llvm dev - Feb 2014 - [LLVMdev] [RFC] BlockFrequency is the wrong metric; we need a new one

On Sun, Feb 2, 2014 at 6:18 PM, Andrew Trick <atrick at apple.com> wrote:
>
> On Feb 2, 2014, at 2:13 AM, Chandler Carruth <chandlerc at gmail.com>
wrote:
>
> > Right now, all profile information is funneled through two analysis
> passes prior to any part of the optimizer using it.
> >
> > First, we have BranchProbabilityInfo, which provides a simple
interface
> to the simplest form of profile information: local and relative branch
> probabilities. These merely express the likelihood of taking one of a
> mutually exclusive set of exit paths from a basic block. They are very
> simple, and the foundation of the profile information. Even the other
> analysis is merely built on top of this one.
> >
> > Second we have BlockFrequencyInfo which attempts to provide a more
> "global" (function-wide, not actually program wide) view of the
statistical
> frequency with which any particular basic block is executed. This is nicely
> principled analysis that just computes the probabilistic flow of control
> through the various branches according to their probabilities established
> in the first analysis.
> >
> >
> > However, I think that BlockFrequencyInfo provides the wrong set of
> information. There is one critical reason why. Let's take a totally
> uninteresting CFG:
> >
> > A -> B1, B2
> > B1 -> C1, C2
> > B2 -> C3, C4
> > C1 -> D1, D2
> > C2 -> ret
> > C3 -> D3, D4
> > C4 -> ret
> > D1 -> E1, E2
> > D2 -> ret
> > D3 -> E3, E4
> > D4 -> ret
> >
> > You can imagine this repeating on for as many levels as you like. This
> isn't an uncommon situation with real code. BlockFrequencyInfo computes
for
> this a very logical answer:
> >
> > ---- Block Freqs ----
> >  a = 1.0
> >   a -> b1 = 0.5
> >   a -> b2 = 0.5
> >  b1 = 0.5
> >   b1 -> c1 = 0.25
> >   b1 -> c2 = 0.25
> >  b2 = 0.5
> >   b2 -> c3 = 0.25
> >   b2 -> c4 = 0.25
> >  c1 = 0.25
> >   c1 -> d1 = 0.125
> >   c1 -> d2 = 0.125
> >  c2 = 0.25
> >  c3 = 0.25
> >   c3 -> d3 = 0.125
> >   c3 -> d4 = 0.125
> >  c4 = 0.25
> >  d1 = 0.125
> >   d1 -> e1 = 0.0625
> >   d1 -> e2 = 0.0625
> >  d2 = 0.125
> >  d3 = 0.125
> >   d3 -> e3 = 0.0625
> >   d3 -> e4 = 0.0625
> >  d4 = 0.125
> >
> > One way of thinking about this is that for any basic block X which is
> predicated by N branches of unbiased probability (50/50), the frequency
> computed for that block is 2^(-N).
> >
> > The problem is that this doesn't represent anything close to
reality.
> Processors' branch prediction works precisely because very, *very* few
> branches in programs are 50/50. Most programs do not systematically explore
> breadth first the full diversity of paths through the program. And yet, in
> the absense of better information our heuristics would lead us to believe
> (and act!) as though this were true.
> >
> > Now, I'm not saying that the computation of block frequencies is
wrong.
> Merely that it cannot possibly be used for at least one of it purposes --
> it's relative frequency (to the entry block "basis"
frequency) is
> completely useless for detecting hot or cold regions of a function -- it
> will simply claim that all regions of the function are cold. What it *is*
> useful for is establishing a total ordering over the basic blocks of a
> function. So it works well for some things like code layout, but is grossly
> misleading for others.
> >
> >
> > There are several possible solutions here. I'll outline my
proposal as
> well as some other ideas.
> >
> >
> > BlockWeights instead of BlockFrequencies. My idea is that we don't
> really care about the depth of the control dependence for a particular
> basic block. We care about the accumulated *bias* toward or away from a
> basic block. This is predicated on the idea that branches are
> overwhelmingly predictable. As a consequence, evenly distributed
> probabilities are really just *uncertainties*.
>
> This sounds like an interesting way to merge unkown branch probabilities,
> static heuristics, statistical profiles, and partial profiles.
>
> > My proposed way of implementing this would take the exact algorithm
> already used in BlockFrequency, but instead of computing a frequency for an
> edge based on the probability of the branch times the frequency of the
> predecessor, instead compute it as the frequency of the predecessor times
> how "biased" the branch is relative to other branches in the
predecessor.
> Essentially, this would make a branch probability set of {0.5, 0.5} produce
> edge frequencies equal to the predecessor's edge frequency.
> >
> > The result of such a system would produce weights for every block in
the
> above CFG as '1.0', or equivalent to the entry block weight. This
to me is
> a really useful metric -- it indicates that no block in the CFG is really
> more or less likely than any other. Only *biases* in a specific direction
> would cause the block weights to go up or down significantly.
>
> I don't like this statement, or don't understand it. It is useful
to know
> a branch is unbiased. Currently we assume branches are unbiased then
> optimize conservatively in those cases (do no harm). But if we had greater
> confidence in unbiased branches (because the branch was actually profiled),
> we could if-convert much more aggressively.
>
I think the key is that I'm not talking about changing how we model
branches at all. I agree that it would be useful to model both our
confidence in the relative probabilities and the probabilities themselves
for things like if-conversion. We can still do that.

