llvm dev - Feb 2017 - [Proposal][RFC] Epilog loop vectorization

On 02/22/2017 11:52 AM, Adam Nemet via llvm-dev wrote:
> Hi Ashutosh,
>
>> On Feb 22, 2017, at 1:57 AM, Nema, Ashutosh via llvm-dev 
>> <llvm-dev at lists.llvm.org <mailto:llvm-dev at
lists.llvm.org>> wrote:
>>
>> Hi,
>> This is a proposal about epilog loop vectorization.
>> Currently Loop Vectorizer inserts an epilogue loop for handling loops 
>> that don’t have known iteration counts.
>> The Loop Vectorizer supports loops with an unknown trip count, 
>> unknown trip count may not be a multiple of the vector width, and the 
>> vectorizer has to execute the last few iterations as scalar code. It 
>> keeps a scalar copy of the loop for the remaining iterations.
>> Loop with the large width has a high possibility of executing many 
>> scalar iterations.
>> i.e. i8 data type with 256bits target register can vectorize with 
>> vector width 32, with that maximum trip count possibility for 
>> scalar(epilog) loop is 31, which is significant & worth
vectorizing.
>> Large vector factor has following challenges:
>> 1)Possibility of remainder iteration is substantial.
>> 2)Actual trip count at runtime is substantial but not meeting minimum 
>> trip count to execute vector loop.
>> These challenges can be addressed by mask instructions, but these 
>> instructions are limited and may not be available to all targets.
>> By epilog vectorization our aim to vectorize epilog loop where 
>> original loop is vectorized with large vector factor and has a high 
>> possibility of executing scalar iterations.
>> This require following changes:
>> 1)Costing: Preserve all profitable vector factor.
>> 2)Transform: Create an additional vector loop with next profitable 
>> vector factor.
>
> Is this something that you propose to be on by default for wide VPU 
> architectures without masking support? I.e. how widely is this 
> applicable?   If not then perhaps a better strategy would be to just 
> annotate the remainder loop with some metadata to limit the 
> vectorization factor and just rerun the vectorizer.
Why would this solution (annotating the remainder loop to limit 
vectorization and rerunning the vectorization process) not be preferred 
regardless?

One issue that might be relevant here are runtime aliasing checks, which 
are probably going to be redundant, or partially redundant, between the 
different vectorized versions. Will we be able to do any necessary 
cleanup after the fact? Moreover, we often don't hoist (unswitch) these 
checks out of inner loops (perhaps because they're inside the trip-count 
checks?); I wonder if the proposed block structure will make this 
situation better or worse (or have no overall effect).

Thanks again,
Hal
>
> Adam
>
>> Please refer attached file (BlockLayout.png) for the details about 
>> transformed block layout.
>> Patch is available at:https://reviews.llvm.org/D30247
>> Regards,
>> Ashutosh
>> <BlockLayout.png>_______________________________________________
>> LLVM Developers mailing list
>> llvm-dev at lists.llvm.org <mailto:llvm-dev at lists.llvm.org>
>> http://lists.llvm.org/cgi-bin/mailman/listinfo/llvm-dev
>
>
>
> _______________________________________________
> LLVM Developers mailing list
> llvm-dev at lists.llvm.org
> http://lists.llvm.org/cgi-bin/mailman/listinfo/llvm-dev
-- 
Hal Finkel
Lead, Compiler Technology and Programming Languages
Leadership Computing Facility
Argonne National Laboratory

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+1  for “just rerun the vectorizer” on the scalar remainder loop, as the
proposed decision process is broken down into “first determine best VF for main
loop, then determine best next EpilogVF for remainder loop” anyhow:

   const LoopVectorizationCostModel::VectorizationFactor EpilogVF          
CM.identifyNextProfitableVF(VF.Width);

Raising some aspects:

o The unroll factor may also affect the best EpilogVF. For instance, if UF=1
then EpilogVF < VF, as the patch currently enforces. But if UF is larger the
next profitable EpilogVF could be equal to VF.

o The scalar loop serves two purposes, as determined by its two pre-headers:
either as a default in case runtime dependence checks fail, or as a remainder
loop in which case it is known to be vectorizable with trip count less-than
VF*UF (or equal-to it*). Would be good to keep this in mind when rerunning.

