Document Number: P2588R0
Date: 2022-05-13
Reply to: Gonzalo Brito Gadeschi <gonzalob _at_>
Authors: Gonzalo Brito Gadeschi, Eric Niebler, Anthony Williams, Thomas Rodgers
Audience: LEWG, Concurrency

barrier’s phase completion guarantees


Unintended consequences of std::barrier’s specification constrain implementations to run the CompletionFunction on the last thread that arrives at the barrier during the phase. This prevents std::barrier from benefiting from hardware acceleration for thread synchronization. Removing these constraints is a breaking change. This paper aims to find a sweet spot for the barrier specification that delivers the functionality that applications need while allowing efficient implementations.


The specification of std::barrier<CompletionFunction> requires the phase completion step to run when the expected count becomes zero on one of the threads that arrived at the barrier during the phase thread.barrier.class-1.2:

When the expected count reaches zero, the phase completion step is run. For the specialization with the default value of the CompletionFunction template parameter, the completion step is run as part of the call to arrive or arrive_and_drop that caused the expected count to reach zero. For other specializations, the completion step is run on one of the threads that arrived at the barrier during the phase.

The specification of std::barrier does not require any thread that arrives at the barrier to call std::barrier::wait, but calling barrier::wait is necessary to observe phase completion thread.barrier.class-3.sentence-3:

[…] the behavior is undefined if any of the barrier object’s member functions other than wait are called while the completion step is in progress.

A thread that never calls wait can still arrive at the barrier again via synchronization through some other thread that does call wait. This is very useful in practice, as the following example shows:

Example 0: a producer / consumer pipeline (godbolt)

std::barrier<CF0> b0(2, cf0); std::barrier<CF1> b1(2, cf1); void thread_0() { while(true) { produce_data(); // A b0.arrive(); // B: signal data produced b1.arrive_and_wait(); // C: wait on data consumed } } void thread_1() { while(true) { b0.arrive_and_wait(); // D: wait on data produced consume_data(); // E b1.arrive_and_wait(); // F: signal data consumed } }

In this example, thread_0 at “A” produces some data, and then at “B” signals thread_1 that the data is ready. thread_0 will never wait on barrier b0. Then thread_0 waits on thread_1 consuming the data, and proceeds to generate new data.

The [[nodiscard]] attribute on arrive shows that barrier designers were not expecting threads to arrive at the barrier without calling wait.

The current standard wording in thread.barrier.class-1.2.sentence-3:

For other specializations, the completion step is run on one of the threads that arrived at the barrier during the phase.

aims to provide implementations with enough freedom to run the CompletionFunction on any thread that participates in the barrier during the phase.

Unfortunately, the current wording requires all implementations to run the CompletionFunction as part of the call to arrive performed by the last thread that arrives at the barrier during the phase, as the following example shows:

Example 1: Guarantee that CompletionFunction runs if no thread waits (godbolt)

std::barrier<CF> b{2, cf}; using tok_t = decltype(b.arrive()); void thread() { new tok_t(b.arrive()); // A: arrive and leak token } // B: thread exit auto t0 = std::thread(thread); // C: Spawn two threads auto t1 = std::thread(thread); t0.join(); // D: Join them t1.join(); // E: Standard guarantees that CompletionFunction did run

This example spawns two threads at “C”, both of which execute the same sequence of operations:

After joining both threads at “D”, they no longer exist.

The standard guarantees in thread.barrier.class-1.2.sentence-1 that the completion function runs:

When the expected count reaches zero, the phase completion step is run.

in one of the threads that arrived at the barrier during the phase (thread.barrier.class-1.2.sentence-3). At “E”, these threads do not exist anymore. Therefore, the CompletionFunction must have run before “E”, and more precisely, it must have run before the last thread that arrived at the barrier during the phase exits.

That is: there is only one place in which it makes sense for conforming standard library implementations to run the phase completion step: as part of the arrive performed by the last thread that arrives at the barrier during the phase.

This is an unintended consequence of the interaction between:

These consequences are problematic in practice, where Amhdahl’s law limits the scalability of massively parallel applications with small “serial” overheads on modern NUMA architectures with millions of hardware threads. While std::barrier’s split arrive/wait APIs enable threads to hide the cost of synchronization behind independent work, the amount of independent computation available in real applications is limited. Clauses like thread.barrier.class-1.2.sentence-3 aim to enable std::barrier to use hardware accelerators for synchronization, such as those available in NVIDIA GPUs, to allow applications to hide the cost of synchronization behind small amounts of independent work, but due to the unintended consequences explained above, implementations currently cannot do so.

Fixing this requires a breaking change. There are clear engineering trade-offs between varying degrees of guarantees which determine the set of well-formed programs with different degrees of implementation flexibility which determine performance.

This paper aims to to help the reader answer the question: What is the sweet spot for std::barrier semantics, and what are the consequences of the breaking changes required to get there?