But the current users of 'block frequency' are not trying to understand
a
specific branch as biased or un-biased, or deal with our confidence.
They're just trying to understand how "important" a specific basic
block is
to the execution of the function.


> > This would immediately make the analysis useful to consumers such as
the
> vectorizer, unroller, or when we have the capability, the inliner and an
> outliner which respect cold regions of functions.
>
> I'm a little concerned that you're adding complexity that may not
be
> necessary. Algorithms like if-conversion make use of the fact that the
> block weights are additive.

Are you talking about branch probabilities here? I don't believe we use
block frequencies in anything other than:
1) MachineBlockPlacement
2) Spill cost, placement, and generally the register allocator
3) StackSlotColoring (but really only for spill weight computation)
4) LoopVectorizer (disabled currently)

I just want to make sure we're on the same page. I don't think it really
invalidates your concern.

I'm sure they could be adapted to something more sophisticated, but
these
> heuristics are notoriously hard to tune. There is value in a simple model.
> You say that your approach is simple, but I would have to think hard about
> handling block frequencies that are inconsistent with their immediate
> branch probabilities.
>
If they were inconsistent, I would agree. But they are in fact consistent
and additive. They even preserve information during CFG merge points. The
only difference is that they scale (both up and down) relative to the bias
of a branch (probability's delta from the mean) rather than relative to the
flat probability.

So I don't think this makes the computation, propagation or mathematical
model more complex. What it does is shift it from a flow frequency modeling
to biased branch modeling. That is certainly a change and worth justifying,
I just don't want you to imagine it as having more impact than it does.

So the question is: why do you really need more
sophistication?
>
> Part of you problem is that you're looking at frequency relative to the
> entry block when you should be looking at frequency relative to the region
> being optimized. When deciding to unroll, compare the loop header with the
> preheader.
>
Actually, I did that already. ;] I really thought that would work back when
we were first discussing how to use block frequencies and have not
forgotten it. However, that doesn't actually capture the the frequency
relative to the region. I think an example is best. Let's imagine that in
my CFG above "E1" is a loop preheader. There is some loop header below
it
in the CFG that I hadn't reached yet.

The first thing to realize is that the heuristic we want is not how hot (or
cold) the loop header is relative to the preheader. That tells us, once we
execute this loop, how many times are we likely to take the backedge (or
for a nested loop, one of the backedges). But this number may be correctly
be arbitrarily large if we have a loop in a cold region which when executed
loops for a very long time. What we actually want to check for is how
frequently we enter the loop from outside the loop at all.

So the test is how "hot" is the loop preheader relative to its
"region".
Now, if the loop preheader is nested inside of another loop, we might
usefully compare the inner loop's preheader to the outer loop's header
and
get a useful metric of "hot" or "cold". But what happens
when we are trying
to test for this in the outer most loop? We need some way to define a
"region" of the function body itself then. There are a bunch of viable
definitions. The one which maps to the loop header for a loop nest is the
function entry block. However, that is the precise circumstance that
doesn't work -- in a branch program, most blocks are "cold"
relative to the
function entry if we simply trust the block frequency.

Now, there are other ways to define a "region". I mentioned one as an
alternative to my proposal -- we could look at block frequencies only
relative to the root of their domtree. Another would be to use SESE
regions, or any number of others. However, they all have the problem that
we must do substantial work and add substantial complexity and
sophistication to compute the region, the frequency for that region, and
then compare ours against it.

As a practical matter, they also have the problem of accumulating
significant error rapidly due to continual division of the entry frequency.
My proposal actually mitigates such problems significantly.

> I think you're trying to avoid unrolling even high trip count loops
that
> are  seldom reached, and you're using a non-negligible frequency for
"cold"
> code (e.g. > 2^-10). In that case it sounds like we need a static
heuristic
> to handle the pattern of chains of branches with no CFG merge. The
> fall-thru path should be > 0.5.
>
I'm really not looking at a single problem or solution. There are a wide
variety of consumers of profile information that want to specially handle
both cold and hot regions of a function. Looking at the frequencies as they
exist today, these transformations cannot conclude either cold or hot for a
region of code from them. We need something better.

If we had any static heuristic that was good at predicting *which* long
chain should be >0.5 probability, we would be using it already. The problem
is that there is no single idea of "fallthrough". We canonicalize the
branches, and even programmers cannot make up their mind. Is the early exit
the fast-path or is the early exit the error handling? Both patterns exist
widely. If we have raw profile information, then yes, this is *much* less
of a problem because we will actually know what direction all of the
predictable branches in the program actually take. But our analyses
currently are deceptive in the absence of strong profile information by
giving the impression that the resulting branches are *unpredictable*
instead of being predictable and unknown.

So, I think this is a cool idea. If I better understood how it worked
I
> might be less concerned about the complexity. I'm not very convinced
that
> it is necessary yet.
>
Ok, for the necessity, simply take any function from a simple benchmark,
and look at its static block frequencies. Arnold posted a great example
from libquantum. Here we have the most boring code (1 loop in a tiny basic
block) and in a boring benchmark that is completely predictable. Yet still,
we think that the loop is reached less than 20% of the times that the
function is called. That just doesn't make sense.


If you want to take a more complex approach, we could definitely add a
confidence dimension to the branch probability. This would allow us to
express a high-confidence un-predictable branch and optimize based on that.
However, we would *still* want block weights to be computed more like I
have proposed. Instead of only branch probabilities that were far outside
of 50/50 influencing it, we would want branch probabilities that were
highly confident to influence it. The practical result would be exactly the
same as what I have proposed, it is just that I'm making the simplifying
assumption that at the moment, there are no evenly distributed branch
probabilities which we have high confidence in.

