[llvm-commits] [PATCH] BasicBlock Autovectorization Pass

[Tobias Grosser]

On 11/08/2011 03:36 PM, Hal Finkel wrote:
> On Tue, 2011-11-08 at 12:12 +0100, Tobias Grosser wrote:
>> On 11/08/2011 11:45 AM, Hal Finkel wrote:
>>> 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?

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.

>>> The largest three performance speedups are:
>>> SingleSource/Benchmarks/BenchmarkGame/puzzle - 59.2% speedup
>>> SingleSource/UnitTests/Vector/multiplies - 57.7% speedup
>>> SingleSource/Benchmarks/Misc/flops-7 - 50.75% speedup
>>>
>>> The largest three performance slowdowns are:
>>> MultiSource/Benchmarks/MiBench/security-rijndael/security-rijndael -
>>> 114% slowdown
>>> MultiSource/Benchmarks/MiBench/network-patricia/network-patricia - 66.6%
>>> slowdown
>>> SingleSource/Benchmarks/Misc/flops-8 - 64.2% slowdown
>>>
>> 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.

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?


>>> 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?

> 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().

Cheers
Tobi