I am suspecting a bug around updates of loop info for unreachable exits, but don't have a test case. Running this locally on make check didn't reveal anything, we'll see if the expensive checks bots find it.
This patch updates SCEVExpander::expandUnionPredicate to not create
redundant 'or false, x' instructions. While those are trivially
foldable, they can be easily avoided and hinder code that checks the
size/cost of the generated checks before further folds.
I am planning on look into a few other similar improvements to code
generated by SCEVExpander.
I remember a while ago @lebedev.ri working on doing some trivial folds
like that in IRBuilder itself, but there where concerns that such
changes may subtly break existing code.
Reviewed By: reames, lebedev.ri
Differential Revision: https://reviews.llvm.org/D116696
Track all GlobalObjects that reference a given comdat, which allows
determining whether a function in a comdat is dead without scanning
the whole module.
In particular, this makes filterDeadComdatFunctions() have complexity
O(#DeadFunctions) rather than O(#SymbolsInModule), which addresses
half of the compile-time issue exposed by D115545.
Differential Revision: https://reviews.llvm.org/D115864
This does not appear to cause any problems, and it
fixes#50910
Extra tests with a trunc user were added with:
3a239379
...but they don't match either way, so there's an
opportunity to improve the matching further.
The naming has come up as a source of confusion in several recent reviews. onlyWritesMemory is consist with onlyReadsMemory which we use for the corresponding readonly case as well.
This reverts commit ea75be3d9d and
1eb5b6e850.
That commit caused crashes with compilation e.g. like this
(not fixed by the follow-up commit):
$ cat sqrt.c
float a;
b() { sqrt(a); }
$ clang -target x86_64-linux-gnu -c -O2 sqrt.c
Attributes 'readnone and writeonly' are incompatible!
%sqrtf = tail call float @sqrtf(float %0) #1
in function b
fatal error: error in backend: Broken function found, compilation aborted!
isBitOrNoopPointerCastable() returns true if the types are the
same, but it's not actually possible to create a bitcast for all
such types. The assumption seems to be that the user will omit
creating the cast in that case, as it is unnecessary.
Fixes https://github.com/llvm/llvm-project/issues/52994.
All of these functions would be `readnone`, but can't be on platforms
where they can set `errno`. A `writeonly` function with no pointer
arguments can only write (but never read) global state.
Writeonly theoretically allows these calls to be CSE'd (a writeonly call
with the same arguments will always result in the same global stores) or
hoisted out of loops, but that's not implemented currently.
There are a few functions in this list that could be `readnone` instead
of `writeonly`, if someone is interested.
Differential Revision: https://reviews.llvm.org/D116426
Global ctor evaluation currently models memory as a map from Constant*
to Constant*. For this to be correct, it is required that there is
only a single Constant* referencing a given memory location. The
Evaluator tries to ensure this by imposing certain limitations that
could result in ambiguities (by limiting types, casts and GEP formats),
but ultimately still fails, as can be seen in PR51879. The approach
is fundamentally fragile and will get more so with opaque pointers.
My original thought was to instead store memory for each global as an
offset => value representation. However, we also need to make sure
that we can actually rematerialize the modified global initializer
into a Constant in the end, which may not be possible if we allow
arbitrary writes.
What this patch does instead is to represent globals as a MutableValue,
which is either a Constant* or a MutableAggregate*. The mutable
aggregate exists to allow efficient mutation of individual aggregate
elements, as mutating an element on a Constant would require interning
a new constant. When a write to the Constant* is made, it is converted
into a MutableAggregate* as needed.
I believe this should make the evaluator more robust, compatible
with opaque pointers, and a bit simpler as well.
Fixes https://github.com/llvm/llvm-project/issues/51221.
Differential Revision: https://reviews.llvm.org/D115530
This function returns an upper bound on the number of bits needed
to represent the signed value. Use "Max" to match similar functions
in KnownBits like countMaxActiveBits.
Rename APInt::getMinSignedBits->getSignificantBits. Keeping the old
name around to keep this patch size down. Will do a bulk rename as
follow up.
Rename KnownBits::countMaxSignedBits->countMaxSignificantBits.
Reviewed By: lebedev.ri, RKSimon, spatel
Differential Revision: https://reviews.llvm.org/D116522
If we have an exit which is controlled by a loop invariant condition and which dominates the latch, we know only the copy in the first unrolled iteration can be taken. All other copies are dead.
