Reimplements MisExpect diagnostics from D66324 to reconstruct its
original checking methodology only using MD_prof branch_weights
metadata.
New checks rely on 2 invariants:
1) For frontend instrumentation, MD_prof branch_weights will always be
populated before llvm.expect intrinsics are lowered.
2) for IR and sample profiling, llvm.expect intrinsics will always be
lowered before branch_weights are populated from the IR profiles.
These invariants allow the checking to assume how the existing branch
weights are populated depending on the profiling method used, and emit
the correct diagnostics. If these invariants are ever invalidated, the
MisExpect related checks would need to be updated, potentially by
re-introducing MD_misexpect metadata, and ensuring it always will be
transformed the same way as branch_weights in other optimization passes.
Frontend based profiling is now enabled without using LLVM Args, by
introducing a new CodeGen option, and checking if the -Wmisexpect flag
has been passed on the command line.
Reviewed By: tejohnson
Differential Revision: https://reviews.llvm.org/D115907
Reimplements MisExpect diagnostics from D66324 to reconstruct its
original checking methodology only using MD_prof branch_weights
metadata.
New checks rely on 2 invariants:
1) For frontend instrumentation, MD_prof branch_weights will always be
populated before llvm.expect intrinsics are lowered.
2) for IR and sample profiling, llvm.expect intrinsics will always be
lowered before branch_weights are populated from the IR profiles.
These invariants allow the checking to assume how the existing branch
weights are populated depending on the profiling method used, and emit
the correct diagnostics. If these invariants are ever invalidated, the
MisExpect related checks would need to be updated, potentially by
re-introducing MD_misexpect metadata, and ensuring it always will be
transformed the same way as branch_weights in other optimization passes.
Frontend based profiling is now enabled without using LLVM Args, by
introducing a new CodeGen option, and checking if the -Wmisexpect flag
has been passed on the command line.
Reviewed By: tejohnson
Differential Revision: https://reviews.llvm.org/D115907
Reimplements MisExpect diagnostics from D66324 to reconstruct its
original checking methodology only using MD_prof branch_weights
metadata.
New checks rely on 2 invariants:
1) For frontend instrumentation, MD_prof branch_weights will always be
populated before llvm.expect intrinsics are lowered.
2) for IR and sample profiling, llvm.expect intrinsics will always be
lowered before branch_weights are populated from the IR profiles.
These invariants allow the checking to assume how the existing branch
weights are populated depending on the profiling method used, and emit
the correct diagnostics. If these invariants are ever invalidated, the
MisExpect related checks would need to be updated, potentially by
re-introducing MD_misexpect metadata, and ensuring it always will be
transformed the same way as branch_weights in other optimization passes.
Frontend based profiling is now enabled without using LLVM Args, by
introducing a new CodeGen option, and checking if the -Wmisexpect flag
has been passed on the command line.
Reviewed By: tejohnson
Differential Revision: https://reviews.llvm.org/D115907
For MachO, lower `@llvm.global_dtors` into `@llvm_global_ctors` with
`__cxa_atexit` calls to avoid emitting the deprecated `__mod_term_func`.
Reuse the existing `WebAssemblyLowerGlobalDtors.cpp` to accomplish this.
Enable fallback to the old behavior via Clang driver flag
(`-fregister-global-dtors-with-atexit`) or llc / code generation flag
(`-lower-global-dtors-via-cxa-atexit`). This escape hatch will be
removed in the future.
Differential Revision: https://reviews.llvm.org/D121736
Reimplements MisExpect diagnostics from D66324 to reconstruct its
original checking methodology only using MD_prof branch_weights
metadata.
New checks rely on 2 invariants:
1) For frontend instrumentation, MD_prof branch_weights will always be
populated before llvm.expect intrinsics are lowered.
2) for IR and sample profiling, llvm.expect intrinsics will always be
lowered before branch_weights are populated from the IR profiles.
These invariants allow the checking to assume how the existing branch
weights are populated depending on the profiling method used, and emit
the correct diagnostics. If these invariants are ever invalidated, the
MisExpect related checks would need to be updated, potentially by
re-introducing MD_misexpect metadata, and ensuring it always will be
transformed the same way as branch_weights in other optimization passes.
Frontend based profiling is now enabled without using LLVM Args, by
introducing a new CodeGen option, and checking if the -Wmisexpect flag
has been passed on the command line.
Differential Revision: https://reviews.llvm.org/D115907
For MachO, lower `@llvm.global_dtors` into `@llvm_global_ctors` with
`__cxa_atexit` calls to avoid emitting the deprecated `__mod_term_func`.
Reuse the existing `WebAssemblyLowerGlobalDtors.cpp` to accomplish this.
Enable fallback to the old behavior via Clang driver flag
(`-fregister-global-dtors-with-atexit`) or llc / code generation flag
(`-lower-global-dtors-via-cxa-atexit`). This escape hatch will be
removed in the future.
Differential Revision: https://reviews.llvm.org/D121736
For MachO, lower `@llvm.global_dtors` into `@llvm_global_ctors` with
`__cxa_atexit` calls to avoid emitting the deprecated `__mod_term_func`.
