llvm dev - Aug 2020 - [RFC] Introducing convergence control bundles and intrinsics

Hi all,

please see https://reviews.llvm.org/D85603 and its related changes for 
our most recent and hopefully final attempt at putting the `convergent` 
attribute on a solid theoretical foundation in a way that is useful for 
modern GPU compiler use cases. We have clear line of sight to enabling a 
new control flow implementation in the AMDGPU backend which is built on 
this foundation. I have started upstreaming some ground work recently. 
We expect this foundation to also be usable by non-GPU whole-program 
vectorization environments, if they choose to do so.

What follows is a (not quite) brief overview of what this is all about, 
but do see the linked review and its "stack" for a fuller story.


The Problem
-----------
`convergent` was originally introduced in LLVM to label "barrier" 
instructions in GPU programming languages. Those put some unusual 
constraints on program transforms that weren't particularly well 
understood at the time. Since then, GPU languages have introduced 
additional instructions such as wave shuffles or subgroup reductions 
that made the problem harder and exposed some shortcomings of the 
current definition of `convergent`. At the same time, those additional 
instructions helped illuminate what the problem really is.

Briefly, in whole-program vectorization environments such as GPUs, we 
can expose opportunities for performance optimizations to developers by 
allowing data exchange between threads that are grouped together as 
lanes in a vector. As long as control flow is uniform, i.e. all threads 
of the vector follow the same path through the CFG, it is natural that 
all threads assigned to the same vector participate in this data 
exchange. When control flow diverges, some threads may be unavailable 
for the data exchange, but we still need to allow data exchange among 
the threads that _are_ available.

This means that those data exchange operations -- i.e., `convergent` 
operations -- communicate among a set of threads that implicitly depends 
on the position of the operation in control flow.

The problem boils down to defining how the set of communicating threads 
is determined.

This problem inherently has a large target- and environment-specific 
component. Even if control flow is fully uniform, there is the question 
of which threads are grouped together in the first place. In the same 
way that LLVM IR has no built-in understanding of how threads come to be 
in the first place (even in the more well-known multi-threaded CPU 
programming world where convergent operations don't exist), we don't 
concern ourselves at all with these types of questions here. For the 
purpose of target-independent LLVM IR semantics, we simply assume that 
they are answered _somehow_ and instead focus on the part of the problem 
that relates to control flow.

When looking at high-level language source, it is often "intuitively 
obvious" which threads should communicate. This is not the case in an 
unstructured CFG. Here's an example of a non-trivial loop where a 
reduction is used (the result of the reduction is the sum of the input 
values of all participating threads):

   A:
     br label %B

   B:
     ...
     %sum.b = call i32 @subgroupAdd(i32 %v)  ; convergent
     ...
     br i1 %cc1, label %B, label %C

   C:
     ...
     br i1 %cc2, label %B, label %D

   D:
     ; loop exit

Suppose this code is executed by two threads grouped in a (very short) 
vector, and the threads execute the following sequences of basic blocks:

 >  Thread 1: A B B C D
 >  Thread 2: A B C B C D

There are two different intuitive ways of "aligning" the threads for
the
purpose of communication between convergent operations:

 >  Align based on natural loops:
 >    Thread 1: A B - B C D
 >    Thread 2: A B C B C D

 >  Align based on a nested loop interpretation:
 >    Thread 1: A B B C - - D
 >    Thread 2: A B - C B C D

(Explanation of the alignment: In the first alignment, %sum.b is always 
the sum of the input values from both threads. In the second alignment, 
the first computation of %sum.b is collaborative between the two 
threads; the second one in each thread simply returns the input value 
for that thread.)

The example only has a single natural loop, but it could result from a 
high-level language source that has two nested do-while loops, so the 
second interpretation may well be what the programmer intended.

