atomic_queue

atomic_queue

C++14 multiple-producer-multiple-consumer lock-free queues based on circular buffer and std::atomic. Designed with a goal to minimize the latency between one thread pushing an element into a queue and another thread popping it from the queue.

It has been developed, tested and benchmarked on Linux, but should support any C++14 platforms which implement std::atomic. Reported as compatible with Windows, but the continuous integrations hosted by GitHub are currently set up only for x86_64 platform on Ubuntu-20.04 and Ubuntu-22.04. Pull requests to extend the continuous integrations to run on other architectures and/or platforms are welcome.

Design Principles

When minimizing latency a good design is not when there is nothing left to add, but rather when there is nothing left to remove, as these queues exemplify.

The main design principle these queues follow is minimalism, which results in such design choices as:

• Bare minimum of atomic instructions. Inlinable by default push and pop functions can hardly be any cheaper in terms of CPU instruction number / L1i cache pressure.
• Explicit contention/false-sharing avoidance for queue and its elements.
• Linear fixed size ring-buffer array. No heap memory allocations after a queue object has constructed. It doesn’t get any more CPU L1d or TLB cache friendly than that.
• Value semantics. Meaning that the queues make a copy/move upon push/pop, no reference/pointer to elements in the queue can be obtained.

The impact of each of these small design choices on their own is barely measurable, but their total impact is much greater than a simple sum of the constituents’ impacts, aka super-scalar compounding or synergy. The synergy emerging from combining multiple of these small design choices together is what allows CPUs to perform at their peak capacities least impeded.

These design choices are also limitations:

• The maximum queue size must be set at compile time or construction time. The circular buffer side-steps the memory reclamation problem inherent in linked-list based queues for the price of fixed buffer size. See Effective memory reclamation for lock-free data structures in C++ for more details. Fixed buffer size may not be that much of a limitation, since once the queue gets larger than the maximum expected size that indicates a problem that elements aren’t consumed fast enough, and if the queue keeps growing it may eventually consume all available memory which may affect the entire system, rather than the problematic process only. The only apparent inconvenience is that one has to do an upfront calculation on what would be the largest expected/acceptable number of unconsumed elements in the queue.
• There are no OS-blocking push/pop functions. This queue is designed for ultra-low-latency scenarios and using an OS blocking primitive would be sacrificing push-to-pop latency. For lowest possible latency one cannot afford blocking in the OS kernel because the wake-up latency of a blocked thread is about 1-3 microseconds, whereas this queue’s round-trip time can be as low as 150 nanoseconds.

Ultra-low-latency applications need just that and nothing more. The minimalism pays off, see the throughput and latency benchmarks.

Role Models

Several other well established and popular thread-safe containers are used for reference in the benchmarks:

• std::mutex - a fixed size ring-buffer with std::mutex.
• pthread_spinlock - a fixed size ring-buffer with pthread_spinlock_t.
• boost::lockfree::spsc_queue - a wait-free single-producer-single-consumer queue from Boost library.
• boost::lockfree::queue - a lock-free multiple-producer-multiple-consumer queue from Boost library.
• moodycamel::ConcurrentQueue - a lock-free multiple-producer-multiple-consumer queue used in non-blocking mode. This queue is designed to maximize throughput at the expense of latency and eschewing the global time order of elements pushed into one queue by different threads. It is not equivalent to other queues benchmarked here in this respect.
• moodycamel::ReaderWriterQueue - a lock-free single-producer-single-consumer queue used in non-blocking mode.
• xenium::michael_scott_queue - a lock-free multi-producer-multi-consumer queue proposed by Michael and Scott (this queue is similar to boost::lockfree::queue which is also based on the same proposal).
• xenium::ramalhete_queue - a lock-free multi-producer-multi-consumer queue proposed by Ramalhete and Correia.
• xenium::vyukov_bounded_queue - a bounded multi-producer-multi-consumer queue based on the version proposed by Vyukov.
• tbb::spin_mutex - a locked fixed size ring-buffer with tbb::spin_mutex from Intel Threading Building Blocks.
• tbb::concurrent_bounded_queue - eponymous queue used in non-blocking mode from Intel Threading Building Blocks.

Using the library

The containers provided are header-only class templates, no building/installing is necessary.

Install from GitHub

1. Clone the project:

   git clone https://github.com/max0x7ba/atomic_queue.git
   

2. Add atomic_queue/include directory (use full path) to the include paths of your build system.
3. #include <atomic_queue/atomic_queue.h> in your C++ source.

Install using vcpkg

vcpkg install atomic-queue

Install using conan

Follow the official tutorial on how to consume conan packages. Details specific to this library are available in ConanCenter.

Benchmark build and run instructions

The containers provided are header-only class templates that require only #include <atomic_queue/atomic_queue.h>, no building/installing is necessary.

