[LLVMdev] RFC - Improvements to PGO profile support

[Xinliang David Li]

On Tue, Mar 24, 2015 at 2:47 PM, Bob Wilson <bob.wilson at apple.com> wrote:
>
>> On Mar 24, 2015, at 12:08 PM, Xinliang David Li <davidxl at google.com> wrote:
>>
>> On Tue, Mar 24, 2015 at 10:54 AM, Bob Wilson <bob.wilson at apple.com> wrote:
>>>
>>>> On Mar 24, 2015, at 10:53 AM, Diego Novillo <dnovillo at google.com> wrote:
>>>>
>>>> On Tue, Mar 24, 2015 at 1:48 PM, Bob Wilson <bob.wilson at apple.com> wrote:
>>>>>
>>>>>> On Mar 24, 2015, at 10:27 AM, Xinliang David Li <davidxl at google.com> wrote:
>>>>>>
>>>>>> 2.3) remove the 'laplace rule of succession' which can be very harmful
>>>>>
>>>>> I have not seen any consensus on this. I’m not at all convinced this would be a good idea. From what I’ve seen, using LaPlace’s rule avoids really bad behavior in cases where the counts are very small and has almost no impact when the counts are large.
>>>>
>>>> Duncan and I chatted briefly about this on IRC. I'm going to
>>>> experiment with only applying laplace smoothing if any scaled weights
>>>> are 0. AFAIU, usage of this rule is really just trying to avoid having
>>>> downstream users deal with 0 weights. Duncan, please whack me with a
>>>> clue stick if I totally misrepresented our conclusions.
>>>
>>> Zero is the extreme case, but it’s also important for other small counts. I’d like to see some specific examples of why you think the current behavior is harmful.
>>
>> Using the rule has at least the following bad side effects:
>> 1) wrong estimation of average loop trip count -- possibly leading to
>> wrong unroll decisions
>
> I remain skeptical. If the loop only ran once in your profile run, how much confidence do you have in the trip count? The LaPlace rule will tend to make the compiler more conservative in exactly the cases where there is insufficient information to be confident about what to do.

Training run is expensive, it is common for PGO users to reduce
workload as much as possible without sacrificing the
representativeness of the workload.

Workload 1:  loop run once, iterating 100 times in total
workload 2:  loop run twice, iterating 200 times in total

Which workload  will user prefer to use?

Without the rule, the two workload at least produces consistent
profile data.  With the Laplace rule, you get 50 in one case, and 66
in the other.

Having some technology to improve confidence of the profile data is
fine, but I don't see
1) how laplace rule is good for it
2) why this can not be done in the consumer side (i.e., faithfully
record the profile data).


>
>> 2) result in bad inlining decisions. For instance:
>>    for (...)
>>        bar();  // (1)
>>
>> where (1) is the only callsite to bar().   Using the rule, BB count
>> enclosing the call to bar() can be as low as half of the entry count
>> of bar().  Inliner will get confused and think there are more hot
>> callsites to 'bar' and  make suboptimal decisions ..
>>
>> Also if bar has calls to other functions, those callsites will look
>> hotter than the call to 'bar' …
>
> Your own proposal for recording entry counts is to record “relative hotness”, not absolute profile counts.

The proposal is to record 'global hotness' that can used to compare
relative hotness across procedural boundaries (e.g. callsites in
different callers). Profile counts satisfies this condition.

>On the caller’s side, we’ve got a branch weight influenced by LaPlace’s rule that is then used to compute BlockFrequency and you’re concerned about a mismatch between that the “relative hotness” recorded for the callee??

Basically, say the caller is test()

bar(){
  // ENTRY count =  100 (from profile data)
  // ENTRY freq = 1

  // BB2: Freq(BB2) = 1, count = 100
  foo ();              (2)
}


test() {
   // ENTRY count = 1 (from profile data)
   // Entry Freq = 1
   for (i = 0; i < 100; i++) {
       // BB1: Freq(BB1) = 50 due to Laplace rule
       bar();  // Freq = 50, count = 50    (1)
    }
}

With laplace rule, the block freq computed for bar's enclosing BB will
be wrong -- as a result, the bar's enclosing BB's count will  be wrong
too: 50*1/1 = 50.

The global hotness of call site (1) & (2) should be the same, but
distorted when Laplace rule is used.

Yes, we care about using PGO across routine boundaries for IPO.

>
>>
>> The attached are two cases as well as the frequency graph computed
>> today (with the laplace rule) and the correct frequency expected.
>
> I’d be a lot more interested to see a real-world example.

See my reply above. On the other hand, I'd like to see examples where
LaPlace Rule can actually help improve the profile data quality.

thanks,

David