It has often been proposed that the alignment should be defined purely 
based on the IR shown above, such as by always aligning based on natural 
loops. This doesn't work, even if we ignore the possibility of 
irreducible control flow. In the example, we could in principle generate 
IR as follows if the "nested loop alignment" is desired:

   A:
     br label %outer.hdr

   outer.hdr:
     br label %B

   B:
     ...
     %sum.b = call i32 @subgroupAdd(i32 %v)  ; convergent
     ...
     br i1 %cc1, label %B, label %C

   C:
     ...
     br i1 %cc2, label %outer.hdr, label %D

   D:
     ; loop exit

 From a single-threaded perspective, it would be correct to short-cut 
all paths through outer.hdr, but this results in the original IR and 
therefore different behavior for the convergent operation (under such a 
proposal). Worse, the part of the IR which makes the short-cutting 
incorrect -- i.e., the affected convergent operation -- is potentially 
far away from the paths that we're short-cutting. We want to avoid this 
"spooky action at a distance" because it puts an undue burden on
generic
code transforms.


The Solution
------------
We introduce explicit annotations in the IR using "convergence tokens"
that "anchor" a convergent operation with respect to some other point
in
control flow or with respect to the function entry. The details are in 
the linked Phabricator review, so I will simply illustrate here how this 
solves the example above.

To obtain the original natural loop alignment, we augment the example as 
follows:

   A:
     %anchor = call token @llvm.experimental.convergence.anchor()
     br label %B

   B:
     %loop = call token @llvm.experimental.convergence.loop() [ 
"convergencectrl"(token %anchor) ]
     ...
     %sum.b = call i32 @subgroupAdd(i32 %v) [ "convergencectrl"(token 
%loop) ]
     ...
     br i1 %cc1, label %B, label %C

   C:
     ...
     br i1 %cc2, label %B, label %D

   D:
     ; loop exit

To obtain the loop nest alignment, we augment it as follows:

   A:
     %anchor = call token @llvm.experimental.convergence.anchor()
     br label %outer.hdr

   outer.hdr:
     %outer = call token @llvm.experimental.convergence.loop() [ 
"convergencectrl"(token %anchor) ]
     br label %B

   B:
     %inner = call token @llvm.experimental.convergence.loop() [ 
"convergencectrl"(token %outer) ]
     ...
     %sum.b = call i32 @subgroupAdd(i32 %v) [ "convergencectrl"(token 
%inner) ]
     ...
     br i1 %cc1, label %B, label %C

   C:
     ...
     br i1 %cc2, label %outer.hdr, label %D

   D:
     ; loop exit

We end up with two nested natural loops as before, but short-cutting 
through outer.hdr is easily seen as forbidden because of the "loop 
heart" intrinsic along the path through outer.hdr. (We call it a
"heart"
because intuitively that's where loop iterations are counted for the 
purpose of convergent operations, i.e. we count "heartbeats" of the 
loop.) Having the non-removable outer.hdr block may seem like an 
inefficiency, but at least in the AMDGPU backend we end up needing that 
basic block anyway to implement the desired behavior. We don't literally 
count loop iterations in the AMDGPU backend, but we end up doing 
something isomorphic that also requires the block. We suspect that other 
implementations will have similar requirements. In any case, backends 
are free to optimize the block away using target-specific handling.


Have you tried other solutions?
-------------------------------
Yes, we've tried many things over the years. I've personally been 
thinking about and working on this problem on and off for a significant 
amount of time over the last four years. I supervised a practical master 
thesis on a related topic during this time. Other people have thought 
about the problem a lot as well. Discussions with many people in the 
LLVM community, inside AMD, and elsewhere have all helped our 
understanding of the problem. Many different ideas were considered and 
ultimately rejected in the process.

All attempts to solve the problem _without_ some form of "convergence 
token" have suffered from spooky action at a distance. This includes the 
current definition of `convergent` in the LangRef.

We tried _very_ hard to find a workable definition of `convergent` that 
doesn't talk about groups of threads in the LangRef, but ultimately came 
to the conclusion that thinking about convergent operations inherently 
involves threads of execution, and it's much easier to just accept that. 
As I've already written above, convergent operations really are a form 
of (often non- or inaccessible-memory) communication among threads where 
the set of communicating threads is affected by the static position of 
the operation in control flow.

