This is reasonable as they're
used for both instructions and constants. Thus, it belongs in the IR
library with instructions and constants.
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this would have been required because of the use of DataLayout, but that
has moved into the IR proper. It is still required because this folder
uses the constant folding in the analysis library (which uses the
datalayout) as the more aggressive basis of its folder.
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Move the test for this class into the IR unittests as well.
This uncovers that ValueMap too is in the IR library. Ironically, the
unittest for ValueMap is useless in the Support library (honestly, so
was the ValueHandle test) and so it already lives in the IR unittests.
Mmmm, tasty layering.
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business.
This header includes Function and BasicBlock and directly uses the
interfaces of both classes. It has to do with the IR, it even has that
in the name. =] Put it in the library it belongs to.
This is one step toward making LLVM's Support library survive a C++
modules bootstrap.
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Eventually DataLayoutPass should go away, but for now that is the only easy
way to get a DataLayout in some APIs. This patch only changes the ones that
have easy access to a Module.
One interesting issue with sometimes using DataLayoutPass and sometimes
fetching it from the Module is that we have to make sure they are equivalent.
We can get most of the way there by always constructing the pass with a Module.
In fact, the pass could be changed to point to an external DataLayout instead
of owning one to make this stricter.
Unfortunately, the C api passes a DataLayout, so it has to be up to the caller
to make sure the pass and the module are in sync.
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After this I will set the default back to F_None. The advantage is that
before this patch forgetting to set F_Binary would corrupt a file on windows.
Forgetting to set F_Text produces one that cannot be read in notepad, which
is a better failure mode :-)
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During LSR of one loop we can run into a situation where we have to expand the
start of a recurrence of a loop induction variable in this loop. This start
value is a value derived of the induction variable of a preceeding loop. SCEV
has cannonicalized this value to a different recurrence than the recurrence of
the preceeding loop's induction variable (the type and/or step direction) has
changed). When we come to instantiate this SCEV we created a second induction
variable in this preceeding loop. This patch tries to base such derived
induction variables of the preceeding loop's induction variable.
This helps twolf on arm and seems to help scimark2 on x86.
Reapply with a fix for the case of a value derived from a pointer.
radar://15970709
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During LSR of one loop we can run into a situation where we have to expand the
start of a recurrence of a loop induction variable in this loop. This start
value is a value derived of the induction variable of a preceeding loop. SCEV
has cannonicalized this value to a different recurrence than the recurrence of
the preceeding loop's induction variable (the type and/or step direction) has
changed). When we come to instantiate this SCEV we created a second induction
variable in this preceeding loop. This patch tries to base such derived
induction variables of the preceeding loop's induction variable.
This helps twolf on arm and seems to help scimark2 on x86.
radar://15970709
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'OK_NonUniformConstValue' to identify operands which are constants but
not constant splats.
The cost model now allows returning 'OK_NonUniformConstValue'
for non splat operands that are instances of ConstantVector or
ConstantDataVector.
With this change, targets are now able to compute different costs
for instructions with non-uniform constant operands.
For example, On X86 the cost of a vector shift may vary depending on whether
the second operand is a uniform or non-uniform constant.
This patch applies the following changes:
- The cost model computation now takes into account non-uniform constants;
- The cost of vector shift instructions has been improved in
X86TargetTransformInfo analysis pass;
- BBVectorize, SLPVectorizer and LoopVectorize now know how to distinguish
between non-uniform and uniform constant operands.
Added a new test to verify that the output of opt
'-cost-model -analyze' is valid in the following configurations: SSE2,
SSE4.1, AVX, AVX2.
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