Reuse the existing `WebAssemblyLowerGlobalDtors.cpp` to accomplish this.
Enable fallback to the old behavior via Clang driver flag
(`-fregister-global-dtors-with-atexit`) or llc / code generation flag
(`-lower-global-dtors-via-cxa-atexit`). This escape hatch will be
removed in the future.
Differential Revision: https://reviews.llvm.org/D121327
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 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
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.
Reviewed By: wenlei, hoy
Differential Revision: https://reviews.llvm.org/D109860
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.
Reviewed By: wenlei, hoy
Differential Revision: https://reviews.llvm.org/D109860
This refactors SCCP and creates a SCCPSolver interface and class so that it can
be used by other passes and transformations. We will use this in D93838, which
adds a function specialisation pass.
This is based on an early version by Vinay Madhusudan.
Differential Revision: https://reviews.llvm.org/D93762
Lookup tables generate non PIC-friendly code, which requires dynamic relocation as described in:
https://bugs.llvm.org/show_bug.cgi?id=45244
This patch adds a new pass that converts lookup tables to relative lookup tables to make them PIC-friendly.
Differential Revision: https://reviews.llvm.org/D94355
Lookup tables generate non PIC-friendly code, which requires dynamic relocation as described in:
https://bugs.llvm.org/show_bug.cgi?id=45244
This patch adds a new pass that converts lookup tables to relative lookup tables to make them PIC-friendly.
Differential Revision: https://reviews.llvm.org/D94355
Lookup tables generate non PIC-friendly code, which requires dynamic relocation as described in:
https://bugs.llvm.org/show_bug.cgi?id=45244
This patch adds a new pass that converts lookup tables to relative lookup tables to make them PIC-friendly.
Differential Revision: https://reviews.llvm.org/D94355
Lookup tables generate non PIC-friendly code, which requires dynamic relocation as described in:
https://bugs.llvm.org/show_bug.cgi?id=45244
This patch adds a new pass that converts lookup tables to relative lookup tables to make them PIC-friendly.
Differential Revision: https://reviews.llvm.org/D94355
D96109 was recently submitted which contains the refactored implementation of
-funique-internal-linakge-names by adding the unique suffixes in clang rather
than as an LLVM pass. Deleting the former implementation in this change.
Differential Revision: https://reviews.llvm.org/D98234
This adds support for analyzing the instruction with the !annotation
"auto-init" in order to generate a more user-friendly remark.
For now, support the store size, and whether it's atomic/volatile.
Example:
```
auto-init.c:4:7: remark: Store inserted by -ftrivial-auto-var-init.Store size: 4 bytes. [-Rpass-missed=annotation-remarks]
int var;
^
```
Differential Revision: https://reviews.llvm.org/D97412
Apply the patch for the third time after fixing buildbot failures.
Refactor SampleProfile.cpp to use the core code in CodeGen.
The main changes are:
(1) Move SampleProfileLoaderBaseImpl class to a header file.
(2) Split SampleCoverageTracker to a head file and a cpp file.
(3) Move the common codes (common options and callsiteIsHot())
to the common cpp file.
(4) Add inline keyword to avoid duplicated symbols -- they will
be removed later when the class is changed to a template.
Differential Revision: https://reviews.llvm.org/D96455
Revert "[SampleFDO] Add missing #includes to unbreak modules build after D96455"
This reverts commit c73cbf218a.
Revert "[SampleFDO] Fix MSVC "namespace uses itself" warning (NFC)"
This reverts commit a23e6b321c.
Revert "[SampleFDO] Reapply: Refactor SampleProfile.cpp"
This reverts commit 6fd5ccff72.
Still seeing link failures when building llc (or other tools), due to
the new SampleProfileLoaderBaseImpl.h containing definitions that get
duplicated across multiple TU's.