The proposal contains no lock-step execution, even though that's what 
many people think of when they think about GPU programming models. It is 
purely expressed in terms of communicating threads, which could in 
theory just be threads on a CPU. We believe that this is more in line 
with how LLVM works. Besides, we found lock-step execution semantics to 
be undesirable from first principles, because they prevent certain 
desirable program transforms or at least make them much more difficult 
to achieve.

The most recent earlier proposal we brought to the LLVM community is 
here: https://reviews.llvm.org/D68994. That one already used a form of 
convergence token, but we ultimately rejected it for two main reasons. 
First, the convergence control intrinsics described there create more 
"noise" in the IR, i.e. there are more intrinsic calls than with what
we
have now. Second, the particular formalism of dynamic instances used 
there had some undesirable side effects that put undue burden on 
transforms that should be trivial like dead-code elimination.

The current proposal still talks about dynamic instances, but there is 
no longer any automatic propagation of convergence: even if two threads 
execute the same dynamic instances of an instruction, whether they 
execute the same dynamic instance of the very next instruction in 
straight-line code is implementation-defined in general (which means 
that program transforms are free to make changes that could affect this).

We chose to use an operand bundle for the convergence token so that 
generic code can identify the relevant token value easily. We considered 
adding the convergence token as a regular function call argument, but 
this makes it impractical for generic transforms to understand functions 
that take both a convergence token and some other token.

Cheers,
Nicolai
-- 
Lerne, wie die Welt wirklich ist,
Aber vergiss niemals, wie sie sein sollte.

Hi, Nicolai,

Thanks for sending this. What do you think that we should do with the 
existing convergent attribute?