My suspicion is that we will find even with direct profile information,
this will still be true. Mostly, I suspect there are relatively few cases
where it is important we model an even distribution of branches with high
probability. I'm happy to be wrong about this, but *that* is where I want
to wait for more data to make a decision that adds complexity to the system.
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On Feb 2, 2014, at 6:55 PM, Chandler Carruth <chandlerc at gmail.com>
wrote:
> 
> If you want to take a more complex approach, we could definitely add a
confidence dimension to the branch probability. This would allow us to express a
high-confidence un-predictable branch and optimize based on that. However, we
would *still* want block weights to be computed more like I have proposed.
Instead of only branch probabilities that were far outside of 50/50 influencing
it, we would want branch probabilities that were highly confident to influence
it. The practical result would be exactly the same as what I have proposed, it
is just that I'm making the simplifying assumption that at the moment, there
are no evenly distributed branch probabilities which we have high confidence in.
Adding a confidence dimension seems like overkill to me. It seems like we should
be able to distinguish branches with known 50/50 probability, e.g., from real
profile data, from those where we have no idea, simply by omitting the branch
probability information in the latter case. I suppose that is essentially adding
a confidence dimension, but only in a very simple way.

That’s a separate question from what to do when you don’t know anything about
the probability of a branch. Andy has thought a lot more about this than I have,
so for now, I will defer to his opinion. I will just point out that Duncan Exon
Smith is currently looking at rewriting the BlockFrequency pass in a way that
Jakob had suggested to help fix some serious problems with the loss of
precision. Your proposed change to BlockFrequency would almost certainly have an
impact on his work, so hopefully we can figure out soon what we want to do.

Incidentally, while looking at this, I noticed that our current static
heuristics are assigning naive 50/50 probabilities due to a bug. I filed PR18705
to describe it, but basically we’re often doing the wrong thing for anything
inside a loop.

On Sun, Feb 2, 2014 at 9:42 PM, Bob Wilson <bob.wilson at apple.com>
wrote:
> Adding a confidence dimension seems like overkill to me. It seems like we
> should be able to distinguish branches with known 50/50 probability, e.g.,
> from real profile data, from those where we have no idea, simply by
> omitting the branch probability information in the latter case. I suppose
> that is essentially adding a confidence dimension, but only in a very
> simple way.
>
BranchProbabilityInfo exposes *some* probability for every branch as a
significant simplification to the consumers. I don't think we want to
trivially give this property up.

>
> That’s a separate question from what to do when you don’t know anything
> about the probability of a branch. Andy has thought a lot more about this
> than I have, so for now, I will defer to his opinion. I will just point out
> that Duncan Exon Smith is currently looking at rewriting the BlockFrequency
> pass in a way that Jakob had suggested to help fix some serious problems
> with the loss of precision. Your proposed change to BlockFrequency would
> almost certainly have an impact on his work, so hopefully we can figure out
> soon what we want to do.
>
Yes, indeed. My change would make the precision issues significantly less
severe, but they're still fundamentally present.

> Incidentally, while looking at this, I noticed that our current static
> heuristics are assigning naive 50/50 probabilities due to a bug. I filed
> PR18705 to describe it, but basically we’re often doing the wrong thing for
> anything inside a loop.
>
Yea, just a bug. Most of the profile input that I care about comes before
the loop heuristic though, so this hasn't been hurting us heavily. I do
worry that it may have been hiding significant static heuristic problems
further down the stack.