The following section analyzes the semantics of the current barrier specification and the different changes that we could make to balance functionality and performance. Then, we evaluate changes according to the functionality provided, their ability to leverage hardware acceleration, and the impact of a change on both existing and potential standard library implementations and end-user applications.

Finally, the authors suggest changes that balance functionality and performance to deliver the functionality that applications need in practice while allowing efficient implementations and propose wording for these changes.

Analysis of barrier semantics

The “core” semantics of the std::barrier phase completion step are:

  1. The last arrival happens-before phase completion, which happens-before any thread unblocks from wait.
  2. Establish cumulativity from all threads that arrive at the barrier during the phase to all threads unblocked from wait through the thread that runs the CompletionFunction.

These “core” semantics enable applications to, e.g., perform a reduction in a critical-section in-between arriving and waiting:

Example 2: reduction in critical section (godbolt)

std::vector<int> data(nthreads); int reduction; auto reduce = [&] { reduction = std::accumulate(data.begin(), data.end(), 0); }; std::barrier<decltype(reduce)> b(nthreads, reduce); void thread(size_t i) { data[i] = produce_data(); // A b.arrive_and_wait(); // B consume(reduction); // C }

Here, threads produce some data at “A” and arrive and wait at the barrier at “B”. reduce is then called inside a critical section, after the last thread arrives, and before any thread is unblocked from the wait. Since all threads participating in the barrier are “stalled” at the wait, reduce can access data safely without data-races. Finally, reduce happens-before any thread is unblocked from the wait. That is, all uses of reduction at C observe the value that reduce initialized it with safely and without data-races.

The main design choices that this paper concerns itself with are “when” and “where” should the C++ standard guarantee that the CompletionFunction runs. These two properties, “where” and “when”, are intertwined. The following Table 1 explores some of the main options, the choices they enable, and their impact on hardware acceleration, existing implementations, and user applications.

Table 1: Design tradeoffs: “When does the CompletionFunction run?”, “Where is it allowed to run (on which threads)?”, “Does the CompletionFunction run if no thread calls wait?”, “What are the hardware acceleration opportunities of these constraints?”, “What’s the impact on implementations and users?”

When Where Runs if no thread waits? Hardware acceleration opportunities Implementation impact User impact
Last arrive Last thread to arrive Yes Very low None None
Any arrive Any thread that arrives Yes Very low None None
Any wait Any thread that calls wait No Low All None for any application surveyed, minimal otherwise.
Any arrive or wait A thread that arrives or waits Options:
“Implementation defined”
Medium If answer to “Runs if no thread waits?” is No, None. Otherwise, All. None for any application surveyed, minimal otherwise.
After last arrive before any thread unblocks from wait Options:
“A thread that arrives or waits”
“A new thread”
“A thread that arrives or waits or a new thread”
“Implementation defined”
High If answer to “Runs if no thread waits?” is No, None. Otherwise, All. None for any application surveyed, minimal otherwise.

Restricting the choice of “when” significantly constrains the threads in which it makes sense for implementations to run the CompletionFunction. For example, restricting “when” to particular API calls, including the broad “Any arrive or wait”, restricts implementations to only run the phase completion step within those API calls (on threads making these API calls).

An important question impacting Example 1 is whether the CompletionFunction runs if no thread waits. The third column in Table 1 answers this question for the options considered. For the last two rows in the table, the standard is free to define these semantics as it wishes. Some options are provided in the table inline, and vary from well-defined semantics (“Yes” or “No”), to “implementation-defined” or “unspecified”.

On hardware acceleration

The column on “Hardware acceleration opportunities” describes how effective “hardware barrier synchronization accelerations” can be at accelerating synchronization via std::barrier. Hardware accelerators that have more freedom to pick which thread completes the barrier phase (up to the freedom of creating a new thread to do so) can be significantly more effective than those that cannot make this choice. As the number of hardware threads available on computer hardware increases, the benefits of these accelerators outweighs their costs. NVIDIA GPUs have been shipping barrier accelerators for many years. Being descriptive and not prescriptive enables hardware vendors to innovate.

Impact on existing implementations

If the standard allows the CompletionFunction to run even if no thread ever waits, no mainstream standard library implementation (libc++, libstdc++, and MSVC STL) needs to change. Otherwise, these standard library implementations would need to change.

For the last two rows, selecting “implementation-defined” behavior would require the existing implementations to document their behavior.

Largest possible semantic change

To evaluate the impact of breaking changes, this section defines the “largest” possible semantic change we could make. It allows CompletionFunction to run:

This change breaks three std::barrier guarantees:

Potential impact on implementations

Thomas Rodgers mentioned that libdispatch barriers potentially complete on a different thread in the thread pool: all the work in the queue before the enqueuing of the barrier work item completes before the barrier work item, whose completion may happen on some other thread. The largest change would enable such an implementation.

