Changing ARMBaseTargetMachine to return ARMTargetLowering intead of
the generic one (similar to x86 code).
Tests showing which instructions were added to cast when necessary
or cost zero when not. Downcast to 16 bits are not lowered in NEON,
so costs are not there yet.
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We ignore the cpu frontend and focus on pipeline utilization. We do this because we
don't have a good way to estimate the loop body size at the IR level.
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This separates the check for "too few elements to run the vector loop" from the
"memory overlap" check, giving a lot nicer code and allowing to skip the memory
checks when we're not going to execute the vector code anyways. We still leave
the decision of whether to emit the memory checks as branches or setccs, but it
seems to be doing a good job. If ugly code pops up we may want to emit them as
separate blocks too. Small speedup on MultiSource/Benchmarks/MallocBench/espresso.
Most of this is legwork to allow multiple bypass blocks while updating PHIs,
dominators and loop info.
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the target if it supports the different CAST types. We didn't do this
on X86 because of the different register sizes and types, but on ARM
this makes sense.
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We don't have a detailed analysis on which values are vectorized and which stay scalars in the vectorized loop so we use
another method. We look at reduction variables, loads and stores, which are the only ways to get information in and out
of loop iterations. If the data types are extended and truncated then the cost model will catch the cost of the vector
zext/sext/trunc operations.
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small loops. On small loops post-loop that handles scalars (and runs slower) can take more time to execute than the
rest of the loop. This patch disables widening of loops with a small static trip count.
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Since subtraction does not commute the loop vectorizer incorrectly vectorizes
reductions such as x = A[i] - x.
Disabling for now.
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1. Add code to estimate register pressure.
2. Add code to select the unroll factor based on register pressure.
3. Add bits to TargetTransformInfo to provide the number of registers.
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LCSSA PHIs may have undef values. The vectorizer updates values that are used by outside users such as PHIs.
The bug happened because undefs are not loop values. This patch handles these PHIs.
PR14725
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the cost of arithmetic functions. We now assume that the cost of arithmetic
operations that are marked as Legal or Promote is low, but ops that are
marked as custom are higher.
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memory bound checks. Before the fix we were able to vectorize this loop from
the Livermore Loops benchmark:
for ( k=1 ; k<n ; k++ )
x[k] = x[k-1] + y[k];
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Before if-conversion we could check if a value is loop invariant
if it was declared inside the basic block. Now that loops have
multiple blocks this check is incorrect.
This fixes External/SPEC/CINT95/099_go/099_go
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- An MVT can become an EVT when being split (e.g. v2i8 -> v1i8, the latter doesn't exist)
- Return the scalar value when an MVT is scalarized (v1i64 -> i64)
Fixes PR14639ff.
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- added function to VectorTargetTransformInfo to query cost of intrinsics
- vectorize trivially vectorizable intrinsic calls such as sin, cos, log, etc.
Reviewed by: Nadav
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reduction variable is not used outside the loop then we ran into an
endless loop. This change checks if we found the original PHI.
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Added the code that actually performs the if-conversion during vectorization.
We can now vectorize this code:
for (int i=0; i<n; ++i) {
unsigned k = 0;
if (a[i] > b[i]) <------ IF inside the loop.
k = k * 5 + 3;
a[i] = k; <---- K is a phi node that becomes vector-select.
}
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Previously in a vector of pointers, the pointer couldn't be any pointer type,
it had to be a pointer to an integer or floating point type. This is a hassle
for dragonegg because the GCC vectorizer happily produces vectors of pointers
where the pointer is a pointer to a struct or whatever. Vector getelementptr
was restricted to just one index, but now that vectors of pointers can have
any pointer type it is more natural to allow arbitrary vector getelementptrs.
There is however the issue of struct GEPs, where if each lane chose different
struct fields then from that point on each lane will be working down into
unrelated types. This seems like too much pain for too little gain, so when
you have a vector struct index all the elements are required to be the same.
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If the arrays are found to be disjoint then we run the vectorized version of
the loop. If they are not, we run the scalar code.
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This is important for loops in the LAPACK test-suite.
These loops start at 1 because they are auto-converted from fortran.
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Add getCostXXX calls for different families of opcodes, such as casts, arithmetic, cmp, etc.
Port the LoopVectorizer to the new API.
The LoopVectorizer now finds instructions which will remain uniform after vectorization. It uses this information when calculating the cost of these instructions.
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We used a SCEV to detect that A[X] is consecutive. We assumed that X was
the induction variable. But X can be any expression that uses the induction
for example: X = i + 2;
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This is important for nested-loop reductions such as :
In the innermost loop, the induction variable does not start with zero:
for (i = 0 .. n)
for (j = 0 .. m)
sum += ...
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If the pointer is consecutive then it is safe to read and write. If the pointer is non-loop-consecutive then
it is unsafe to vectorize it because we may hit an ordering issue.
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