The change itself is pretty straight forward, but let me add two points of context:
* I'd have expected other transform passes to catch this after unrolling, but I'm seeing multiple examples where we get to the end of O2/O3 without simplifying.
* I'd like to do a stronger change which did CSE during unroll and accounted for invariant expressions (as defined by SCEV instead of trivial ones from LoopInfo), but that doesn't fit cleanly into the current code structure.
Differential Revision: https://reviews.llvm.org/D116496
This list is confusing because it conflates functions attributes
(which are either extractable or not) and other attribute kinds,
which are simply irrelevant for this code.
This fixes a typo/bug when checking for pointer reuse when testing
DI location preservation in the Debugify original mode (when
checking -g generated Debug Info).
Differential Revision: https://reviews.llvm.org/D115621
With Control-Flow Integrity (CFI), the LowerTypeTests pass replaces
function references with CFI jump table references, which is a problem
for low-level code that needs the address of the actual function body.
For example, in the Linux kernel, the code that sets up interrupt
handlers needs to take the address of the interrupt handler function
instead of the CFI jump table, as the jump table may not even be mapped
into memory when an interrupt is triggered.
This change adds the no_cfi constant type, which wraps function
references in a value that LowerTypeTestsModule::replaceCfiUses does not
replace.
Link: https://github.com/ClangBuiltLinux/linux/issues/1353
Reviewed By: nickdesaulniers, pcc
Differential Revision: https://reviews.llvm.org/D108478
After this function call, the LLVM IR would look like the following:
```
if (true)
/* NonVersionedLoop */
else
/* VersionedLoop */
```
Reviewed By: Whitney
Differential Revision: https://reviews.llvm.org/D104631
I noticed we weren't propagating tail flags on calls when
FortifiedLibCallSimplifier.optimizeCall() was replacing calls to runtime
checked calls to the non-checked routines (when safe to do so). Make
sure to check this before replacing the original calls!
Also, avoid any libcall transforms when notail/musttail is present.
PR46734
Fixes: https://github.com/llvm/llvm-project/issues/46079
Reviewed By: jdoerfert
Differential Revision: https://reviews.llvm.org/D107872
This patch fixes the relative table converter pass for the lookup table
accesses that are resulted in an instruction sequence, where gep is not
immediately followed by a load, such as gep being hoisted outside the loop
or another instruction is inserted in between them. The fix inserts the
call to load.relative.instrinsic in the original place of load instead of gep.
Issue is reported by FreeBSD via https://bugs.freebsd.org/259921.
Differential Revision: https://reviews.llvm.org/D115571
The code claimed to handle nsw/nuw, but those aren't passed via builder state and the explicit IR construction just above never sets them.
The only case this bit of code is actually relevant for is FMF flags. However, dropPoisonGeneratingFlags currently doesn't know about FMF at all, so this was a noop. It's also unneeded, as the caller explicitly configures the flags on the builder before this call, and the flags on the individual ops should be controled by the intrinsic flags anyways. If any of the flags aren't safe to propagate, the caller needs to make that change.
The recurrence lowering code has handling which claims to be about flag intersection, but all the callers pass empty arrays to the arguments. The sole exception is a caller of a method which has the argument, but no implementation.
I don't know what the intent was here, but it certaintly doesn't actually do anything today.
This change allows us to estimate trip count from profile metadata for all multiple exit loops. We still do the estimate only from the latch, but that's fine as it causes us to over estimate the trip count at worst.
Reviewing the uses of the API, all but one are cases where we restrict a loop transformation (unroll, and vectorize respectively) when we know the trip count is short enough. So, as a result, the change makes these passes strictly less aggressive. The test change illustrates a case where we'd previously have runtime unrolled a loop which ran fewer iterations than the unroll factor. This is definitely unprofitable.
The one case where an upper bound on estimate trip count could drive a more aggressive transform is peeling, and I duplicated the logic being removed from the generic estimation there to keep it the same. The resulting heuristic makes no sense and should probably be immediately removed, but we can do that in a separate change.
This was noticed when analyzing regressions on D113939.
I plan to come back and incorporate estimated trip counts from other exits, but that's a minor improvement which can follow separately.
Differential Revision: https://reviews.llvm.org/D115362
This patch adds 4 options for specifying functions, aliases, globals and
structs name prefixes hat don't need to be renamed by MetaRenamer pass.
This is useful if one has some downstream logic that depends directly
on an entity name. MetaRenamer can break this logic, but with the patch
you can tell it not to rename certain names.
