>>> I've attached the latest version of my autovectorization patch.
>>>
>>> Working through the test suite has proved to be a productive
>>> experience 
-- And almost all of the bugs that it revealed have now
>>> been fixed. There are still two programs that don't compile with
>>> vectorization turned on, and I'm working on those now, but in case
>>> anyone feels like playing with vectorization, this patch will probably
>>> work for you.
>>
>> Hey Hal,
>>
>> those are great news. Especially as the numbers seem to show that
>> vectorization has a significant performance impact. What did you compare
>> exactly. 'clang -O3' against 'clang -O3 -mllvm -vectorize'?
>
> Yes. [I've tested the current patch directly using opt -vectorize
> -unroll-allow-partial; for running the test suite I recompiled
> llvm/clang to hardcode the options as I wanted them].
You should not need to hack clang. As shown above, you should be able to
pass '-vectorize' to the optimizer by using '-mllvm -vectorize' in your
CFLAGS. Did you run the test suite at -O3?
That's good to know. Yes, it was run at -O3.
Also I think it would be interesting to compare for the test-suite the
performance of
'clang -O3 -mllvm -unroll-allow-partial' with
'clang -O3 -mllvm -unroll-allow-partial -mllvm -vectorize'. It will show
how much of the runtime overhead is due to the unrolling (produces more
code that needs to be optimized) and which part is due to vectorization.
The same counts for the speedup. How much is caused by
unrolling and how much is actually caused by your pass.
Just turning on partial unrolling yields, on average, a 4.7% slowdown
(0% median), and a 28.6% increase in compile time (21.8% median).
>>> The largest three performance speedups are:
>>> SingleSource/Benchmarks/BenchmarkGame/puzzle - 59.2% speedup
With just partial unrolling: 64.8% speedup
>>> SingleSource/UnitTests/Vector/multiplies - 57.7% speedup
With just partial unrolling: 58.2% speedup
>>> SingleSource/Benchmarks/Misc/flops-7 - 50.75% speedup
With just partial unrolling: 11.8% speedup
>>>
>>> The largest three performance slowdowns are:
>>> MultiSource/Benchmarks/MiBench/security-rijndael/security-rijndael -
>>> 114% slowdown
With just partial unrolling: 0% slowdown (47.8% slowdown in compile
time)
>>> MultiSource/Benchmarks/MiBench/network-patricia/network-patricia - 66.6%
>>> slowdown
With just partial unrolling: 8.3% speedup
>>> SingleSource/Benchmarks/Misc/flops-8 - 64.2% slowdown
With just partial unrolling: 8.3% speedup
>>>
Here are the top-3 lists comparing vectorization with partial unrolling
to just with partial unrolling:
Top speedups:
SingleSource/Benchmarks/Misc/flops-7 - 44.1% speedup
SingleSource/Benchmarks/Misc/flops-1 - 42.3% speedup
MultiSource/Applications/sqlite3/sqlite3 - 42.3% speedup
Top slowdowns:
MultiSource/Benchmarks/MiBench/security-rijndael/security-rijndael -
114% slowdown
MultiSource/Benchmarks/MiBench/network-patricia/network-patricia - 81.8%
slowdown
SingleSource/Benchmarks/Misc/flops-8 - 79% slowdown
Top compile-time slowdowns:
MultiSource/Benchmarks/MiBench/security-rijndael/security-rijndael -
832% slowdown
SingleSource/Benchmarks/Misc/salsa20 - 600% slowdown
MultiSource/Benchmarks/Trimaran/enc-3des/enc-3des - 263% slowdown
For this comparison, however (unlike comparing to plain -O3), there are
a significant number of compile-time speedups (I guess that this is
because vectorization can reduce the number of instructions processed by
later passes). Top compile-time speedups:
SingleSource/Benchmarks/Stanford/Oscar - 46% speedup
SingleSource/Regression/C/bigstack - 45% speedup
SingleSource/Benchmarks/BenchmarkGame/fannkuch - 45% speedup
>> Interesting. Do you understand what causes these slowdowns? Can your
>> heuristic be improved?
>
> I've not specifically looked at these cases.
>
> Generally, I've observed slowdowns from the introduction of too many
> permutations per chain (the chain selection procedure can be changed to
> help this, and I'll work on that). Also, sometimes legalization of
> non-native vector operations creates inefficient code, and I'll also
> look at these cases in more detail.
Sure. That sounds reasonable. I am confident you can improve this gradually.
>> If I understood correctly it seems your vectorizer has quadratic
>> complexity which may cause large slowdowns. Do you think it may be
>> useful/possible to make it linear by introducing a constant upper bound
>> somewhere? E.g. limiting it to 10/20/100 steps. Maybe we are lucky and
>> most of the vectorization opportunities are close by (in some sense),
>> such that we get most of the speedup by locking at a subset of the problem.
