vectorizer, placing it behind an off-by-default flag.
It turns out that block frequency isn't what we want at all, here or
elsewhere. This has been I think a nagging feeling for several of us
working with it, but Arnold has given some really nice simple examples
where the results are so comprehensively wrong that they aren't useful.
I'm planning to email the dev list with a summary of why its not really
useful and a couple of ideas about how to better structure these types
of heuristics.
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The vectorizer takes a loop like this and widens all instructions except for the
store. The stores are scalarized/unrolled and hidden behind an "if" block.
for (i = 0; i < 128; ++i) {
if (a[i] < 10)
a[i] += val;
}
for (i = 0; i < 128; i+=2) {
v = a[i:i+1];
v0 = (extract v, 0) + 10;
v1 = (extract v, 1) + 10;
if (v0 < 10)
a[i] = v0;
if (v1 < 10)
a[i] = v1;
}
The vectorizer relies on subsequent optimizations to sink instructions into the
conditional block where they are anticipated.
The flag "vectorize-num-stores-pred" controls whether and how many stores to
handle this way. Vectorization of conditional stores is disabled per default for
now.
This patch also adds a change to the heuristic when the flag
"enable-loadstore-runtime-unroll" is enabled (off by default). It unrolls small
loops until load/store ports are saturated. This heuristic uses TTI's
getMaxUnrollFactor as a measure for load/store ports.
I also added a second flag -enable-cond-stores-vec. It will enable vectorization
of conditional stores. But there is no cost model for vectorization of
conditional stores in place yet so this will not do good at the moment.
rdar://15892953
Results for x86-64 -O3 -mavx +/- -mllvm -enable-loadstore-runtime-unroll
-vectorize-num-stores-pred=1 (before the BFI change):
Performance Regressions:
Benchmarks/Ptrdist/yacr2/yacr2 7.35% (maze3() is identical but 10% slower)
Applications/siod/siod 2.18%
Performance improvements:
mesa -4.42%
libquantum -4.15%
With a patch that slightly changes the register heuristics (by subtracting the
induction variable on both sides of the register pressure equation, as the
induction variable is probably not really unrolled):
Performance Regressions:
Benchmarks/Ptrdist/yacr2/yacr2 7.73%
Applications/siod/siod 1.97%
Performance Improvements:
libquantum -13.05% (we now also unroll quantum_toffoli)
mesa -4.27%
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cold loops as-if they were being optimized for size.
Nothing fancy here. Simply test case included. The nice thing is that we
can now incrementally build on top of this to drive other heuristics.
All of the infrastructure work is done to get the profile information
into this layer.
The remaining work necessary to make this a fully general purpose loop
unroller for very hot loops is to make it a fully general purpose loop
unroller. Things I know of but am not going to have time to benchmark
and fix in the immediate future:
1) Don't disable the entire pass when the target is lacking vector
registers. This really doesn't make any sense any more.
2) Teach the unroller at least and the vectorizer potentially to handle
non-if-converted loops. This is trivial for the unroller but hard for
the vectorizer.
3) Compute the relative hotness of the loop and thread that down to the
various places that make cost tradeoffs (very likely only the
unroller makes sense here, and then only when dealing with loops that
are small enough for unrolling to not completely blow out the LSD).
I'm still dubious how useful hotness information will be. So far, my
experiments show that if we can get the correct logic for determining
when unrolling actually helps performance, the code size impact is
completely unimportant and we can unroll in all cases. But at least
we'll no longer burn code size on cold code.
One somewhat unrelated idea that I've had forever but not had time to
implement: mark all functions which are only reachable via the global
constructors rigging in the module as optsize. This would also decrease
the impact of any more aggressive heuristics here on code size.
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to stabilize a test that really is trying to test generic behavior and
not a specific target's behavior.
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powers of two. This is essentially always the correct thing given the
impact on alignment, scaling factors that can be used in addressing
modes, etc. Also, fix the management of the unroll vs. small loop cost
to more accurately model things with this world.
Enhance a test case to actually exercise more of the unroll machinery if
using synthetic constants rather than a specific target model. Before
this change, with the added flags this test will unroll 3 times instead
of either 2 or 4 (the two sensible answers).