  -Hal

On 8/9/20 10:03 AM, Nicolai Hähnle via llvm-dev wrote:
> Hi all,
>
> please see https://reviews.llvm.org/D85603 and its related changes for 
> our most recent and hopefully final attempt at putting the 
> `convergent` attribute on a solid theoretical foundation in a way that 
> is useful for modern GPU compiler use cases. We have clear line of 
> sight to enabling a new control flow implementation in the AMDGPU 
> backend which is built on this foundation. I have started upstreaming 
> some ground work recently. We expect this foundation to also be usable 
> by non-GPU whole-program vectorization environments, if they choose to 
> do so.
>
> What follows is a (not quite) brief overview of what this is all 
> about, but do see the linked review and its "stack" for a fuller
story.
>
>
> The Problem
> -----------
> `convergent` was originally introduced in LLVM to label "barrier"
> instructions in GPU programming languages. Those put some unusual 
> constraints on program transforms that weren't particularly well 
> understood at the time. Since then, GPU languages have introduced 
> additional instructions such as wave shuffles or subgroup reductions 
> that made the problem harder and exposed some shortcomings of the 
> current definition of `convergent`. At the same time, those additional 
> instructions helped illuminate what the problem really is.
>
> Briefly, in whole-program vectorization environments such as GPUs, we 
> can expose opportunities for performance optimizations to developers 
> by allowing data exchange between threads that are grouped together as 
> lanes in a vector. As long as control flow is uniform, i.e. all 
> threads of the vector follow the same path through the CFG, it is 
> natural that all threads assigned to the same vector participate in 
> this data exchange. When control flow diverges, some threads may be 
> unavailable for the data exchange, but we still need to allow data 
> exchange among the threads that _are_ available.
>
> This means that those data exchange operations -- i.e., `convergent` 
> operations -- communicate among a set of threads that implicitly 
> depends on the position of the operation in control flow.
>
> The problem boils down to defining how the set of communicating 
> threads is determined.
>
> This problem inherently has a large target- and environment-specific 
> component. Even if control flow is fully uniform, there is the 
> question of which threads are grouped together in the first place. In 
> the same way that LLVM IR has no built-in understanding of how threads 
> come to be in the first place (even in the more well-known 
> multi-threaded CPU programming world where convergent operations don't 
> exist), we don't concern ourselves at all with these types of 
> questions here. For the purpose of target-independent LLVM IR 
> semantics, we simply assume that they are answered _somehow_ and 
> instead focus on the part of the problem that relates to control flow.
>
> When looking at high-level language source, it is often "intuitively 
> obvious" which threads should communicate. This is not the case in an 
> unstructured CFG. Here's an example of a non-trivial loop where a 
> reduction is used (the result of the reduction is the sum of the input 
> values of all participating threads):
>
>   A:
>     br label %B
>
>   B:
>     ...
>     %sum.b = call i32 @subgroupAdd(i32 %v)  ; convergent
>     ...
>     br i1 %cc1, label %B, label %C
>
>   C:
>     ...
>     br i1 %cc2, label %B, label %D
>
>   D:
>     ; loop exit
>
> Suppose this code is executed by two threads grouped in a (very short) 
> vector, and the threads execute the following sequences of basic blocks:
>
> >  Thread 1: A B B C D
> >  Thread 2: A B C B C D
>
> There are two different intuitive ways of "aligning" the threads
for
> the purpose of communication between convergent operations:
>
> >  Align based on natural loops:
> >    Thread 1: A B - B C D
> >    Thread 2: A B C B C D
>
> >  Align based on a nested loop interpretation:
> >    Thread 1: A B B C - - D
> >    Thread 2: A B - C B C D
>
> (Explanation of the alignment: In the first alignment, %sum.b is 
> always the sum of the input values from both threads. In the second 
> alignment, the first computation of %sum.b is collaborative between 
> the two threads; the second one in each thread simply returns the 
> input value for that thread.)
>
> The example only has a single natural loop, but it could result from a 
> high-level language source that has two nested do-while loops, so the 
> second interpretation may well be what the programmer intended.
>
> It has often been proposed that the alignment should be defined purely 
> based on the IR shown above, such as by always aligning based on 
> natural loops. This doesn't work, even if we ignore the possibility of 
> irreducible control flow. In the example, we could in principle 
> generate IR as follows if the "nested loop alignment" is desired:
>
>   A:
>     br label %outer.hdr
>
>   outer.hdr:
>     br label %B
>
>   B:
>     ...
>     %sum.b = call i32 @subgroupAdd(i32 %v)  ; convergent
>     ...
>     br i1 %cc1, label %B, label %C
>
>   C:
>     ...
>     br i1 %cc2, label %outer.hdr, label %D
>
>   D:
>     ; loop exit
>
> From a single-threaded perspective, it would be correct to short-cut 
> all paths through outer.hdr, but this results in the original IR and 
> therefore different behavior for the convergent operation (under such 
> a proposal). Worse, the part of the IR which makes the short-cutting 
> incorrect -- i.e., the affected convergent operation -- is potentially 
> far away from the paths that we're short-cutting. We want to avoid 
> this "spooky action at a distance" because it puts an undue
burden on
> generic code transforms.
>
>