NVIDIA’s barrier accelerators can run the phase completion step very close to the memory where the barrier resides. The largest change enables such an implementation.

Impact on existing applications

We surveyed all public uses of std::barrier on GitHub as well as within some NVIDIA’s code bases that use them heavily.

The impact of all changes considered - including the largest change - on all existing applications we surveyed was non-existent. The feature is a new C++20 feature that’s been only available since:

and it is a relatively niche and sharp tool.

All applications we discovered were either educational or small toy programs for learning purposes. They all had one thread that both arrived and waited at the barrier during all barrier phases. None of them relied on a particular thread running the CompletionFunction (we explore the uses we can imagine in the next section).

Potential impact on applications

While the authors could not find any application in the wild that would break due to the largest change, together with the experts we polled, we can imagine some.

Portability concerns

Anthony Williams raised the following concern: if most standard library implementations run the phase completion step as part of the last thread that arrives at the barrier during the phase, applications might end up silently and accidentally relying on this guarantee. Therefore, applications will not be portable to implementations that do something else.

Count-down with effect

P2300 when_all algorithm accepts a variable number of asynchronous tasks and executes a continuation when they all complete. Given std::barrier as specified today, one valid implementation of when_all simply arrives at the barrier from all asynchronous tasks. After the last task arrives, the continuation runs. No thread calls wait, and the barrier is never used again.

The largest change would silently break the semantics of such a when_all implementation, causing the application to hang on implementations that do not run the phase completion step as part of the last thread that arrives.

This implementation of when_all would be using std::barrier as a “single-use count-down with effect”: a counter initialized to the number of asynchronous tasks that each task decrements by 1. When the counter reaches 0, it runs some function for its effect.

Since the counter is “single-use”, it would make more sense for when_all to use std::latch here. However, when_all cannot do so because std::latch does not support CompletionFunctions. A way to enable this use case would be to add support for CompletionFunctions to std::latch. Furthermore, since std::latch is single-use, the semantics that make sense for std::barrier do not necessarily make sense for std::latch. This is worth exploring.

Barriers and latches are synchronization primitives intended to synchronize groups of threads with each other. However, this is not what the when_all example needs. The when_all example synchronizes a set of threads with the thread that runs some effect; it does not synchronize the threads themselves with each other. So another alternative that might be worth exploring is to provide such synchronization primitives in the standard library.

Thread id

Anthony Williams mentioned that they commonly see applications in which a handle, like a database handle, is stored in thread_local storage (TLS). Such an application could use the knowledge that the CompletionFunction is only executed by a thread that arrived at the barrier during the phase to just access this handle from the CompletionFunction.

Thomas Rodgers recognized that such an application could, in some cases, store a shared_ptr<Handle*> in the CompletionFunction itself and access the database handle through it. However, if the database handle is tied to the thread that created it, this approach would not work. He also recognized that on the libdispatch barrier model, such an application could not depend on TLS either.

This is an example of a larger class of applications that rely on the guarantee that one of the threads that arrived at the barrier during the phase runs the CompletionFunction. Thread locals are one example; inspecting the thread id and doing something with it would be another.

Any change that relaxes the guarantees about “where” (on which thread) the CompletionFunction may run will be turning some of these use cases from “well-formed” programs into non-portable or illegal programs. We can imagine these applications but have not found them in the wild.


Our suggestion is to pursue some flavor of the largest semantic change, restricting ourselves to the options that do not impact any existing standard library implementation or application we surveyed in the wild. While we can imagine theoretical applications that would be impacted by any option we take, it is very hard to write applications that do so correctly. Given the novelty and niche of this feature, we expect the impact on such applications to be minimal to non-existent. All experts we polled from the domains of applications using std::barrier, standard library implementors, and designers of std::barrier were surprised by the current semantics. These semantics were not intended. The intention was to enable std::barrier to leverage hardware acceleration. If feasible, our recommendation is to backport this change to C++20.

That is, our suggestion is to guarantee that:

Running the phase completion step on an “unspecified” thread includes existing unrelated threads, with code injected at any point.

Suggested wording

The proposed change modifies thread.barrier.class-1 as follows:

Each barrier phase consists of the following steps:

Optional changes

Remove [[nodiscard]] attribute from barrier::arrive

As Example 0 shows, discarding the return value of barrier::arrive is fine and useful. We should consider removing [[nodiscard]].

Deprecate the const overload of wait and add non-const overload

Since wait can complete the barrier phase, we might want to consider deprecating the const-overload and adding a non-const overload.

This would require changing all implementations. To support the deprecated const overload, any implementation that wants to modify the barrier internal state during wait needs to use mutable anyways.


Everyone that helped, in particular, Olivier Giroux, Eric Niebler, David Olsen, Anthony Williams, and Thomas Rodgers.