[PATCH] D15489: Use getEdgeProbability() instead of getEdgeWeight() in BFI and remove getEdgeWeight() interfaces from MBPI.

[Dehao Chen via llvm-commits]

AutoFDO does not convert 0-weight to 1. In theory, converting 0-weight to 1
should not affect performance. I'll do some experiments to evaluate.

Dehao

On Tue, Dec 15, 2015 at 4:51 PM, Cong Hou <congh at google.com> wrote:

> On Tue, Dec 15, 2015 at 10:52 AM, Xinliang David Li via llvm-commits <
> llvm-commits at lists.llvm.org> wrote:
>
>> In most of the cases, BFI can rebuild BB weights from probability (plus
>> entry count) correctly, but there are exceptions including 1) functions
>> with irreducible loops; and 2) lossy profile data in AutoFDO case.
>>
>> For the example CFG, without Cong's patch, BFI can recover the BB2's
>> weight -- but this is not by design but based on the assumption that the
>> weight of backedge is not scaled (and still keep the original value of the
>> sample count). However since BFI will force BB2->BB3 to have have weight 1,
>> the iteration count of the loop will be 1000 which might be lower than
>> actual.  With Cong's patch, the weight of BB2 recomputed can be way higher
>> than actual (as it is using probability 1's nominator value).
>>
>> I think AutoFDO code needs to consider the limitation of BFI when
>> annotating loops -- basically if a loop has only single exit -- the exit
>> edge's weight can not be zero.
>>
>
> If we don't have 0 edge weight in AutoFDO and other weights are not very
> high, then we should not get very large loop count even with this patch. I
> think we have already transform all zero-weights into ones in AutoFDO,
> right?
>
> Cong
>
>
>>
>> David
>>
>> On Tue, Dec 15, 2015 at 10:22 AM, Dehao Chen via llvm-commits <
>> llvm-commits at lists.llvm.org> wrote:
>>
>>> For the following CFG:
>>>
>>> BB1(weight:100)
>>> BB2(weight:1000)
>>> BB3(weight:100)
>>>
>>> BB1->BB2(probability: 0%)
>>> BB1->BB3(probability: 100%)
>>> BB2->BB2(probability: 100%)
>>> BB2->BB3(probability: 0%)
>>>
>>> Is there a way we can rebuild BB weights from probability?
>>> Dehao
>>>
>>> On Mon, Dec 14, 2015 at 9:58 PM, David Li <davidxl at google.com> wrote:
>>> > davidxl added a comment.
>>> >
>>> > Ok.  This is a case where loop exit edge is 'never' taken where the
>>> loop backedge is executed. With instrumentation based PGO, even without FE
>>> fix up of the 0 weight edge, such as scenario (aka, bad meta data) will
>>> never occur, so whatever output of BFI will be fine.
>>> >
>>> > However, with AutoFDO, such MD_prof data can actually be generated .
>>> For instance, the function has a single BB loop, and only the BB in the
>>> loop has some samples but the entry/exit BB has none.
>>> >
>>> > The BFI result before this patch will most likely under-estimate the
>>> loop trip count, while with this patch, it will most likely over-estimate
>>> it. I think neither results are ideal, and AutoFDO needs to do something
>>> (apply some heuristic) to prevent such case from being generated.  +dehao
>>> and +dnovillo for comments.
>>> >
>>> >
>>> > http://reviews.llvm.org/D15489
>>> >
>>> >
>>> >
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>>
>>
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