(*) Note that if original loop requiresScalarEpilogue(), the trip count of the
remainder loop may be equal to VF*UF, and/or the vectorized remainder loop may
too require a scalar epilogue.

o It should be interesting to see how a single masked iteration that uses the
original VF, say for UF=1, works. LV should hopefully already support most of
what’s needed.

o The original Trip Count clearly affects the profitability of vectorizing the
remainder loop. Would be good to leverage any information that can be derived
about TC either statically or based on profiling, when determining EpilogVF.
Potential speedups and overheads/slowdowns could possibly be demonstrated using
extreme cases; what would the best and worst cases be? Perhaps
TinyTripCountVectorThreshold is also affected?

o Finally, VPlan is currently modeling how to vectorize the loop body according
to the potentially profitable VF’s. It’s modelling could be used to generate
vector code for both body and remainder, and to consider their combined, overall
cost.

Ayal.


From: llvm-dev [mailto:llvm-dev-bounces at lists.llvm.org] On Behalf Of Hal
Finkel via llvm-dev
Sent: Friday, February 24, 2017 00:14
To: Adam Nemet <anemet at apple.com>; Nema, Ashutosh <Ashutosh.Nema at
amd.com>
Cc: llvm-dev <llvm-dev at lists.llvm.org>
Subject: Re: [llvm-dev] [Proposal][RFC] Epilog loop vectorization



On 02/22/2017 11:52 AM, Adam Nemet via llvm-dev wrote:
Hi Ashutosh,

On Feb 22, 2017, at 1:57 AM, Nema, Ashutosh via llvm-dev <llvm-dev at
lists.llvm.org<mailto:llvm-dev at lists.llvm.org>> wrote:

Hi,

This is a proposal about epilog loop vectorization.

Currently Loop Vectorizer inserts an epilogue loop for handling loops that don’t
have known iteration counts.

The Loop Vectorizer supports loops with an unknown trip count, unknown trip
count may not be a multiple of the vector width, and the vectorizer has to
execute the last few iterations as scalar code. It keeps a scalar copy of the
loop for the remaining iterations.

Loop with the large width has a high possibility of executing many scalar
iterations.
i.e. i8 data type with 256bits target register can vectorize with vector width
32, with that maximum trip count possibility for scalar(epilog) loop is 31,
which is significant & worth vectorizing.

Large vector factor has following challenges:
1)    Possibility of remainder iteration is substantial.
2)    Actual trip count at runtime is substantial but not meeting minimum trip
count to execute vector loop.

These challenges can be addressed by mask instructions, but these instructions
are limited and may not be available to all targets.

By epilog vectorization our aim to vectorize epilog loop where original loop is
vectorized with large vector factor and has a high possibility of executing
scalar iterations.

This require following changes:
1)    Costing: Preserve all profitable vector factor.
2)    Transform: Create an additional vector loop with next profitable vector
factor.

Is this something that you propose to be on by default for wide VPU
architectures without masking support? I.e. how widely is this applicable?   If
not then perhaps a better strategy would be to just annotate the remainder loop
with some metadata to limit the vectorization factor and just rerun the
vectorizer.

Why would this solution (annotating the remainder loop to limit vectorization
and rerunning the vectorization process) not be preferred regardless?

One issue that might be relevant here are runtime aliasing checks, which are
probably going to be redundant, or partially redundant, between the different
vectorized versions. Will we be able to do any necessary cleanup after the fact?
Moreover, we often don't hoist (unswitch) these checks out of inner loops
(perhaps because they're inside the trip-count checks?); I wonder if the
proposed block structure will make this situation better or worse (or have no
overall effect).

Thanks again,
Hal



Adam



Please refer attached file (BlockLayout.png) for the details about transformed
block layout.