Reviewed By: nikic
Differential Revision: https://reviews.llvm.org/D115323
A new basic block ordering improving existing MachineBlockPlacement.
The algorithm tries to find a layout of nodes (basic blocks) of a given CFG
optimizing jump locality and thus processor I-cache utilization. This is
achieved via increasing the number of fall-through jumps and co-locating
frequently executed nodes together. The name follows the underlying
optimization problem, Extended-TSP, which is a generalization of classical
(maximum) Traveling Salesmen Problem.
The algorithm is a greedy heuristic that works with chains (ordered lists)
of basic blocks. Initially all chains are isolated basic blocks. On every
iteration, we pick a pair of chains whose merging yields the biggest increase
in the ExtTSP value, which models how i-cache "friendly" a specific chain is.
A pair of chains giving the maximum gain is merged into a new chain. The
procedure stops when there is only one chain left, or when merging does not
increase ExtTSP. In the latter case, the remaining chains are sorted by
density in decreasing order.
An important aspect is the way two chains are merged. Unlike earlier
algorithms (e.g., based on the approach of Pettis-Hansen), two
chains, X and Y, are first split into three, X1, X2, and Y. Then we
consider all possible ways of gluing the three chains (e.g., X1YX2, X1X2Y,
X2X1Y, X2YX1, YX1X2, YX2X1) and choose the one producing the largest score.
This improves the quality of the final result (the search space is larger)
while keeping the implementation sufficiently fast.
Differential Revision: https://reviews.llvm.org/D113424
A new basic block ordering improving existing MachineBlockPlacement.
The algorithm tries to find a layout of nodes (basic blocks) of a given CFG
optimizing jump locality and thus processor I-cache utilization. This is
achieved via increasing the number of fall-through jumps and co-locating
frequently executed nodes together. The name follows the underlying
optimization problem, Extended-TSP, which is a generalization of classical
(maximum) Traveling Salesmen Problem.
The algorithm is a greedy heuristic that works with chains (ordered lists)
of basic blocks. Initially all chains are isolated basic blocks. On every
iteration, we pick a pair of chains whose merging yields the biggest increase
in the ExtTSP value, which models how i-cache "friendly" a specific chain is.
A pair of chains giving the maximum gain is merged into a new chain. The
procedure stops when there is only one chain left, or when merging does not
increase ExtTSP. In the latter case, the remaining chains are sorted by
density in decreasing order.
An important aspect is the way two chains are merged. Unlike earlier
algorithms (e.g., based on the approach of Pettis-Hansen), two
chains, X and Y, are first split into three, X1, X2, and Y. Then we
consider all possible ways of gluing the three chains (e.g., X1YX2, X1X2Y,
X2X1Y, X2YX1, YX1X2, YX2X1) and choose the one producing the largest score.
This improves the quality of the final result (the search space is larger)
while keeping the implementation sufficiently fast.
Differential Revision: https://reviews.llvm.org/D113424
The earlier usage of wouldInstructionBeTriviallyDead is based on the
assumption that the use_count of that instruction being checked will be
zero. This patch separates the API into two different ones:
1. The strictly conservative one where the instruction is trivially dead iff the uses are dead.
2. The slightly relaxed form, where an instruction is dead along paths where it is not used.
The second form can be used in identifying instructions that are valid
to sink down to uses (D109917).
Reviewed-By: reames
Differential Revision: https://reviews.llvm.org/D114647
This is a continuation of D109860 and D109903.
An important challenge for profile inference is caused by the fact that the
sample profile is collected on a fully optimized binary, while the block and
edge frequencies are consumed on an early stage of the compilation that operates
with a non-optimized IR. As a result, some of the basic blocks may not have
associated sample counts, and it is up to the algorithm to deduce missing
frequencies. The problem is illustrated in the figure where three basic
blocks are not present in the optimized binary and hence, receive no samples
during profiling.
We found that it is beneficial to treat all such blocks equally. Otherwise the
compiler may decide that some blocks are “cold” and apply undesirable
optimizations (e.g., hot-cold splitting) regressing the performance. Therefore,
we want to distribute the counts evenly along the blocks with missing samples.
This is achieved by a post-processing step that identifies "dangling" subgraphs
consisting of basic blocks with no sampled counts; once the subgraphs are
found, we rebalance the flow so as every branch probability is 50:50 within the
subgraphs.
Our experiments indicate up to 1% performance win using the optimization on
some binaries and a significant improvement in the quality of profile counts
(when compared to ground-truth instrumentation-based counts)
{F19093045}
Reviewed By: hoy
Differential Revision: https://reviews.llvm.org/D109980
This is a continuation of D109860.
