[llvm-dev] (RFC) Encoding code duplication factor in discriminator

Tue Nov 1 11:53:38 PDT 2016

On Tue, Nov 1, 2016 at 11:27 AM, Hal Finkel <hfinkel at anl.gov> wrote:

>
> ------------------------------
>
> *From: *"Dehao Chen" <dehao at google.com>
> *To: *"Hal Finkel" <hfinkel at anl.gov>
> *Cc: *"Xinliang David Li" <davidxl at google.com>, "llvm-dev" <
> llvm-dev at lists.llvm.org>
> *Sent: *Tuesday, November 1, 2016 1:24:01 PM
>
> *Subject: *Re: [llvm-dev] (RFC) Encoding code duplication factor in
> discriminator
>
>
>
> On Tue, Nov 1, 2016 at 10:50 AM, Hal Finkel <hfinkel at anl.gov> wrote:
>
>>
>> ------------------------------
>>
>> *From: *"Dehao Chen" <dehao at google.com>
>> *To: *"Hal Finkel" <hfinkel at anl.gov>
>> *Cc: *"Xinliang David Li" <davidxl at google.com>, "llvm-dev" <
>> llvm-dev at lists.llvm.org>
>> *Sent: *Tuesday, November 1, 2016 11:43:41 AM
>> *Subject: *Re: [llvm-dev] (RFC) Encoding code duplication factor in
>> discriminator
>>
>>
>>
>> On Fri, Oct 28, 2016 at 3:07 PM, Hal Finkel <hfinkel at anl.gov> wrote:
>>
>>> Hi Dehao,
>>>
>>> This is definitely an important problem, thanks for writing this up!
>>>
>>> There is a related problem that I think we can address at the same time:
>>> When we multiversion code, for example when we use runtime checks to enable
>>> the creation of a vectorized loop while retaining the scalar loop, and then
>>> we collect profiling data, we should be able to recover the relative
>>> running time of the different versions of the loop. In the future, I
>>> suspect we'll end up with transformations that produce even more versions
>>> of loops (Intel's compiler, for example, has a useful pragma that allows
>>> the user to request loop specializations for a specified set of trip
>>> counts).
>>>
>>> I'd like to have a scheme where the source location + discriminator can
>>> be mapped to information about the relevant loop version so that our
>>> profiling tools can display that information usefully to the user, and so
>>> that our optimizations can make useful choices (i.e. don't bother
>>> vectorizing a loop when the scalar loop is run a lot but the vectorized
>>> version almost never runs).
>>>
>>
>> That's definitely a valid and important use case, and it is important to
>> sample pgo too. That's why I proposed to have "duplicated code that may
>> have different execution count" being recorded. Will that suffice to get
>> the info you want? (i.e. for every version of the multi-versioned loop, you
>> will have a disincentive discriminator associated with all the code it
>> expands.
>>
>> I don't know. Can you explain how the process will work? By the time the
>> code/metadata arrives at, say, the loop vectorizer, how can we tell whether
>> the vectorized version we might now create will be executed (based on
>> having profiling data from a run where the compiler might have previously
>> made a similar choice)?
>>
>
> For example, for the following loop:
>
> for (i = 0; i < N; i++)
>   stmt;
>
> After many loop optimizations it could become something like:
>
> if (vectorized1)
>   for (i = 0;.....)
>      stmt;    //uid: 1
> else if (vectorized2)
>   for (i = 0;.....)
>     stmt;    //uid: 2
> else if (unrolled)
>   for (i = 0; i < N & (~0x3) i += 4)
>     stmt; stmt; stmt; stmt; //uid: 3
>   for (; i < N; i++)
>     stmt;  //uid: 4
> else
>   for (; i < N; i++)
>     stmt;  //uid: 5
>
> So basically, each clone of the loop will have a uid, which you can use to
> identify which version of the loop the instruction was coming from.
>
>
>>
>>
>>
>>>
>>> In short, I think that differentiating these different regions using the
>>> descriminator seems like a natural choice, but "And we do not need to
>>> introduce new building blocks to debug info" might not save us all that
>>> much in the long run. To keep information on what regions correspond to
>>> what optimizations, we may need to do that. That's not a bad thing, and I'd
>>> rather we solve this in a way that is extensible. Plus, this might make it
>>> easier to use fewer bits, thus helping the overall impact on the size of
>>> the debug sections.
>>>
>>
>> I agree that if we want to extend this in the future, we need to have
>> separate dwarf bits other than discriminator. For current use case,
>> discriminator seem to be good enough. And if we encode efficiently, it will
>> be better than introducing a new field. e.g., we can encode all info in a
>> 1-byte ULEB128 85%~90% of the time, but for a new field, we will at least
>> need 2 bytes if both discriminator and cloning info exists for an
>> instruction.
>>
>> Is this because you need at least one more byte for the debug-info field?
>>
>> In general, I don't really care where we stuff the bits so long as we can
>> get the necessary information back out. For a vectorized loop, for example,
>> we should be able to figure out which counts go to the vectorized loop vs.
>> the scalar loop. I don't, however, want to end up with something that is
>> super-non-extensible (e.g. we have only a few bits, so the vectorizer can
>> get one and the unroller can get one, but loop distribution is out of
>> luck). Maybe we need an 'extension' bit saying that there is more
>> information encoded elsewhere?
>>
>
> As illustrated in the above example, it is not like "vectorization has a
> distinct bit". All different optimizations make clones of code which will
> be labeled by UIDs represented by N (e.g. 8) bits. In this way, the space
> will be capped by the number of clones all optimizations have made, instead
> of # of optimizations that has applied. And it will be capped at 2^N-1. The
> cons of using uid is that you will not know if a clone is coming from
> vectorization or unroll or loop distribution.
>
> Okay, but that kind of semantic mapping is important. How should we
> encode/recover that information? To be clear, I'm not saying that we need
> to implement that up front, but there needs to be a clear path to an
> implementation, because I don't want to have two disjoint schemes.
>

You mean that you want to know which optimization created the clone? How
would you use that info? Looks to me this will expose compiler
implementation detail in debug info.