-Chandler
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On Feb 2, 2014, at 6:55 PM, Chandler Carruth <chandlerc at gmail.com>
wrote:
> On Sun, Feb 2, 2014 at 6:18 PM, Andrew Trick <atrick at apple.com>
wrote:
> 
> On Feb 2, 2014, at 2:13 AM, Chandler Carruth <chandlerc at gmail.com>
wrote:
> 
> > Right now, all profile information is funneled through two analysis
passes prior to any part of the optimizer using it.
> >
> > First, we have BranchProbabilityInfo, which provides a simple
interface to the simplest form of profile information: local and relative branch
probabilities. These merely express the likelihood of taking one of a mutually
exclusive set of exit paths from a basic block. They are very simple, and the
foundation of the profile information. Even the other analysis is merely built
on top of this one.
> >
> > Second we have BlockFrequencyInfo which attempts to provide a more
"global" (function-wide, not actually program wide) view of the
statistical frequency with which any particular basic block is executed. This is
nicely principled analysis that just computes the probabilistic flow of control
through the various branches according to their probabilities established in the
first analysis.
> >
> >
> > However, I think that BlockFrequencyInfo provides the wrong set of
information. There is one critical reason why. Let's take a totally
uninteresting CFG:
> >
> > A -> B1, B2
> > B1 -> C1, C2
> > B2 -> C3, C4
> > C1 -> D1, D2
> > C2 -> ret
> > C3 -> D3, D4
> > C4 -> ret
> > D1 -> E1, E2
> > D2 -> ret
> > D3 -> E3, E4
> > D4 -> ret
> >
> > You can imagine this repeating on for as many levels as you like. This
isn't an uncommon situation with real code. BlockFrequencyInfo computes for
this a very logical answer:
> >
> > ---- Block Freqs ----
> >  a = 1.0
> >   a -> b1 = 0.5
> >   a -> b2 = 0.5
> >  b1 = 0.5
> >   b1 -> c1 = 0.25
> >   b1 -> c2 = 0.25
> >  b2 = 0.5
> >   b2 -> c3 = 0.25
> >   b2 -> c4 = 0.25
> >  c1 = 0.25
> >   c1 -> d1 = 0.125
> >   c1 -> d2 = 0.125
> >  c2 = 0.25
> >  c3 = 0.25
> >   c3 -> d3 = 0.125
> >   c3 -> d4 = 0.125
> >  c4 = 0.25
> >  d1 = 0.125
> >   d1 -> e1 = 0.0625
> >   d1 -> e2 = 0.0625
> >  d2 = 0.125
> >  d3 = 0.125
> >   d3 -> e3 = 0.0625
> >   d3 -> e4 = 0.0625
> >  d4 = 0.125
> >
> > One way of thinking about this is that for any basic block X which is
predicated by N branches of unbiased probability (50/50), the frequency computed
for that block is 2^(-N).
> >
> > The problem is that this doesn't represent anything close to
reality. Processors' branch prediction works precisely because very, *very*
few branches in programs are 50/50. Most programs do not systematically explore
breadth first the full diversity of paths through the program. And yet, in the
absense of better information our heuristics would lead us to believe (and act!)
as though this were true.
> >
> > Now, I'm not saying that the computation of block frequencies is
wrong. Merely that it cannot possibly be used for at least one of it purposes --
it's relative frequency (to the entry block "basis" frequency) is
completely useless for detecting hot or cold regions of a function -- it will
simply claim that all regions of the function are cold. What it *is* useful for
is establishing a total ordering over the basic blocks of a function. So it
works well for some things like code layout, but is grossly misleading for
others.
> >
> >
> > There are several possible solutions here. I'll outline my
proposal as well as some other ideas.
> >
> >
> > BlockWeights instead of BlockFrequencies. My idea is that we don't
really care about the depth of the control dependence for a particular basic
block. We care about the accumulated *bias* toward or away from a basic block.
This is predicated on the idea that branches are overwhelmingly predictable. As
a consequence, evenly distributed probabilities are really just *uncertainties*.
> 
> This sounds like an interesting way to merge unkown branch probabilities,
static heuristics, statistical profiles, and partial profiles.
> 
> > My proposed way of implementing this would take the exact algorithm
already used in BlockFrequency, but instead of computing a frequency for an edge
based on the probability of the branch times the frequency of the predecessor,
instead compute it as the frequency of the predecessor times how
"biased" the branch is relative to other branches in the predecessor.
Essentially, this would make a branch probability set of {0.5, 0.5} produce edge
frequencies equal to the predecessor's edge frequency.
> >
> > The result of such a system would produce weights for every block in
the above CFG as '1.0', or equivalent to the entry block weight. This to
me is a really useful metric -- it indicates that no block in the CFG is really
more or less likely than any other. Only *biases* in a specific direction would
cause the block weights to go up or down significantly.
> 
> I don't like this statement, or don't understand it. It is useful
to know a branch is unbiased. Currently we assume branches are unbiased then
optimize conservatively in those cases (do no harm). But if we had greater
confidence in unbiased branches (because the branch was actually profiled), we
could if-convert much more aggressively.
> 
> I think the key is that I'm not talking about changing how we model
branches at all. I agree that it would be useful to model both our confidence in
the relative probabilities and the probabilities themselves for things like
if-conversion. We can still do that.
> 
> But the current users of 'block frequency' are not trying to
understand a specific branch as biased or un-biased, or deal with our
confidence. They're just trying to understand how "important" a
specific basic block is to the execution of the function.
Yes, I understand that. My concern was that passes may assume frequency/weight
is consistent with probability (path weights add up to their merge/join points).
At least, I'm used to making that assumption. Take a simple CFG:

A
|\
B C
| |\
D E F
| |/
|/
G

If each basic block has cost=1. Path A,B,D,G has weighted cost=4. Path A,C,E|F,G
has weighted cost=5. But they should be equal.
>  
> > This would immediately make the analysis useful to consumers such as
the vectorizer, unroller, or when we have the capability, the inliner and an
outliner which respect cold regions of functions.
> 
> I'm a little concerned that you're adding complexity that may not
be necessary. Algorithms like if-conversion make use of the fact that the block
weights are additive.
> 
> Are you talking about branch probabilities here? I don't believe we use
block frequencies in anything other than:
> 1) MachineBlockPlacement
> 2) Spill cost, placement, and generally the register allocator
> 3) StackSlotColoring (but really only for spill weight computation)
> 4) LoopVectorizer (disabled currently)
> 
> I just want to make sure we're on the same page. I don't think it
really invalidates your concern.
You're right. I thought early if-conversion was multiplying the cost of each
path by its weight. We aren't doing that. Instead we're taking a
do-no-harm approach of estimating critical path increase without considering
probability. It's actually more consistent with your approach.
> I'm sure they could be adapted to something more sophisticated, but
these heuristics are notoriously hard to tune. There is value in a simple model.
You say that your approach is simple, but I would have to think hard about
handling block frequencies that are inconsistent with their immediate branch
probabilities.
> 
> If they were inconsistent, I would agree. But they are in fact consistent
and additive. They even preserve information during CFG merge points. The only
difference is that they scale (both up and down) relative to the bias of a
branch (probability's delta from the mean) rather than relative to the flat
probability.
> 
> So I don't think this makes the computation, propagation or
mathematical model more complex. What it does is shift it from a flow frequency
modeling to biased branch modeling. That is certainly a change and worth
justifying, I just don't want you to imagine it as having more impact than
it does.
> 
> So the question is: why do you really need more sophistication?
> 
> Part of you problem is that you're looking at frequency relative to the
entry block when you should be looking at frequency relative to the region being
optimized. When deciding to unroll, compare the loop header with the preheader.
> 
> Actually, I did that already. ;] I really thought that would work back when
we were first discussing how to use block frequencies and have not forgotten it.
However, that doesn't actually capture the the frequency relative to the
region. I think an example is best. Let's imagine that in my CFG above
"E1" is a loop preheader. There is some loop header below it in the
CFG that I hadn't reached yet.
> 
> The first thing to realize is that the heuristic we want is not how hot (or
cold) the loop header is relative to the preheader. That tells us, once we
execute this loop, how many times are we likely to take the backedge (or for a
nested loop, one of the backedges). But this number may be correctly be
arbitrarily large if we have a loop in a cold region which when executed loops
for a very long time. What we actually want to check for is how frequently we
enter the loop from outside the loop at all.
> 
> So the test is how "hot" is the loop preheader relative to its
"region". Now, if the loop preheader is nested inside of another loop,
we might usefully compare the inner loop's preheader to the outer loop's
header and get a useful metric of "hot" or "cold". But what
happens when we are trying to test for this in the outer most loop? We need some
way to define a "region" of the function body itself then. There are a
bunch of viable definitions. The one which maps to the loop header for a loop
nest is the function entry block. However, that is the precise circumstance that
doesn't work -- in a branch program, most blocks are "cold"
relative to the function entry if we simply trust the block frequency.
> 
> Now, there are other ways to define a "region". I mentioned one
as an alternative to my proposal -- we could look at block frequencies only
relative to the root of their domtree. Another would be to use SESE regions, or
any number of others. However, they all have the problem that we must do
substantial work and add substantial complexity and sophistication to compute
the region, the frequency for that region, and then compare ours against it.
> 
> As a practical matter, they also have the problem of accumulating
significant error rapidly due to continual division of the entry frequency. My
proposal actually mitigates such problems significantly.
> 
> 
> I think you're trying to avoid unrolling even high trip count loops
that are  seldom reached, and you're using a non-negligible frequency for
"cold" code (e.g. > 2^-10). In that case it sounds like we need a
static heuristic to handle the pattern of chains of branches with no CFG merge.
The fall-thru path should be > 0.5.
> 
> I'm really not looking at a single problem or solution. There are a
wide variety of consumers of profile information that want to specially handle
both cold and hot regions of a function. Looking at the frequencies as they
exist today, these transformations cannot conclude either cold or hot for a
region of code from them. We need something better.
> 
> If we had any static heuristic that was good at predicting *which* long
chain should be >0.5 probability, we would be using it already. The problem
is that there is no single idea of "fallthrough". We canonicalize the
branches, and even programmers cannot make up their mind. Is the early exit the
fast-path or is the early exit the error handling? Both patterns exist widely.
If we have raw profile information, then yes, this is *much* less of a problem
because we will actually know what direction all of the predictable branches in
the program actually take. But our analyses currently are deceptive in the
absence of strong profile information by giving the impression that the
resulting branches are *unpredictable* instead of being predictable and unknown.
> 
> So, I think this is a cool idea. If I better understood how it worked I
might be less concerned about the complexity. I'm not very convinced that it
is necessary yet.
> 
> Ok, for the necessity, simply take any function from a simple benchmark,
and look at its static block frequencies. Arnold posted a great example from
libquantum. Here we have the most boring code (1 loop in a tiny basic block) and
in a boring benchmark that is completely predictable. Yet still, we think that
the loop is reached less than 20% of the times that the function is called. That
just doesn't make sense.
That makes a fair amount of sense to me, but I see your point that it’s not
particularly useful.

I think there are three sets of facts we’re trying to ascertain from block
frequencies:

(1) Are blocks expected to be executed more or less frequently than their
neighbors? This is effectively a way to summarize the CFG with a single block
frequency number. Consider a global code motion pass. If it is guided by block
frequencies, it doesn't need to know about loops or CFG regions. It always
places instruction outside loops and sinks them below branches and above merges.
Spill placement is similar.

(2) Does a loop iterate enough times to justify loop versioning or unrolling?

(3) Which blocks are "cold"? In other words identity blocks that are
almost never reached and not worth optimizing if it means increasing code size
and possibly compile time.

The proposed change makes (3) easier in case of deep acyclic control flow. It
allows us to more aggressively prune the optimization space.

(2) is not currently problem. But with the new approach we could not simply
estimate trip count as freq(header) / freq(preheader).

Regarding (1). You've probably already considered any impact on block
layout, so I'll ignore that. For code motion/placement, I'm also not
sure about your method of "scaling upward" within acyclic control
flow. I'm used to thinking that if a block has higher weight it must be more
frequently executed. So if I place code on the lightest block, I am either (a)
hoisting it outside a loop or (b) sinking it below branches or above merges.

With a new approach where we scale for a branch bias, if you have a block with
frequency 1.0 terminated by a branch with 0.25/0.75 probability, regardless of
whether it's profiled or subject to static heuristic, I could imagine giving
its successors frequency 0.33 and 1.0. I think what you’re proposing is to give
the successors frequency 0.5 and 1.5. That’s  weird to me because I don’t know
how we would identify loops from the frequency info.

