This should be unreachable, but bugs can make it reachable. This
adds a debug print so we can see the bad node in the output when
the llvm_unreachable triggers.
llvm-svn: 364091
This reverts r363985 (git commit d5f16d6cfc)
This test can't use printf on Windows because the path contains
backslashes which must not be interpreted as escapes by printf.
llvm-svn: 364089
With this we can now fully code generate jump tables, which is important for code size.
Differential Revision: https://reviews.llvm.org/D63223
llvm-svn: 364086
This change makes use of the newly refactored SwitchLoweringUtils code from
SelectionDAG to in order to generate jump tables and range checks where appropriate.
Much of this code is ported from SDAG with some modifications. We generate
G_JUMP_TABLE and G_BRJT instructions when JT opportunities are found. This means
that targets which previously relied on the naive one MBB per case stmt
translation will now start falling back until they add support for the new opcodes.
For range checks, we don't generate any previously unused operations. This
just recognizes contiguous ranges of case values and generates a single block per
range. Single case value blocks are just a special case of ranges so we get that
support almost for free.
There are still some optimizations missing that I haven't ported over, and
bit-tests are also unimplemented. This patch series is already complex enough.
Actual arm64 support for selection of jump tables is coming in a later patch.
Differential Revision: https://reviews.llvm.org/D63169
llvm-svn: 364085
This patch introduces a new heuristic for guiding operand reordering. The new "look-ahead" heuristic can look beyond the immediate predecessors. This helps break ties when the immediate predecessors have identical opcodes (see lit test for an example).
Committed on behalf of @vporpo (Vasileios Porpodas)
Differential Revision: https://reviews.llvm.org/D60897
llvm-svn: 364084
If the variably modified type is declared outside of the captured region
and then used in the cast expression along with array subscript
expression, the type is not captured and it leads to the compiler crash.
llvm-svn: 364080
We already use vmovq for v2i64/v2f64 vzmovl. But we were using a
blendpd+xorpd for v4i64/v4f64/v8i64/v8f64 under opt speed. Or
movsd+xorpd under optsize.
I think the blend with 0 or movss/d is only needed for
vXi32 where we don't have an instruction that can move 32
bits from one xmm to another while zeroing upper bits.
movq is no worse than blendpd on any known CPUs.
llvm-svn: 364079
These days, Python 3 installs itself into Program Files, so it often has
spaces. At first, I resisted this, and I reinstalled it globally into
C:/Python37, similar to the location used for Python 2.7. But then I
updated VS 2019, and it uninstalled my copy of Python and installed a
new one inside "C:/Program Files (x86)/Microsoft Visual Studio/". At
this point, I gave up and switched to using its built-in version of
Python. However, now these tests fail, and have to be made aware of the
possibility of spaces in paths. :(
llvm-svn: 364077
We sometimes get poor code size because constants of types < 32b are legalized
as 32 bit G_CONSTANTs with a truncate to fit. This works but means that the
localizer can no longer sink them (although it's possible to extend it to do so).
On AArch64 however s8 and s16 constants can be selected in the same way as s32
constants, with a mov pseudo into a W register. If we make s8 and s16 constants
legal then we can avoid unnecessary truncates, they can be CSE'd, and the
localizer can sink them as normal.
There is a caveat: if the user of a smaller constant has to widen the sources,
we end up with an anyext of the smaller typed G_CONSTANT. This can cause
regressions because of the additional extend and missed pattern matching. To
remedy this, there's a new artifact combiner to generate the wider G_CONSTANT
if it's legal for the target.
Differential Revision: https://reviews.llvm.org/D63587
llvm-svn: 364075
Add overloads with generic address space pointer to old atomics.
This is currently only added for C++ compilation mode.
Differential Revision: https://reviews.llvm.org/D62335
llvm-svn: 364071
Otherwise the tests hang on Windows attempting to report nested errors.
Reviewed By: vitalybuka
Differential Revision: https://reviews.llvm.org/D63627
llvm-svn: 364070
As per the discussion on D58375, we disable test that have optimizations under
the new PM. This patch adds -fno-experimental-new-pass-manager to RUNS that:
- Already run with optimizations (-O1 or higher) that were missed in D58375.
- Explicitly test new PM behavior along side some new PM RUNS, but are missing
this flag if new PM is enabled by default.
- Specify -O without the number. Based on getOptimizationLevel(), it seems the
default is 2, and the IR appears to be the same when changed to -O2, so
update the test to explicitly say -O2 and provide -fno-experimental-new-pass-manager`.
Differential Revision: https://reviews.llvm.org/D63156
llvm-svn: 364066
These functions are key to allowing the use of rvalues and variadics
in C++03 mode. Everything works the same as in C++11, except for one
tangentially related case:
struct T {
T(T &&) = default;
};
In C++11, T has a deleted copy constructor. But in C++03 Clang gives
it both a move and a copy constructor. This seems reasonable enough
given the extensions it's using.
The other changes in this patch were the minimal set required
to keep the tests passing after the move/forward change. Most notably
the removal of the `__rv<unique_ptr>` hack that was present
in an attempt to make unique_ptr move only without language support.
llvm-svn: 364063
This also details what filters, if any, were used to generate the test output. Updates all the current JSON testing files to include the automated note.
llvm-svn: 364055
We do not have to spread using the precompiled binaries in the tests,
when we can use YAML. This patch removes the dynrel.elf binary and adds
a few comments to the test cases.