Traditional flow-based algorithms cannot guarantee that the resulting edge
frequencies correspond to a *connected* flow in the control-flow graph. For
example, for an instance in the attached figure, a flow-based (or any other)
inference algorithm may produce an output in which the hot loop is disconnected
from the entry block (refer to the rightmost graph in the figure). Furthermore,
creating a connected minimum-cost maximum flow is a computationally NP-hard
problem. Hence, we apply a post-processing adjustments to the computed flow
by connecting all isolated flow components ("islands").
This feature helps to keep all blocks with sample counts connected and results
in significant performance wins for some binaries.
{F19077343}
Reviewed By: hoy
Differential Revision: https://reviews.llvm.org/D109903
When doing load/store promotion within LICM, if we
cannot prove that it is safe to sink the store we won't
hoist the load, even though we can prove the load could
be dereferenced and moved outside the loop. This patch
implements the load promotion by moving it in the loop
preheader by inserting proper PHI in the loop. The store
is kept as is in the loop. By doing this, we avoid doing
the load from a memory location in each iteration.
Please consider this small example:
loop {
var = *ptr;
if (var) break;
*ptr= var + 1;
}
After this patch, it will be:
var0 = *ptr;
loop {
var1 = phi (var0, var2);
if (var1) break;
var2 = var1 + 1;
*ptr = var2;
}
This addresses some problems from [0].
[0] https://bugs.llvm.org/show_bug.cgi?id=51193
Differential revision: https://reviews.llvm.org/D113289
Add support for memset_pattern{4,8} similar to the existing
memset_pattern16 handling.
Reviewed By: ab
Differential Revision: https://reviews.llvm.org/D114883
This fixes the assertion failure reported in https://reviews.llvm.org/D114889#3166417,
by making RecursivelyDeleteTriviallyDeadInstructionsPermissive()
more permissive. As the function accepts a WeakTrackingVH, even if
originally only Instructions were inserted, we may end up with
different Value types after a RAUW operation. As such, we should
not assume that the vector only contains instructions.
Notably this matches the behavior of the
RecursivelyDeleteTriviallyDeadInstructions() function variant which
accepts a single value rather than vector.
`memcpy_chk` can be treated like `memcpy`, with the exception that it
may not return (if it aborts the program).
See D114793 for a similar patch for `memset_chk`.
Reviewed By: xbolva00
Differential Revision: https://reviews.llvm.org/D114863
Previously we missed cloning metadata on function declarations because
we don't call CloneFunctionInto() on declarations in CloneModule().
Reviewed By: dexonsmith
Differential Revision: https://reviews.llvm.org/D113812
The benefits of sampling-based PGO crucially depends on the quality of profile
data. This diff implements a flow-based algorithm, called profi, that helps to
overcome the inaccuracies in a profile after it is collected.
Profi is an extended and significantly re-engineered classic MCMF (min-cost
max-flow) approach suggested by Levin, Newman, and Haber [2008, Complementing
missing and inaccurate profiling using a minimum cost circulation algorithm]. It
models profile inference as an optimization problem on a control-flow graph with
the objectives and constraints capturing the desired properties of profile data.
Three important challenges that are being solved by profi:
- "fixing" errors in profiles caused by sampling;
- converting basic block counts to edge frequencies (branch probabilities);
- dealing with "dangling" blocks having no samples in the profile.
The main implementation (and required docs) are in SampleProfileInference.cpp.
The worst-time complexity is quadratic in the number of blocks in a function,
O(|V|^2). However a careful engineering and extensive evaluation shows that
the running time is (slightly) super-linear. In particular, instances with
1000 blocks are solved within 0.1 second.
The algorithm has been extensively tested internally on prod workloads,
significantly improving the quality of generated profile data and providing
speedups in the range from 0% to 5%. For "smaller" benchmarks (SPEC06/17), it
generally improves the performance (with a few outliers) but extra work in
the compiler might be needed to re-tune existing optimization passes relying on
profile counts.
UPD Dec 1st 2021:
- synced the declaration and definition of the option `SampleProfileUseProfi ` to use type `cl::opt<bool`;
- added `inline` for `SampleProfileInference<BT>::findUnlikelyJumps` and `SampleProfileInference<BT>::isExit` to avoid linking problems on windows.
Reviewed By: wenlei, hoy
Differential Revision: https://reviews.llvm.org/D109860