Also, I honestly don’t see how the block frequency analysis will work. It’s not
clear to me how you can compute cyclic probability for the loops if you’ve been
scaling frequencies along the way.

-Andy
> If you want to take a more complex approach, we could definitely add a
confidence dimension to the branch probability. This would allow us to express a
high-confidence un-predictable branch and optimize based on that. However, we
would *still* want block weights to be computed more like I have proposed.
Instead of only branch probabilities that were far outside of 50/50 influencing
it, we would want branch probabilities that were highly confident to influence
it. The practical result would be exactly the same as what I have proposed, it
is just that I'm making the simplifying assumption that at the moment, there
are no evenly distributed branch probabilities which we have high confidence in.
> 
> My suspicion is that we will find even with direct profile information,
this will still be true. Mostly, I suspect there are relatively few cases where
it is important we model an even distribution of branches with high probability.
I'm happy to be wrong about this, but *that* is where I want to wait for
more data to make a decision that adds complexity to the system.

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On Feb 3, 2014, at 12:13 AM, Andrew Trick <atrick at apple.com> wrote:
> On Feb 2, 2014, at 6:55 PM, Chandler Carruth <chandlerc at gmail.com>
wrote:
> 
>> On Sun, Feb 2, 2014 at 6:18 PM, Andrew Trick <atrick at
apple.com> wrote:
>> 
>> On Feb 2, 2014, at 2:13 AM, Chandler Carruth <chandlerc at
gmail.com> wrote:
>> 
>> > Right now, all profile information is funneled through two
analysis passes prior to any part of the optimizer using it.
>> >
>> > First, we have BranchProbabilityInfo, which provides a simple
interface to the simplest form of profile information: local and relative branch
probabilities. These merely express the likelihood of taking one of a mutually
exclusive set of exit paths from a basic block. They are very simple, and the
foundation of the profile information. Even the other analysis is merely built
on top of this one.
>> >
>> > Second we have BlockFrequencyInfo which attempts to provide a more
"global" (function-wide, not actually program wide) view of the
statistical frequency with which any particular basic block is executed. This is
nicely principled analysis that just computes the probabilistic flow of control
through the various branches according to their probabilities established in the
first analysis.
>> >
>> >
>> > However, I think that BlockFrequencyInfo provides the wrong set of
information. There is one critical reason why. Let's take a totally
uninteresting CFG:
>> >
>> > A -> B1, B2
>> > B1 -> C1, C2
>> > B2 -> C3, C4
>> > C1 -> D1, D2
>> > C2 -> ret
>> > C3 -> D3, D4
>> > C4 -> ret
>> > D1 -> E1, E2
>> > D2 -> ret
>> > D3 -> E3, E4
>> > D4 -> ret
>> >
>> > You can imagine this repeating on for as many levels as you like.
This isn't an uncommon situation with real code. BlockFrequencyInfo computes
for this a very logical answer:
>> >
>> > ---- Block Freqs ----
>> >  a = 1.0
>> >   a -> b1 = 0.5
>> >   a -> b2 = 0.5
>> >  b1 = 0.5
>> >   b1 -> c1 = 0.25
>> >   b1 -> c2 = 0.25
>> >  b2 = 0.5
>> >   b2 -> c3 = 0.25
>> >   b2 -> c4 = 0.25
>> >  c1 = 0.25
>> >   c1 -> d1 = 0.125
>> >   c1 -> d2 = 0.125
>> >  c2 = 0.25
>> >  c3 = 0.25
>> >   c3 -> d3 = 0.125
>> >   c3 -> d4 = 0.125
>> >  c4 = 0.25
>> >  d1 = 0.125
>> >   d1 -> e1 = 0.0625
>> >   d1 -> e2 = 0.0625
>> >  d2 = 0.125
>> >  d3 = 0.125
>> >   d3 -> e3 = 0.0625
>> >   d3 -> e4 = 0.0625
>> >  d4 = 0.125
>> >
>> > One way of thinking about this is that for any basic block X which
is predicated by N branches of unbiased probability (50/50), the frequency
computed for that block is 2^(-N).
>> >
>> > The problem is that this doesn't represent anything close to
reality. Processors' branch prediction works precisely because very, *very*
few branches in programs are 50/50. Most programs do not systematically explore
breadth first the full diversity of paths through the program. And yet, in the
absense of better information our heuristics would lead us to believe (and act!)
as though this were true.
>> >
>> > Now, I'm not saying that the computation of block frequencies
is wrong. Merely that it cannot possibly be used for at least one of it purposes
-- it's relative frequency (to the entry block "basis" frequency)
is completely useless for detecting hot or cold regions of a function -- it will
simply claim that all regions of the function are cold. What it *is* useful for
is establishing a total ordering over the basic blocks of a function. So it
works well for some things like code layout, but is grossly misleading for
others.
>> >
>> >
>> > There are several possible solutions here. I'll outline my
proposal as well as some other ideas.
>> >
>> >
>> > BlockWeights instead of BlockFrequencies. My idea is that we
don't really care about the depth of the control dependence for a particular
basic block. We care about the accumulated *bias* toward or away from a basic
block. This is predicated on the idea that branches are overwhelmingly
predictable. As a consequence, evenly distributed probabilities are really just
*uncertainties*.
>> 
>> This sounds like an interesting way to merge unkown branch
probabilities, static heuristics, statistical profiles, and partial profiles.
>> 
>> > My proposed way of implementing this would take the exact
algorithm already used in BlockFrequency, but instead of computing a frequency
for an edge based on the probability of the branch times the frequency of the
predecessor, instead compute it as the frequency of the predecessor times how
"biased" the branch is relative to other branches in the predecessor.
Essentially, this would make a branch probability set of {0.5, 0.5} produce edge
frequencies equal to the predecessor's edge frequency.
>> >
>> > The result of such a system would produce weights for every block
in the above CFG as '1.0', or equivalent to the entry block weight. This
to me is a really useful metric -- it indicates that no block in the CFG is
really more or less likely than any other. Only *biases* in a specific direction
would cause the block weights to go up or down significantly.
>> 
>> I don't like this statement, or don't understand it. It is
useful to know a branch is unbiased. Currently we assume branches are unbiased
then optimize conservatively in those cases (do no harm). But if we had greater
confidence in unbiased branches (because the branch was actually profiled), we
could if-convert much more aggressively.
>> 
>> I think the key is that I'm not talking about changing how we model
branches at all. I agree that it would be useful to model both our confidence in
the relative probabilities and the probabilities themselves for things like
if-conversion. We can still do that.
>> 
>> But the current users of 'block frequency' are not trying to
understand a specific branch as biased or un-biased, or deal with our
confidence. They're just trying to understand how "important" a
specific basic block is to the execution of the function.
> 
> Yes, I understand that. My concern was that passes may assume
frequency/weight is consistent with probability (path weights add up to their
merge/join points). At least, I'm used to making that assumption. Take a
simple CFG:
> 
> A
> |\
> B C
> | |\
> D E F
> | |/
> |/
> G
> 
> If each basic block has cost=1. Path A,B,D,G has weighted cost=4. Path
A,C,E|F,G has weighted cost=5. But they should be equal.
> 
>>  
>> > This would immediately make the analysis useful to consumers such
as the vectorizer, unroller, or when we have the capability, the inliner and an