Differential revision: https://reviews.llvm.org/D63641
llvm-svn: 364052
Summary:
The motivation for this was to propagate fast-math flags like nnan and
ninf on vector floating point operations to the corresponding scalar
operations to take advantage of follow-on optimizations. But I think
the same argument applies to all of our IR flags: if they apply to the
vector operation then they also apply to all the individual scalar
operations, and they might enable follow-on optimizations.
Subscribers: hiraditya, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D63593
llvm-svn: 364051
There are some test that are splitted into main part + input yaml for no visible reason.
This patch inines the yaml part for the 3 test cases I found.
Differential revision: https://reviews.llvm.org/D63644
llvm-svn: 364049
All the compilers we support provide these builtins. We don't
need to do a configuration dance anymore.
This patch also cleans up some dead or almost dead
C++11 feature detection macros.
llvm-svn: 364047
Summary:
LLVM Allows Targets to provide information that guides optimisations
made to LLVM IR. This is done with callbacks on a TargetTransformInfo object.
This patch adds a TargetTransformInfo class for RISC-V. This will allow us to
implement RISC-V specific callbacks as they become necessary.
This commit also adds the getIntImmCost callbacks, and tests them with a simple
constant hoisting test. Our immediate costs are on the conservative side, for
the moment, but we prevent hoisting in most circumstances anyway.
Previous review was on D63007
Reviewers: asb, luismarques
Reviewed By: asb
Subscribers: ributzka, MaskRay, llvm-commits, Jim, benna, psnobl, jocewei, PkmX, rkruppe, the_o, brucehoult, MartinMosbeck, rogfer01, edward-jones, zzheng, jrtc27, shiva0217, kito-cheng, niosHD, sabuasal, apazos, simoncook, johnrusso, rbar, hiraditya, mgorny
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D63433
llvm-svn: 364046
This patch teaches the bottleneck analysis how to identify and print the most
expensive sequence of instructions according to the simulation. Fixes PR37494.
The goal is to help users identify the sequence of instruction which is most
critical for performance.
A dependency graph is internally used by the bottleneck analysis to describe
data dependencies and processor resource interferences between instructions.
There is one node in the graph for every instruction in the input assembly
sequence. The number of nodes in the graph is independent from the number of
iterations simulated by the tool. It means that a single node of the graph
represents all the possible instances of a same instruction contributed by the
simulated iterations.
Edges are dynamically "discovered" by the bottleneck analysis by observing
instruction state transitions and "backend pressure increase" events generated
by the Execute stage. Information from the events is used to identify critical
dependencies, and materialize edges in the graph. A dependency edge is uniquely
identified by a pair of node identifiers plus an instance of struct
DependencyEdge::Dependency (which provides more details about the actual
dependency kind).
The bottleneck analysis internally ranks dependency edges based on their impact
on the runtime (see field DependencyEdge::Dependency::Cost). To this end, each
edge of the graph has an associated cost. By default, the cost of an edge is a
function of its latency (in cycles). In practice, the cost of an edge is also a
function of the number of cycles where the dependency has been seen as
'contributing to backend pressure increases'. The idea is that the higher the
cost of an edge, the higher is the impact of the dependency on performance. To
put it in another way, the cost of an edge is a measure of criticality for
performance.
Note how a same edge may be found in multiple iteration of the simulated loop.
The logic that adds new edges to the graph checks if an equivalent dependency
already exists (duplicate edges are not allowed). If an equivalent dependency
edge is found, field DependencyEdge::Frequency of that edge is incremented by
one, and the new cost is cumulatively added to the existing edge cost.
At the end of simulation, costs are propagated to nodes through the edges of the
graph. The goal is to identify a critical sequence from a node of the root-set
(composed by node of the graph with no predecessors) to a 'sink node' with no
successors. Note that the graph is intentionally kept acyclic to minimize the
complexity of the critical sequence computation algorithm (complexity is
currently linear in the number of nodes in the graph).
The critical path is finally computed as a sequence of dependency edges. For
edges describing processor resource interferences, the view also prints a
so-called "interference probability" value (by dividing field
DependencyEdge::Frequency by the total number of iterations).
Examples of critical sequence computations can be found in tests added/modified
by this patch.
On output streams that support colored output, instructions from the critical
sequence are rendered with a different color.
Strictly speaking the analysis conducted by the bottleneck analysis view is not
a critical path analysis. The cost of an edge doesn't only depend on the
dependency latency. More importantly, the cost of a same edge may be computed
differently by different iterations.
The number of dependencies is discovered dynamically based on the events
generated by the simulator. However, their number is not fixed. This is
especially true for edges that model processor resource interferences; an
interference may not occur in every iteration. For that reason, it makes sense
to also print out a "probability of interference".
By construction, the accuracy of this analysis (as always) is strongly dependent
on the simulation (and therefore the quality of the information available in the
scheduling model).
That being said, the critical sequence effectively identifies a performance
criticality. Instructions from that sequence are expected to have a very big
impact on performance. So, users can take advantage of this information to focus
their attention on specific interactions between instructions.
In my experience, it works quite well in practice, and produces useful
output (in a reasonable amount time).
Differential Revision: https://reviews.llvm.org/D63543
llvm-svn: 364045
Summary:
This resolves the issue of introducing c++-style includes for C files.
- refactor the gen_std.py, make it reusable for parsing C symbols.
- add a language mode to the mapping method to use different mapping for
C and C++ files.
Reviewers: kadircet
Subscribers: ilya-biryukov, MaskRay, jkorous, arphaman, jfb, cfe-commits
Tags: #clang
Differential Revision: https://reviews.llvm.org/D63270
llvm-svn: 364044
Craig Topper