> The Solution
> ------------
> We introduce explicit annotations in the IR using "convergence
tokens"
> that "anchor" a convergent operation with respect to some other
point
> in control flow or with respect to the function entry. The details are 
> in the linked Phabricator review, so I will simply illustrate here how 
> this solves the example above.
>
> To obtain the original natural loop alignment, we augment the example 
> as follows:
>
>   A:
>     %anchor = call token @llvm.experimental.convergence.anchor()
>     br label %B
>
>   B:
>     %loop = call token @llvm.experimental.convergence.loop() [ 
> "convergencectrl"(token %anchor) ]
>     ...
>     %sum.b = call i32 @subgroupAdd(i32 %v) [
"convergencectrl"(token
> %loop) ]
>     ...
>     br i1 %cc1, label %B, label %C
>
>   C:
>     ...
>     br i1 %cc2, label %B, label %D
>
>   D:
>     ; loop exit
>
> To obtain the loop nest alignment, we augment it as follows:
>
>   A:
>     %anchor = call token @llvm.experimental.convergence.anchor()
>     br label %outer.hdr
>
>   outer.hdr:
>     %outer = call token @llvm.experimental.convergence.loop() [ 
> "convergencectrl"(token %anchor) ]
>     br label %B
>
>   B:
>     %inner = call token @llvm.experimental.convergence.loop() [ 
> "convergencectrl"(token %outer) ]
>     ...
>     %sum.b = call i32 @subgroupAdd(i32 %v) [
"convergencectrl"(token
> %inner) ]
>     ...
>     br i1 %cc1, label %B, label %C
>
>   C:
>     ...
>     br i1 %cc2, label %outer.hdr, label %D
>
>   D:
>     ; loop exit
>
> We end up with two nested natural loops as before, but short-cutting 
> through outer.hdr is easily seen as forbidden because of the "loop 
> heart" intrinsic along the path through outer.hdr. (We call it a 
> "heart" because intuitively that's where loop iterations are
counted
> for the purpose of convergent operations, i.e. we count
"heartbeats"
> of the loop.) Having the non-removable outer.hdr block may seem like 
> an inefficiency, but at least in the AMDGPU backend we end up needing 
> that basic block anyway to implement the desired behavior. We don't 
> literally count loop iterations in the AMDGPU backend, but we end up 
> doing something isomorphic that also requires the block. We suspect 
> that other implementations will have similar requirements. In any 
> case, backends are free to optimize the block away using 
> target-specific handling.
>
>
> Have you tried other solutions?
> -------------------------------
> Yes, we've tried many things over the years. I've personally been 
> thinking about and working on this problem on and off for a 
> significant amount of time over the last four years. I supervised a 
> practical master thesis on a related topic during this time. Other 
> people have thought about the problem a lot as well. Discussions with 
> many people in the LLVM community, inside AMD, and elsewhere have all 
> helped our understanding of the problem. Many different ideas were 
> considered and ultimately rejected in the process.
>
> All attempts to solve the problem _without_ some form of "convergence 
> token" have suffered from spooky action at a distance. This includes 
> the current definition of `convergent` in the LangRef.
>
> We tried _very_ hard to find a workable definition of `convergent` 
> that doesn't talk about groups of threads in the LangRef, but 
> ultimately came to the conclusion that thinking about convergent 
> operations inherently involves threads of execution, and it's much 
> easier to just accept that. As I've already written above, convergent 
> operations really are a form of (often non- or inaccessible-memory) 
> communication among threads where the set of communicating threads is 
> affected by the static position of the operation in control flow.
>
> The proposal contains no lock-step execution, even though that's what 
> many people think of when they think about GPU programming models. It 
> is purely expressed in terms of communicating threads, which could in 
> theory just be threads on a CPU. We believe that this is more in line 
> with how LLVM works. Besides, we found lock-step execution semantics 
> to be undesirable from first principles, because they prevent certain 
> desirable program transforms or at least make them much more difficult 
> to achieve.
>
> The most recent earlier proposal we brought to the LLVM community is 
> here: https://reviews.llvm.org/D68994. That one already used a form of 
> convergence token, but we ultimately rejected it for two main reasons. 
> First, the convergence control intrinsics described there create more 
> "noise" in the IR, i.e. there are more intrinsic calls than with
what
> we have now. Second, the particular formalism of dynamic instances 
> used there had some undesirable side effects that put undue burden on 
> transforms that should be trivial like dead-code elimination.
>
> The current proposal still talks about dynamic instances, but there is 
> no longer any automatic propagation of convergence: even if two 
> threads execute the same dynamic instances of an instruction, whether 
> they execute the same dynamic instance of the very next instruction in 
> straight-line code is implementation-defined in general (which means 
> that program transforms are free to make changes that could affect this).
>
> We chose to use an operand bundle for the convergence token so that 
> generic code can identify the relevant token value easily. We 
> considered adding the convergence token as a regular function call 
> argument, but this makes it impractical for generic transforms to 
> understand functions that take both a convergence token and some other 
> token.
>
> Cheers,
> Nicolai
-- 
Hal Finkel
Lead, Compiler Technology and Programming Languages
Leadership Computing Facility
Argonne National Laboratory