>
> Yes, I agree. That makes a lot of sense.
>
> What would be even better is if the loop unroller would intermix
> statements from the loops where possible instead of leaving it to the
> vectorizer to do all of the grouping after the fact. That, I fear, is a
> whole other project.
First, I do not understand the 'even better' here. To me it would be
great if on one hand the vectorizer could be constrained, such that the
compile time is predictable. And on the other hand, we could make the
loop unroller create code in a way such that the constrained vectorizer
still performs the relevant transformations.
I was not clear; I meant that imposing a cut off is likely to work, but
it would work even better if the loop unroller produced code
more-conducive to vectorization.
Also, what do you mean with 'if the loop unroller would intermix
statements from the loops where possible'. Are you referring to the
grouped unrolling as shown in my the last mail?
Yes.
Also, the code necessary to take advantage of grouped unrolling already
exists in the vectorizer. Enabling the flag -bb-vectorize-fast-dep
causes the vectorizer to stop searching for instruction pairings after
the first use of an instruction.
>>> Compared to previous patches, which had a minimum required chain length
>>> of 3 or 4, I've now made the default 6. While using a chain length of 4
>>> worked well for targeted benchmarks, it caused an overall slowdown on
>>> almost all test-suite programs. Using a minimum length of 6 causes, on
>>> average, a speedup; so I think that is a better default choice.
>>
>> I also try to understand if it is possible to use your vectorizer for
>> Polly. My idea is to do some clever loop unrolling.
>>
>> Starting from this loop.
>>
>> for (int i = 0; i< 4; i++)
>> A[i] += 1;
>> A[i] = B[i] + 3;
>> C[i] = A[i];
>>
>> The classical unroller would create this code:
>>
>> A[0] += 1;
>> A[0] = B[i] + 3;
>> C[0] = A[i];
>>
>> A[1] += 1;
>> A[1] = B[i] + 3;
>> C[1] = A[i];
>>
>> A[2] += 1;
>> A[2] = B[i] + 3;
>> C[2] = A[i];
>>
>> A[3] += 1;
>> A[3] = B[i] + 3;
>> C[3] = A[i];
>>
>> However, in case I can prove this loop is parallel, I want to create
>> this code:
>>
>> A[0] += 1;
>> A[1] += 1;
>> A[2] += 1;
>> A[3] += 1;
>>
>> A[0] = B[i] + 3;
>> A[1] = B[i] + 3;
>> A[2] = B[i] + 3;
>> A[3] = B[i] + 3;
>>
>> C[0] = A[i];
>> C[1] = A[i];
>> C[2] = A[i];
>> C[3] = A[i];
>>
>> I assume this will allow the vectorization of test cases, that failed
>> because of possible aliasing. However, I am more interested, if the
>> execution order change could also improve the vectorization outcome or
>> reduce compile time overhead of your vectorizer.
>
> Yes, this would certainly help.
In which way? How much? Would it be possible to restrict the vectorizer
such that it's complexity is linear, while at the same time we can
ensure it will still vectorize code that is 'group unrolled' (which
means unrolled in the way shown above?
It would help because we could stop the pairing search after an
instructions first use; and that is significantly faster and generates
many fewer potential pairings that need to be considered by the later
stages. Exactly "how much" it would help I can't yet answer; I'd need to
construct actual benchmarks for that. If you have some group-unrolled
code, then it will be simple to test: just pass -bb-vectorize-fast-dep.
> By the way, the current implementation, by default, it will create
> unaligned vector loads and stores, but these are generally broken up by
> the legalizer. This behavior can be suppressed using the
> -bb-vectorize-aligned-only flag. It would be nice if the loop unroller
> chose the unrolling factor to preserve the maximum available alignment,
> but I don't think that it currently does so.
I don't know, but it sounds like a good thing to do.
> One problem with the current implementation is that it relies on
> GetPointerBaseWithConstantOffset to determine if two loads or stores
> share the same base address. This fails with partially-unrolled loops
> because it cannot "undo" all of the additions to the offset induction
> variable in order to understand that some of the loads and stores are
> really adjacent in memory. This is something that I think can be
> improved within the vectorizer itself, and I'm planning on doing some
> work on this in the future.
Here you may also want to look into ScalarEvolution. Basically two loads
access adjacent memory if the difference of the scalar evolution of the
two load addresses is equal to sizeof(element_type). ScalarEvolution
should be a lot more general than GetPointerBaseWithConstantOffset().
Thanks! That sounds great; I'll have to look at that.
-Hal