Patch is available at: https://reviews.llvm.org/D30247

Regards,
Ashutosh

<BlockLayout.png>_______________________________________________
LLVM Developers mailing list
llvm-dev at lists.llvm.org<mailto:llvm-dev at lists.llvm.org>
http://lists.llvm.org/cgi-bin/mailman/listinfo/llvm-dev





_______________________________________________

LLVM Developers mailing list

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--

Hal Finkel

Lead, Compiler Technology and Programming Languages

Leadership Computing Facility

Argonne National Laboratory
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Thanks for looking into this.

1) Issues with re running vectorizer:
Vectorizer might generate redundant alias checks while vectorizing epilog loop.
Redundant alias checks are expensive, we like to reuse the results of already
computed alias checks.
With metadata we can limit the width of epilog loop, but not sure about reusing
alias check result.
Any thoughts on rerunning vectorizer with reusing the alias check result ?

2) Best & worst case for epilog vectorization.
Epilog vectorization incurs additional cost of checks which decides to execute
either epilog vector loop or scalar loop.
These checks are:

a)      Min trip count check for epilog vector loop.

b)      Alias result check (only if alias check is generated for first vector
version)

Where the epilog vector trip count is large epilog vectorization with profitable
width likely to give gain. Worst case probably after these additional checks
executing the scalar loop.

3) Benchmarking & results:
We have observed 4% improvement in one of our internal benchmark.
Tried CPU2006 and didn’t found any degrade & improvements.
On code size increase I have not yet verified yet will check and get back.

4)  What should be the minimum first vector loop width to enable epilog
vectorization.
This count should not be small as it may degrade the performance, with my
limited tests I have observed 16 is a point it shows gains with one of our
internal benchmark. This require more experiments & testing to decide what
should be the minimum width.

5) Unrolling issues:
As Ayal mentioned with large unroll factor the next profitable EpilogVF could be
equal to VF.
With the same reason the current patch enforces UF=1, as unrolling can minimize
the possibility of executing epilog vector loop.

Example to understand the new layout:

void foo (char *A, char *B, char *C, int len) {
  int i = 0;
  for (i=0 ; i< len; i++)
    A[i] = B[i] + C[i];
}

This loop get vectorize with width 32 and epilog loop get vectorized with width
8.

a)      Please check attached IR(test.ll)

b)      To understand how alias check result got used, check temporary “acr.val”
usage.

Regards,
Ashutosh

From: Zaks, Ayal [mailto:ayal.zaks at intel.com]
Sent: Sunday, February 26, 2017 10:53 PM
To: Hal Finkel <hfinkel at anl.gov>; Adam Nemet <anemet at
apple.com>; Nema, Ashutosh <Ashutosh.Nema at amd.com>
Cc: llvm-dev <llvm-dev at lists.llvm.org>
Subject: RE: [llvm-dev] [Proposal][RFC] Epilog loop vectorization

+1  for “just rerun the vectorizer” on the scalar remainder loop, as the
proposed decision process is broken down into “first determine best VF for main
loop, then determine best next EpilogVF for remainder loop” anyhow:

   const LoopVectorizationCostModel::VectorizationFactor EpilogVF          
CM.identifyNextProfitableVF(VF.Width);

Raising some aspects:

o The unroll factor may also affect the best EpilogVF. For instance, if UF=1
then EpilogVF < VF, as the patch currently enforces. But if UF is larger the
next profitable EpilogVF could be equal to VF.
o The scalar loop serves two purposes, as determined by its two pre-headers:
either as a default in case runtime dependence checks fail, or as a remainder
loop in which case it is known to be vectorizable with trip count less-than
VF*UF (or equal-to it*). Would be good to keep this in mind when rerunning.