```
duplicate symbol 'llvm::SampleProfileLoaderBaseImpl::findEquivalenceClasses(llvm::Function&)' in:
tools/llc/CMakeFiles/llc.dir/llc.cpp.o
lib/libLLVMInstCombine.a(InstCombineVectorOps.cpp.o)
duplicate symbol 'llvm::SampleProfileLoaderBaseImpl::buildEdges(llvm::Function&)' in:
tools/llc/CMakeFiles/llc.dir/llc.cpp.o
lib/libLLVMInstCombine.a(InstCombineVectorOps.cpp.o)
duplicate symbol 'llvm::SampleProfileLoaderBaseImpl::computeDominanceAndLoopInfo(llvm::Function&)' in:
tools/llc/CMakeFiles/llc.dir/llc.cpp.o
lib/libLLVMInstCombine.a(InstCombineVectorOps.cpp.o)
duplicate symbol 'llvm::SampleProfileLoaderBaseImpl::getFunctionLoc(llvm::Function&)' in:
tools/llc/CMakeFiles/llc.dir/llc.cpp.o
lib/libLLVMInstCombine.a(InstCombineVectorOps.cpp.o)
duplicate symbol 'llvm::SampleProfileLoaderBaseImpl::getBlockWeight(llvm::BasicBlock const*)' in:
tools/llc/CMakeFiles/llc.dir/llc.cpp.o
lib/libLLVMInstCombine.a(InstCombineVectorOps.cpp.o)
duplicate symbol 'llvm::SampleProfileLoaderBaseImpl::printBlockWeight(llvm::raw_ostream&, llvm::BasicBlock const*) const' in:
tools/llc/CMakeFiles/llc.dir/llc.cpp.o
lib/libLLVMInstCombine.a(InstCombineVectorOps.cpp.o)
duplicate symbol 'llvm::SampleProfileLoaderBaseImpl::printBlockEquivalence(llvm::raw_ostream&, llvm::BasicBlock const*)' in:
tools/llc/CMakeFiles/llc.dir/llc.cpp.o
lib/libLLVMInstCombine.a(InstCombineVectorOps.cpp.o)
duplicate symbol 'llvm::SampleProfileLoaderBaseImpl::printEdgeWeight(llvm::raw_ostream&, std::__1::pair<llvm::BasicBlock const*, llvm::BasicBlock const*>)' in:
tools/llc/CMakeFiles/llc.dir/llc.cpp.o
lib/libLLVMInstCombine.a(InstCombineVectorOps.cpp.o)
```
Reapply patch after fixing buildbot failure.
Refactor SampleProfile.cpp to use the core code in CodeGen.
The main changes are:
(1) Move SampleProfileLoaderBaseImpl class to a header file.
(2) Split SampleCoverageTracker to a head file and a cpp file.
(3) Move the common codes (common options and callsiteIsHot())
to the common cpp file.
Differential Revision: https://reviews.llvm.org/D96455
This is related to D94982. We want to call these APIs from the Analysis
component, so we can't leave them under Transforms.
Differential Revision: https://reviews.llvm.org/D95079
See discussion in https://bugs.llvm.org/show_bug.cgi?id=45073 / https://reviews.llvm.org/D66324#2334485
the implementation is known-broken for certain inputs,
the bugreport was up for a significant amount of timer,
and there has been no activity to address it.
Therefore, just completely rip out all of misexpect handling.
I suspect, fixing it requires redesigning the internals of MD_misexpect.
Should anyone commit to fixing the implementation problem,
starting from clean slate may be better anyways.
This reverts commit 7bdad08429,
and some of it's follow-ups, that don't stand on their own.
No longer rely on an external tool to build the llvm component layout.
Instead, leverage the existing `add_llvm_componentlibrary` cmake function and
introduce `add_llvm_component_group` to accurately describe component behavior.
These function store extra properties in the created targets. These properties
are processed once all components are defined to resolve library dependencies
and produce the header expected by llvm-config.
Differential Revision: https://reviews.llvm.org/D90848
Summary: This patch separates the Loop Peeling Utilities from Loop Unrolling.
The reason for this change is that Loop Peeling is no longer only being used by
loop unrolling; Patch D82927 introduces loop peeling with fusion, such that
loops can be modified to have to same trip count, making them legal to be
peeled.
Reviewed By: Meinersbur
Differential Revision: https://reviews.llvm.org/D83056
This patch adds a TileInfo abstraction and utilities to
create a 3-level loop nest for tiling.
Reviewers: anemet
Reviewed By: anemet
Differential Revision: https://reviews.llvm.org/D77550
Summary:
Added a new IRCanonicalizer pass which aims to transform LLVM modules into
a canonical form by reordering and renaming instructions while preserving the
same semantics. The canonicalizer makes it easier to spot semantic differences
when diffing two modules which have undergone different passes.
Presentation: https://www.youtube.com/watch?v=c9WMijSOEUg
Reviewed by: plotfi
Differential Revision: https://reviews.llvm.org/D66029
Summary:
If an induction variable is frozen and used, SCEV yields imprecise result
because it doesn't say anything about frozen variables.
Due to this reason, performance degradation happened after
https://reviews.llvm.org/D76483 is merged, causing
SCEV yield imprecise result and preventing LSR to optimize a loop.
The suggested solution here is to add a pass which canonicalizes frozen variables
inside a loop. To be specific, it pushes freezes out of the loop by freezing
the initial value and step values instead & dropping nsw/nuw flags from instructions used by freeze.
This solution was also mentioned at https://reviews.llvm.org/D70623 .
Reviewers: spatel, efriedma, lebedev.ri, fhahn, jdoerfert
Reviewed By: fhahn
Subscribers: nikic, mgorny, hiraditya, javed.absar, llvm-commits, sanwou01, nlopes
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D77523
SCEVExpander modifies the underlying function so it is more suitable in
Transforms/Utils, rather than Analysis. This allows using other
transform utils in SCEVExpander.
This patch was originally committed as b8a3c34eee, but broke the
modules build, as LoopAccessAnalysis was using the Expander.
The code-gen part of LAA was moved to lib/Transforms recently, so this
patch can be landed again.
Reviewers: sanjoy.google, efriedma, reames
Reviewed By: sanjoy.google
Differential Revision: https://reviews.llvm.org/D71537
Paul Kirth