While I don't expect this to make a huge difference, if there are lots
of loops sitting right on the edge of hitting the 'small unroll' factor,
they might change behavior. However, I've benchmarked moving the small
loop cost up and down in many various ways and by a huge factor (2x)
without seeing more than 0.2% code size growth. Small adjustments such
as the series that led up here have led to about 1% improvement on some
benchmarks, but it is very close to the noise floor so I mostly checked
that nothing regressed. Let me know if you see bad behavior on other
targets but I don't expect this to be a sufficiently dramatic change to
trigger anything.
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Sweep the codebase for common typos. Includes some changes to visible function
names that were misspelt.
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a reduction.
Really. Under certain circumstances (the use list of an instruction has to be
set up right - hence the extra pass in the test case) we would not recognize
when a value in a potential reduction cycle was used multiple times by the
reduction cycle.
Fixes PR18526.
radar://15851149
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for (i = 0; i < N; ++i)
A[i * Stride1] += B[i * Stride2];
We take loops like this and check that the symbolic strides 'Strided1/2' are one
and drop to the scalar loop if they are not.
This is currently disabled by default and hidden behind the flag
'enable-mem-access-versioning'.
radar://13075509
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A phi node operand or an instruction operand could be a constant expression that
can trap (division). Check that we don't vectorize such cases.
PR16729
radar://15653590
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Test is platform independent, but I don't want to force vector-width, or
that could spoil the pragma test.
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The intended behaviour is to force vectorization on the presence
of the flag (either turn on or off), and to continue the behaviour
as expected in its absence. Tests were added to make sure the all
cases are covered in opt. No tests were added in other tools with
the assumption that they should use the PassManagerBuilder in the
same way.
This patch also removes the outdated -late-vectorize flag, which was
on by default and not helping much.
The pragma metadata is being attached to the same place as other loop
metadata, but nothing forbids one from attaching it to a function
(to enable #pragma optimize) or basic blocks (to hint the basic-block
vectorizers), etc. The logic should be the same all around.
Patches to Clang to produce the metadata will be produced after the
initial implementation is agreed upon and committed. Patches to other
vectorizers (such as SLP and BB) will be added once we're happy with
the pass manager changes.
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This patch tries to avoid unrelated changes other than fixing a few
hyphen-related ambiguities and contractions in nearby lines.
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clang enables vectorization at optimization levels > 1 and size level < 2. opt
should behave similarily.
Loop vectorization and SLP vectorization can be disabled with the flags
-disable-(loop/slp)-vectorization.
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In signed arithmetic we could end up with an i64 trip count for an i32 phi.
Because it is signed arithmetic we know that this is only defined if the i32
does not wrap. It is therefore safe to truncate the i64 trip count to a i32
value.
Fixes PR18049.
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We are going to drop debug info without a version number or with a different
version number, to make sure we don't crash when we see bitcode files with
different debug info metadata format.
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We are slicing an array of Value pointers and process those slices in a loop.
The problem is that we might invalidate a later slice by vectorizing a former
slice.
Use a WeakVH to track the pointer. If the pointer is deleted or RAUW'ed we can
tell.
The test case will only fail when running with libgmalloc.
radar://15498655
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In some case the loop exit count computation can overflow. Extend the type to
prevent most of those cases.
The problem is loops like:
int main ()
{
int a = 1;
char b = 0;
lbl:
a &= 4;
b--;
if (b) goto lbl;
return a;
}
The backedge count is 255. The induction variable type is i8. If we add one to
255 to get the exit count we overflow to zero.
To work around this issue we extend the type of the induction variable to i32 in
the case of i8 and i16.
PR17532
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When we vectorize a scalar access with no alignment specified, we have to set
the target's abi alignment of the scalar access on the vectorized access.
Using the same alignment of zero would be wrong because most targets will have a
bigger abi alignment for vector types.
This probably fixes PR17878.
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When the elements are extracted from a select on vectors
or a vector select, do the select on the extracted scalars
from the input if there is only one use.
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When the loop vectorizer was part of the SCC inliner pass manager gvn would
run after the loop vectorizer followed by instcombine. This way redundancy
(multiple uses) were removed and instcombine could perform scalarization on the
induction variables. Having moved the loop vectorizer to later we no longer run
any form of redundancy elimination before we perform instcombine. This caused
vectorized induction variables to survive that did not before.
On a recent iMac this helps linpack back from 6000Mflops to 7000Mflops.
This should also help lpbench and paq8p.