Hi Hal,

On Mon, Aug 17, 2020 at 2:13 AM Hal Finkel <hfinkel at anl.gov>
wrote:
> Thanks for sending this. What do you think that we should do with the
> existing convergent attribute?
My preference, which is implicitly expressed in the review, is to use
`convergent` both for the new and the old thing. They are implicitly
distinguished via the "convergencectrl" operand bundle.

The main reason for going down that route is that `convergent` is
really an odd duck in that it is the only attribute in LLVM that
_adds_ new constraints on program transforms. All other attributes
_remove_ constraints on program transforms by expressing constraints
on what the function does (e.g. readnone, nosync, willreturn, ...).
Matt tried to push for changing that, making the "convergent"
restrictions the default and adding `noconvergent` on most things, but
that effort got stuck. In the meantime, let's avoid adding yet another
odd duck like this :)

Cheers,
Nicolai

>
>   -Hal
>
> On 8/9/20 10:03 AM, Nicolai Hähnle via llvm-dev wrote:
> > Hi all,
> >
> > please see https://reviews.llvm.org/D85603 and its related changes for
> > our most recent and hopefully final attempt at putting the
> > `convergent` attribute on a solid theoretical foundation in a way that
> > is useful for modern GPU compiler use cases. We have clear line of
> > sight to enabling a new control flow implementation in the AMDGPU
> > backend which is built on this foundation. I have started upstreaming
> > some ground work recently. We expect this foundation to also be usable
> > by non-GPU whole-program vectorization environments, if they choose to
> > do so.
> >
> > What follows is a (not quite) brief overview of what this is all
> > about, but do see the linked review and its "stack" for a
fuller story.
> >
> >
> > The Problem
> > -----------
> > `convergent` was originally introduced in LLVM to label
"barrier"
> > instructions in GPU programming languages. Those put some unusual
> > constraints on program transforms that weren't particularly well
> > understood at the time. Since then, GPU languages have introduced
> > additional instructions such as wave shuffles or subgroup reductions
> > that made the problem harder and exposed some shortcomings of the
> > current definition of `convergent`. At the same time, those additional
> > instructions helped illuminate what the problem really is.
> >
> > Briefly, in whole-program vectorization environments such as GPUs, we
> > can expose opportunities for performance optimizations to developers
> > by allowing data exchange between threads that are grouped together as
> > lanes in a vector. As long as control flow is uniform, i.e. all
> > threads of the vector follow the same path through the CFG, it is
> > natural that all threads assigned to the same vector participate in
> > this data exchange. When control flow diverges, some threads may be
> > unavailable for the data exchange, but we still need to allow data
> > exchange among the threads that _are_ available.
> >
> > This means that those data exchange operations -- i.e., `convergent`
> > operations -- communicate among a set of threads that implicitly
> > depends on the position of the operation in control flow.
> >
> > The problem boils down to defining how the set of communicating
> > threads is determined.
> >
> > This problem inherently has a large target- and environment-specific
> > component. Even if control flow is fully uniform, there is the
> > question of which threads are grouped together in the first place. In
> > the same way that LLVM IR has no built-in understanding of how threads
> > come to be in the first place (even in the more well-known
> > multi-threaded CPU programming world where convergent operations
don't
> > exist), we don't concern ourselves at all with these types of
> > questions here. For the purpose of target-independent LLVM IR
> > semantics, we simply assume that they are answered _somehow_ and
> > instead focus on the part of the problem that relates to control flow.
> >
> > When looking at high-level language source, it is often
"intuitively
> > obvious" which threads should communicate. This is not the case
in an
> > unstructured CFG. Here's an example of a non-trivial loop where a
> > reduction is used (the result of the reduction is the sum of the input
> > values of all participating threads):
> >