outliner which respect cold regions of functions.
>> 
>> I'm a little concerned that you're adding complexity that may
not be necessary. Algorithms like if-conversion make use of the fact that the
block weights are additive.
>> 
>> Are you talking about branch probabilities here? I don't believe we
use block frequencies in anything other than:
>> 1) MachineBlockPlacement
>> 2) Spill cost, placement, and generally the register allocator
>> 3) StackSlotColoring (but really only for spill weight computation)
>> 4) LoopVectorizer (disabled currently)
>> 
>> I just want to make sure we're on the same page. I don't think
it really invalidates your concern.
> 
> You're right. I thought early if-conversion was multiplying the cost of
each path by its weight. We aren't doing that. Instead we're taking a
do-no-harm approach of estimating critical path increase without considering
probability. It's actually more consistent with your approach.
> 
>> I'm sure they could be adapted to something more sophisticated, but
these heuristics are notoriously hard to tune. There is value in a simple model.
You say that your approach is simple, but I would have to think hard about
handling block frequencies that are inconsistent with their immediate branch
probabilities.
>> 
>> If they were inconsistent, I would agree. But they are in fact
consistent and additive. They even preserve information during CFG merge points.
The only difference is that they scale (both up and down) relative to the bias
of a branch (probability's delta from the mean) rather than relative to the
flat probability.
>> 
>> So I don't think this makes the computation, propagation or
mathematical model more complex. What it does is shift it from a flow frequency
modeling to biased branch modeling. That is certainly a change and worth
justifying, I just don't want you to imagine it as having more impact than
it does.
>> 
>> So the question is: why do you really need more sophistication?
>> 
>> Part of you problem is that you're looking at frequency relative to
the entry block when you should be looking at frequency relative to the region
being optimized. When deciding to unroll, compare the loop header with the
preheader.
>> 
>> Actually, I did that already. ;] I really thought that would work back
when we were first discussing how to use block frequencies and have not
forgotten it. However, that doesn't actually capture the the frequency
relative to the region. I think an example is best. Let's imagine that in my
CFG above "E1" is a loop preheader. There is some loop header below it
in the CFG that I hadn't reached yet.
>> 
>> The first thing to realize is that the heuristic we want is not how hot
(or cold) the loop header is relative to the preheader. That tells us, once we
execute this loop, how many times are we likely to take the backedge (or for a
nested loop, one of the backedges). But this number may be correctly be
arbitrarily large if we have a loop in a cold region which when executed loops
for a very long time. What we actually want to check for is how frequently we
enter the loop from outside the loop at all.
>> 
>> So the test is how "hot" is the loop preheader relative to
its "region". Now, if the loop preheader is nested inside of another
loop, we might usefully compare the inner loop's preheader to the outer
loop's header and get a useful metric of "hot" or
"cold". But what happens when we are trying to test for this in the
outer most loop? We need some way to define a "region" of the function
body itself then. There are a bunch of viable definitions. The one which maps to
the loop header for a loop nest is the function entry block. However, that is
the precise circumstance that doesn't work -- in a branch program, most
blocks are "cold" relative to the function entry if we simply trust
the block frequency.
>> 
>> Now, there are other ways to define a "region". I mentioned
one as an alternative to my proposal -- we could look at block frequencies only
relative to the root of their domtree. Another would be to use SESE regions, or
any number of others. However, they all have the problem that we must do
substantial work and add substantial complexity and sophistication to compute
the region, the frequency for that region, and then compare ours against it.
>> 
>> As a practical matter, they also have the problem of accumulating
significant error rapidly due to continual division of the entry frequency. My
proposal actually mitigates such problems significantly.
>> 
>> 
>> I think you're trying to avoid unrolling even high trip count loops
that are  seldom reached, and you're using a non-negligible frequency for
"cold" code (e.g. > 2^-10). In that case it sounds like we need a
static heuristic to handle the pattern of chains of branches with no CFG merge.
The fall-thru path should be > 0.5.
>> 
>> I'm really not looking at a single problem or solution. There are a
wide variety of consumers of profile information that want to specially handle
both cold and hot regions of a function. Looking at the frequencies as they
exist today, these transformations cannot conclude either cold or hot for a
region of code from them. We need something better.
>> 
>> If we had any static heuristic that was good at predicting *which* long
chain should be >0.5 probability, we would be using it already. The problem
is that there is no single idea of "fallthrough". We canonicalize the
branches, and even programmers cannot make up their mind. Is the early exit the
fast-path or is the early exit the error handling? Both patterns exist widely.
If we have raw profile information, then yes, this is *much* less of a problem
because we will actually know what direction all of the predictable branches in
the program actually take. But our analyses currently are deceptive in the
absence of strong profile information by giving the impression that the
resulting branches are *unpredictable* instead of being predictable and unknown.
>> 
>> So, I think this is a cool idea. If I better understood how it worked I
might be less concerned about the complexity. I'm not very convinced that it
is necessary yet.
>> 
>> Ok, for the necessity, simply take any function from a simple
benchmark, and look at its static block frequencies. Arnold posted a great
example from libquantum. Here we have the most boring code (1 loop in a tiny
basic block) and in a boring benchmark that is completely predictable. Yet
still, we think that the loop is reached less than 20% of the times that the
function is called. That just doesn't make sense.
> 
> That makes a fair amount of sense to me, but I see your point that it’s not
particularly useful.
> 
> I think there are three sets of facts we’re trying to ascertain from block
frequencies:
> 
> (1) Are blocks expected to be executed more or less frequently than their
neighbors? This is effectively a way to summarize the CFG with a single block
frequency number. Consider a global code motion pass. If it is guided by block
frequencies, it doesn't need to know about loops or CFG regions. It always
places instruction outside loops and sinks them below branches and above merges.
Spill placement is similar.
> 
> (2) Does a loop iterate enough times to justify loop versioning or
unrolling?
> 
> (3) Which blocks are "cold"? In other words identity blocks that
are almost never reached and not worth optimizing if it means increasing code
size and possibly compile time.
> 
> The proposed change makes (3) easier in case of deep acyclic control flow.
It allows us to more aggressively prune the optimization space.
The current code in LoopVectorize.cpp (which Chandler disabled in r200579) falls
into this category (3): is the loop too cold to bother vectorizing?