I ran a Release (without Asserts) build over the test-suite and did not see any
negative impact on compile time.
radar://15339680
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If we have a pointer to a single-element struct we can still build wide loads
and stores to it (if there is no padding).
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When a dependence check fails we can still try to vectorize loops with runtime
array bounds checks.
This helps linpack to vectorize a loop in dgefa. And we are back to 2x of the
scalar performance on a corei7-avx.
radar://15339680
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Updated a test case that assumed that <2 x double> would vectorize to use
<4 x float>.
radar://15338229
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The loop vectorizer does not currently understand how to vectorize
extractelement instructions. The existing check, which excluded all
vector-valued instructions, did not catch extractelement instructions because
it checked only the return value. As a result, vectorization would proceed,
producing illegal instructions like this:
%58 = extractelement <2 x i32> %15, i32 0
%59 = extractelement i32 %58, i32 0
where the second extractelement is illegal because its first operand is not a vector.
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Make sure we mark all loops (scalar and vector) when vectorizing,
so that we don't try to vectorize them anymore. Also, set unroll
to 1, since this is what we check for on early exit.
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Otherwise, we don't perform operations that would have been performed on
the scalar version.
Fixes PR17498.
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Don't vectorize with a runtime check if it requires a
comparison between pointers with different address spaces.
The values can't be assumed to be directly comparable.
Previously it would create an illegal bitcast.
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Revert 191122 - with extra checks we are allowed to vectorize math library
function calls.
Standard library indentifiers are reserved names so functions with external
linkage must not overrided them. However, functions with internal linkage can.
Therefore, we can vectorize calls to math library functions with a check for
external linkage and matching signature. This matches what we do during
SelectionDAG building.
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XCore target: Add XCoreTargetTransformInfo
This is where getNumberOfRegisters() resides, which in turn returns the
number of vector registers (=0).
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We would have to compute the pre increment value, either by computing it on
every loop iteration or by splitting the edge out of the loop and inserting a
computation for it there.
For now, just give up vectorizing such loops.
Fixes PR17179.
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Field 2 of DIType (Context), field 9 of DIDerivedType (TypeDerivedFrom),
field 12 of DICompositeType (ContainingType), fields 2, 7, 12 of DISubprogram
(Context, Type, ContainingType).
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When unrolling is disabled in the pass manager, the loop vectorizer should also
not unroll loops. This will allow the -fno-unroll-loops option in Clang to
behave as expected (even for vectorizable loops). The loop vectorizer's
-force-vector-unroll option will (continue to) override the pass-manager
setting (including -force-vector-unroll=0 to force use of the internal
auto-selection logic).
In order to test this, I added a flag to opt (-disable-loop-unrolling) to force
disable unrolling through opt (the analog of -fno-unroll-loops in Clang). Also,
this fixes a small bug in opt where the loop vectorizer was enabled only after
the pass manager populated the queue of passes (the global_alias.ll test needed
a slight update to the RUN line as a result of this fix).
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DICompositeType will have an identifier field at position 14. For now, the
field is set to null in DIBuilder.
For DICompositeTypes where the template argument field (the 13th field)
was optional, modify DIBuilder to make sure the template argument field is set.
Now DICompositeType has 15 fields.
Update DIBuilder to use NULL instead of "i32 0" for null value of a MDNode.
Update verifier to check that DICompositeType has 15 fields and the last
field is null or a MDString.
Update testing cases to include an extra field for DICompositeType.
The identifier field will be used by type uniquing so a front end can
genearte a DICompositeType with a unique identifer.
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This patch enables unrolling of loops when vectorization is legal but not profitable.
We add a new class InnerLoopUnroller, that extends InnerLoopVectorizer and replaces some of the vector-specific logic with scalars.
This patch does not introduce any runtime regressions and improves the following workloads:
SingleSource/Benchmarks/Shootout/matrix -22.64%
SingleSource/Benchmarks/Shootout-C++/matrix -13.06%
External/SPEC/CINT2006/464_h264ref/464_h264ref -3.99%
SingleSource/Benchmarks/Adobe-C++/simple_types_constant_folding -1.95%
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A single metadata will not span multiple lines. This also helps me with
my script to automatic update the testing cases.
A debug info testing case should have a llvm.dbg.cu.
Do not use hard-coded id for debug nodes.
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