This is still doable, assume we have 15 interesting optimizations to track,
we can use 4 bits to encode the optimization type that created the clone.
But this becomes nasty if the a clone is created by more than one
optimizations. In that way, discriminator may not be fit for this purpose.

Thanks,
Dehao

>
> Thanks again,
> Hal
>
>
> Thanks,
> Dehao
>
>
>>
>> Thanks again,
>> Hal
>>
>>
>> Dehao
>>
>>
>>>
>>> Thanks again,
>>> Hal
>>>
>>> ------------------------------
>>>
>>> > From: "Dehao Chen via llvm-dev" <llvm-dev at lists.llvm.org>
>>> > To: llvm-dev at lists.llvm.org
>>> > Cc: "Xinliang David Li" <davidxl at google.com>
>>> > Sent: Thursday, October 27, 2016 1:39:15 PM
>>> > Subject: [llvm-dev] (RFC) Encoding code duplication factor in
>>> discriminator
>>> >
>>> > Motivation:
>>> > Many optimizations duplicate code. E.g. loop unroller duplicates the
>>> > loop body, GVN duplicates computation, etc. The duplicated code will
>>> > share the same debug info with the original code. For SamplePGO, the
>>> > debug info is used to present the profile. Code duplication will
>>> > affect profile accuracy. Taking loop unrolling for example:
>>> >
>>> >
>>> > #1 foo();
>>> > #2 for (i = 0; i < N; i++) {
>>> >
>>> > #3 bar();
>>> > #4 }
>>> >
>>> >
>>> > If N is 8 during runtime, a reasonable profile will look like:
>>> >
>>> >
>>> > #1: 10
>>> > #3: 80
>>> >
>>> >
>>> >
>>> > But if the compiler unrolls the loop by a factor of 4, the callsite
>>> > to bar() is duplicated 4 times and the profile will look like:
>>> >
>>> >
>>> > #1: 10
>>> > #3: 20
>>> >
>>> >
>>> > The sample count for #3 is 20 because all 4 callsites to bar() are
>>> > sampled 20 times each, and they shared the same debug loc (#3) so
>>> > that 20 will be attributed to #3 (If one debugloc is mapped by
>>> > multiple instructions, the max sample count of these instructions is
>>> > used as debugloc's sample count).
>>> >
>>> >
>>> > When loading this profile into compiler, it will think the loop trip
>>> > count is 2 instead of 8.
>>> >
>>> >
>>> > Proposal:
>>> > When compiler duplicates code, it encodes the duplication info in the
>>> > debug info. As the duplication is not interesting to debugger, I
>>> > propose to encode this as part of the discriminator.
>>> >
>>> >
>>> > There are 2 types of code duplication:
>>> >
>>> >
>>> > 1. duplicated code are guaranteed to have the same execution count
>>> > (e.g. loop unroll and loop vectorize). We can record the duplication
>>> > factor, for the above example "4" is recorded in the discriminator.
>>> > 2. duplicated code that may have different execution count (e.g. loop
>>> > peel and gvn). For a same debugloc, a unique number is assigned to
>>> > each copy and encoded in the discriminator.
>>> >
>>> >
>>> > Assume that the discriminator is uint32. The traditional
>>> > discriminator is less than 256, let's take 8 bit for it. For
>>> > duplication factor (type 1 duplication), we assume the maximum
>>> > unroll_factor * vectorize_factor is less than 256, thus 8 bit for
>>> > it. For unique number(type 2 duplication), we assume code is at most
>>> > duplicated 32 times, thus 5 bit for it. Overall, we still have 11
>>> > free bits left in the discriminator encoding.
>>> >
>>> >
>>> > Let's take the original source as an example, after loop unrolling
>>> > and peeling, the code may looks like:
>>> >
>>> >
>>> > for (i = 0; i < N & 3; i+= 4) {
>>> > foo(); // discriminator: 0x40
>>> > foo(); // discriminator: 0x40
>>> > foo(); // discriminator: 0x40
>>> > foo(); // discriminator: 0x40
>>> > }
>>> > if (i++ < N) {
>>> > foo(); // discriminator: 0x100
>>> > if (i++ < N) {
>>> > foo(); // discriminator: 0x200
>>> > if (i++ < N) {
>>> > foo(); // discriminator: 0x300
>>> > }
>>> > }
>>> > }
>>> >
>>> >
>>> > The cost of this change would be increased debug_line size because:
>>> > 1. we are recording more discriminators 2. discriminators become
>>> > larger and will take more ULEB128 encoding.
>>> >
>>> >
>>> > The benefit is that the sample pgo profile can accurately represent
>>> > the code execution frequency. And we do not need to introduce new
>>> > building blocks to debug info.
>>> >
>>> >
>>> > Comments?
>>> >
>>> >
>>> > Thanks,
>>> > Dehao
>>> > _______________________________________________
>>> > 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
>>>
>>
>>
>>
>>
>> --
>> Hal Finkel
>> Lead, Compiler Technology and Programming Languages
>> Leadership Computing Facility
>> Argonne National Laboratory
>>
>
>
>
>
> --
> Hal Finkel
> Lead, Compiler Technology and Programming Languages
> Leadership Computing Facility
> Argonne National Laboratory
>
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