> >   A:
> >     br label %B
> >
> >   B:
> >     ...
> >     %sum.b = call i32 @subgroupAdd(i32 %v)  ; convergent
> >     ...
> >     br i1 %cc1, label %B, label %C
> >
> >   C:
> >     ...
> >     br i1 %cc2, label %B, label %D
> >
> >   D:
> >     ; loop exit
> >
> > Suppose this code is executed by two threads grouped in a (very short)
> > vector, and the threads execute the following sequences of basic
blocks:
> >
> > >  Thread 1: A B B C D
> > >  Thread 2: A B C B C D
> >
> > There are two different intuitive ways of "aligning" the
threads for
> > the purpose of communication between convergent operations:
> >
> > >  Align based on natural loops:
> > >    Thread 1: A B - B C D
> > >    Thread 2: A B C B C D
> >
> > >  Align based on a nested loop interpretation:
> > >    Thread 1: A B B C - - D
> > >    Thread 2: A B - C B C D
> >
> > (Explanation of the alignment: In the first alignment, %sum.b is
> > always the sum of the input values from both threads. In the second
> > alignment, the first computation of %sum.b is collaborative between
> > the two threads; the second one in each thread simply returns the
> > input value for that thread.)
> >
> > The example only has a single natural loop, but it could result from a
> > high-level language source that has two nested do-while loops, so the
> > second interpretation may well be what the programmer intended.
> >
> > It has often been proposed that the alignment should be defined purely
> > based on the IR shown above, such as by always aligning based on
> > natural loops. This doesn't work, even if we ignore the
possibility of
> > irreducible control flow. In the example, we could in principle
> > generate IR as follows if the "nested loop alignment" is
desired:
> >
> >   A:
> >     br label %outer.hdr
> >
> >   outer.hdr:
> >     br label %B
> >
> >   B:
> >     ...
> >     %sum.b = call i32 @subgroupAdd(i32 %v)  ; convergent
> >     ...
> >     br i1 %cc1, label %B, label %C
> >
> >   C:
> >     ...
> >     br i1 %cc2, label %outer.hdr, label %D
> >
> >   D:
> >     ; loop exit
> >
> > From a single-threaded perspective, it would be correct to short-cut
> > all paths through outer.hdr, but this results in the original IR and
> > therefore different behavior for the convergent operation (under such
> > a proposal). Worse, the part of the IR which makes the short-cutting
> > incorrect -- i.e., the affected convergent operation -- is potentially
> > far away from the paths that we're short-cutting. We want to avoid
> > this "spooky action at a distance" because it puts an undue
burden on
> > generic code transforms.
> >
> >
> > The Solution
> > ------------
> > We introduce explicit annotations in the IR using "convergence
tokens"
> > that "anchor" a convergent operation with respect to some
other point
> > in control flow or with respect to the function entry. The details are
> > in the linked Phabricator review, so I will simply illustrate here how
> > this solves the example above.
> >
> > To obtain the original natural loop alignment, we augment the example
> > as follows:
> >
> >   A:
> >     %anchor = call token @llvm.experimental.convergence.anchor()
> >     br label %B
> >
> >   B:
> >     %loop = call token @llvm.experimental.convergence.loop() [
> > "convergencectrl"(token %anchor) ]
> >     ...
> >     %sum.b = call i32 @subgroupAdd(i32 %v) [
"convergencectrl"(token
> > %loop) ]
> >     ...
> >     br i1 %cc1, label %B, label %C
> >
> >   C:
> >     ...
> >     br i1 %cc2, label %B, label %D
> >
> >   D:
> >     ; loop exit
> >
> > To obtain the loop nest alignment, we augment it as follows:
> >
> >   A:
> >     %anchor = call token @llvm.experimental.convergence.anchor()
> >     br label %outer.hdr
> >
> >   outer.hdr:
> >     %outer = call token @llvm.experimental.convergence.loop() [
> > "convergencectrl"(token %anchor) ]
> >     br label %B
> >
> >   B:
> >     %inner = call token @llvm.experimental.convergence.loop() [
> > "convergencectrl"(token %outer) ]
> >     ...
> >     %sum.b = call i32 @subgroupAdd(i32 %v) [
"convergencectrl"(token
> > %inner) ]
> >     ...
> >     br i1 %cc1, label %B, label %C
> >
> >   C:
> >     ...
> >     br i1 %cc2, label %outer.hdr, label %D
> >
> >   D:
> >     ; loop exit
> >
> > We end up with two nested natural loops as before, but short-cutting
> > through outer.hdr is easily seen as forbidden because of the