(*) Note that if original loop requiresScalarEpilogue(), the trip count of the
remainder loop may be equal to VF*UF, and/or the vectorized remainder loop may
too require a scalar epilogue.

o It should be interesting to see how a single masked iteration that uses the
original VF, say for UF=1, works. LV should hopefully already support most of
what’s needed.

o The original Trip Count clearly affects the profitability of vectorizing the
remainder loop. Would be good to leverage any information that can be derived
about TC either statically or based on profiling, when determining EpilogVF.
Potential speedups and overheads/slowdowns could possibly be demonstrated using
extreme cases; what would the best and worst cases be? Perhaps
TinyTripCountVectorThreshold is also affected?

o Finally, VPlan is currently modeling how to vectorize the loop body according
to the potentially profitable VF’s. It’s modelling could be used to generate
vector code for both body and remainder, and to consider their combined, overall
cost.

Ayal.


From: llvm-dev [mailto:llvm-dev-bounces at lists.llvm.org] On Behalf Of Hal
Finkel via llvm-dev
Sent: Friday, February 24, 2017 00:14
To: Adam Nemet <anemet at apple.com<mailto:anemet at apple.com>>;
Nema, Ashutosh <Ashutosh.Nema at amd.com<mailto:Ashutosh.Nema at
amd.com>>
Cc: llvm-dev <llvm-dev at lists.llvm.org<mailto:llvm-dev at
lists.llvm.org>>
Subject: Re: [llvm-dev] [Proposal][RFC] Epilog loop vectorization



On 02/22/2017 11:52 AM, Adam Nemet via llvm-dev wrote:
Hi Ashutosh,

On Feb 22, 2017, at 1:57 AM, Nema, Ashutosh via llvm-dev <llvm-dev at
lists.llvm.org<mailto:llvm-dev at lists.llvm.org>> wrote:

Hi,

This is a proposal about epilog loop vectorization.

Currently Loop Vectorizer inserts an epilogue loop for handling loops that don’t
have known iteration counts.

The Loop Vectorizer supports loops with an unknown trip count, unknown trip
count may not be a multiple of the vector width, and the vectorizer has to
execute the last few iterations as scalar code. It keeps a scalar copy of the
loop for the remaining iterations.

Loop with the large width has a high possibility of executing many scalar
iterations.
i.e. i8 data type with 256bits target register can vectorize with vector width
32, with that maximum trip count possibility for scalar(epilog) loop is 31,
which is significant & worth vectorizing.

Large vector factor has following challenges:
1)    Possibility of remainder iteration is substantial.
2)    Actual trip count at runtime is substantial but not meeting minimum trip
count to execute vector loop.

These challenges can be addressed by mask instructions, but these instructions
are limited and may not be available to all targets.

By epilog vectorization our aim to vectorize epilog loop where original loop is
vectorized with large vector factor and has a high possibility of executing
scalar iterations.

This require following changes:
1)    Costing: Preserve all profitable vector factor.
2)    Transform: Create an additional vector loop with next profitable vector
factor.

Is this something that you propose to be on by default for wide VPU
architectures without masking support? I.e. how widely is this applicable?   If
not then perhaps a better strategy would be to just annotate the remainder loop
with some metadata to limit the vectorization factor and just rerun the
vectorizer.

Why would this solution (annotating the remainder loop to limit vectorization
and rerunning the vectorization process) not be preferred regardless?

One issue that might be relevant here are runtime aliasing checks, which are
probably going to be redundant, or partially redundant, between the different
vectorized versions. Will we be able to do any necessary cleanup after the fact?
Moreover, we often don't hoist (unswitch) these checks out of inner loops
(perhaps because they're inside the trip-count checks?); I wonder if the
proposed block structure will make this situation better or worse (or have no
overall effect).

Thanks again,
Hal


Adam


Please refer attached file (BlockLayout.png) for the details about transformed
block layout.

Patch is available at: https://reviews.llvm.org/D30247

Regards,
Ashutosh

<BlockLayout.png>_______________________________________________
LLVM Developers mailing list
llvm-dev at lists.llvm.org<mailto:llvm-dev at lists.llvm.org>
http://lists.llvm.org/cgi-bin/mailman/listinfo/llvm-dev




_______________________________________________

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--

Hal Finkel

Lead, Compiler Technology and Programming Languages

Leadership Computing Facility

Argonne National Laboratory

---------------------------------------------------------------------
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