I agree with Chandler that block frequency is the wrong metric to answer *this*
sort of question.  I haven’t thought enough about block weights to be sure that
it’s the right metric, but it seems much closer.

[I’m using block “frequency” to refer to the current metric, and block “weight”
to refer to Chandler’s proposal.]
> (2) is not currently problem. But with the new approach we could not simply
estimate trip count as freq(header) / freq(preheater).
I agree.  Block frequency seems to be the right metric for (2).
> Regarding (1). You've probably already considered any impact on block
layout, so I'll ignore that. For code motion/placement, I'm also not
sure about your method of "scaling upward" within acyclic control
flow. I'm used to thinking that if a block has higher weight it must be more
frequently executed. So if I place code on the lightest block, I am either (a)
hoisting it outside a loop or (b) sinking it below branches or above merges.
I also think block frequency is more useful for (1).
> With a new approach where we scale for a branch bias, if you have a block
with frequency 1.0 terminated by a branch with 0.25/0.75 probability, regardless
of whether it's profiled or subject to static heuristic, I could imagine
giving its successors frequency 0.33 and 1.0. I think what you’re proposing is
to give the successors frequency 0.5 and 1.5. That’s  weird to me because I
don’t know how we would identify loops from the frequency info.
> 
> Also, I honestly don’t see how the block frequency analysis will work. It’s
not clear to me how you can compute cyclic probability for the loops if you’ve
been scaling frequencies along the way.
Why not compute two metrics, and use them appropriately?

Generating both in parallel also solves the problem of generating cyclic
probabilities.  The loop scale can be calculated from the block frequency
analysis, and then used to scale up block weights.  However, I’m not entirely
sure that block weights *should* be scaled in loops.  If loop scale is already
available through block frequency, block weight may be more useful unscaled.

Block frequency itself is a combination of two separate metrics:  probability
mass and loop scale.  Perhaps even these should be available separately in the
BlockFrequencyInfo API.
> -Andy
> 
>> If you want to take a more complex approach, we could definitely add a
confidence dimension to the branch probability. This would allow us to express a
high-confidence un-predictable branch and optimize based on that. However, we
would *still* want block weights to be computed more like I have proposed.
Instead of only branch probabilities that were far outside of 50/50 influencing
it, we would want branch probabilities that were highly confident to influence
it. The practical result would be exactly the same as what I have proposed, it
is just that I'm making the simplifying assumption that at the moment, there
are no evenly distributed branch probabilities which we have high confidence in.
>> 
>> My suspicion is that we will find even with direct profile information,
this will still be true. Mostly, I suspect there are relatively few cases where
it is important we model an even distribution of branches with high probability.
I'm happy to be wrong about this, but *that* is where I want to wait for
more data to make a decision that adds complexity to the system.