"loop
> > heart" intrinsic along the path through outer.hdr. (We call it a
> > "heart" because intuitively that's where loop iterations
are counted
> > for the purpose of convergent operations, i.e. we count
"heartbeats"
> > of the loop.) Having the non-removable outer.hdr block may seem like
> > an inefficiency, but at least in the AMDGPU backend we end up needing
> > that basic block anyway to implement the desired behavior. We
don't
> > literally count loop iterations in the AMDGPU backend, but we end up
> > doing something isomorphic that also requires the block. We suspect
> > that other implementations will have similar requirements. In any
> > case, backends are free to optimize the block away using
> > target-specific handling.
> >
> >
> > Have you tried other solutions?
> > -------------------------------
> > Yes, we've tried many things over the years. I've personally
been
> > thinking about and working on this problem on and off for a
> > significant amount of time over the last four years. I supervised a
> > practical master thesis on a related topic during this time. Other
> > people have thought about the problem a lot as well. Discussions with
> > many people in the LLVM community, inside AMD, and elsewhere have all
> > helped our understanding of the problem. Many different ideas were
> > considered and ultimately rejected in the process.
> >
> > All attempts to solve the problem _without_ some form of
"convergence
> > token" have suffered from spooky action at a distance. This
includes
> > the current definition of `convergent` in the LangRef.
> >
> > We tried _very_ hard to find a workable definition of `convergent`
> > that doesn't talk about groups of threads in the LangRef, but
> > ultimately came to the conclusion that thinking about convergent
> > operations inherently involves threads of execution, and it's much
> > easier to just accept that. As I've already written above,
convergent
> > operations really are a form of (often non- or inaccessible-memory)
> > communication among threads where the set of communicating threads is
> > affected by the static position of the operation in control flow.
> >
> > The proposal contains no lock-step execution, even though that's
what
> > many people think of when they think about GPU programming models. It
> > is purely expressed in terms of communicating threads, which could in
> > theory just be threads on a CPU. We believe that this is more in line
> > with how LLVM works. Besides, we found lock-step execution semantics
> > to be undesirable from first principles, because they prevent certain
> > desirable program transforms or at least make them much more difficult
> > to achieve.
> >
> > The most recent earlier proposal we brought to the LLVM community is
> > here: https://reviews.llvm.org/D68994. That one already used a form of
> > convergence token, but we ultimately rejected it for two main reasons.
> > First, the convergence control intrinsics described there create more
> > "noise" in the IR, i.e. there are more intrinsic calls than
with what
> > we have now. Second, the particular formalism of dynamic instances
> > used there had some undesirable side effects that put undue burden on
> > transforms that should be trivial like dead-code elimination.
> >
> > The current proposal still talks about dynamic instances, but there is
> > no longer any automatic propagation of convergence: even if two
> > threads execute the same dynamic instances of an instruction, whether
> > they execute the same dynamic instance of the very next instruction in
> > straight-line code is implementation-defined in general (which means
> > that program transforms are free to make changes that could affect
this).
> >
> > We chose to use an operand bundle for the convergence token so that
> > generic code can identify the relevant token value easily. We
> > considered adding the convergence token as a regular function call
> > argument, but this makes it impractical for generic transforms to
> > understand functions that take both a convergence token and some other
> > token.
> >
> > Cheers,
> > Nicolai
>
> --
> Hal Finkel
> Lead, Compiler Technology and Programming Languages
> Leadership Computing Facility
> Argonne National Laboratory
>

-- 
Lerne, wie die Welt wirklich ist,
aber vergiss niemals, wie sie sein sollte.