Building is necessary to run the tests and benchmarks.

git clone https://github.com/cameron314/concurrentqueue.git
git clone https://github.com/cameron314/readerwriterqueue.git
git clone https://github.com/mpoeter/xenium.git
git clone https://github.com/max0x7ba/atomic_queue.git
cd atomic_queue
make -r -j4 run_benchmarks

The benchmark also requires Intel TBB library to be available. It assumes that it is installed in /usr/local/include and /usr/local/lib. If it is installed elsewhere you may like to modify cppflags.tbb and ldlibs.tbb in Makefile.

Library contemts

Available queues

• AtomicQueue - a fixed size ring-buffer for atomic elements.
• OptimistAtomicQueue - a faster fixed size ring-buffer for atomic elements which busy-waits when empty or full. It is AtomicQueue used with push/pop instead of try_push/try_pop.
• AtomicQueue2 - a fixed size ring-buffer for non-atomic elements.
• OptimistAtomicQueue2 - a faster fixed size ring-buffer for non-atomic elements which busy-waits when empty or full. It is AtomicQueue2 used with push/pop instead of try_push/try_pop.

These containers have corresponding AtomicQueueB, OptimistAtomicQueueB, AtomicQueueB2, OptimistAtomicQueueB2 versions where the buffer size is specified as an argument to the constructor.

Totally ordered mode is supported. In this mode consumers receive messages in the same FIFO order the messages were posted. This mode is supported for push and pop functions, but for not the try_ versions. On Intel x86 the totally ordered mode has 0 cost, as of 2019.

Single-producer-single-consumer mode is supported. In this mode, no expensive atomic read-modify-write CPU instructions are necessary, only the cheapest atomic loads and stores. That improves queue throughput significantly.

Move-only queue element types are fully supported. For example, a queue of std::unique_ptr<T> elements would be AtomicQueue2B<std::unique_ptr<T>> or AtomicQueue2<std::unique_ptr<T>, CAPACITY>.

Queue API

The queue class templates provide the following member functions:

• try_push - Appends an element to the end of the queue. Returns false when the queue is full.
• try_pop - Removes an element from the front of the queue. Returns false when the queue is empty.
• push (optimist) - Appends an element to the end of the queue. Busy waits when the queue is full. Faster than try_push when the queue is not full. Optional FIFO producer queuing and total order.
• pop (optimist) - Removes an element from the front of the queue. Busy waits when the queue is empty. Faster than try_pop when the queue is not empty. Optional FIFO consumer queuing and total order.
• was_size - Returns the number of unconsumed elements during the call. The state may have changed by the time the return value is examined.
• was_empty - Returns true if the container was empty during the call. The state may have changed by the time the return value is examined.
• was_full - Returns true if the container was full during the call. The state may have changed by the time the return value is examined.
• capacity - Returns the maximum number of elements the queue can possibly hold.

Atomic elements are those, for which std::atomic<T>{T{}}.is_lock_free() returns true, and, when C++17 features are available, std::atomic<T>::is_always_lock_free evaluates to true at compile time. In other words, the CPU can load, store and compare-and-exchange such elements atomically natively. On x86-64 such elements are all the C++ standard arithmetic and pointer types.

The queues for atomic elements reserve one value to serve as an empty element marker NIL, its default value is 0. NIL value must not be pushed into a queue and there is an assert statement in push functions to guard against that in debug mode builds. Pushing NIL element into a queue in release mode builds results in undefined behaviour, such as deadlocks and/or lost queue elements.

Note that optimism is a choice of a queue modification operation control flow, rather than a queue type. An optimist push is fastest when the queue is not full most of the time, an optimistic pop - when the queue is not empty most of the time. Optimistic and not so operations can be mixed with no restrictions. The OptimistAtomicQueues in the benchmarks use only optimist push and pop.

See example.cc for a usage example.

Implementation Notes

Memory order of non-atomic loads and stores

push and try_push operations synchronize-with (as defined in std::memory_order) with any subsequent pop or try_pop operation of the same queue object. Meaning that:

• No non-atomic load/store gets reordered past push/try_push, which is a memory_order::release operation. Same memory order as that of std::mutex::unlock.
• No non-atomic load/store gets reordered prior to pop/try_pop, which is a memory_order::acquire operation. Same memory order as that of std::mutex::lock.
• The effects of a producer thread’s non-atomic stores followed by push/try_push of an element into a queue become visible in the consumer’s thread which pop/try_pop that particular element.

Ring-buffer capacity

The available queues here use a ring-buffer array for storing elements. The capacity of the queue is fixed at compile time or construction time.

In a production multiple-producer-multiple-consumer scenario the ring-buffer capacity should be set to the maximum expected queue size. When the ring-buffer gets full it means that the consumers cannot consume the elements fast enough. A fix for that is any of:

• Increase the queue capacity in order to handle temporary spikes of pending elements in the queue. This normally requires restarting the application after re-configuration/re-compilation has been done.
• Increase the number of consumers to drain the queue faster. The number of consumers can be managed dynamically, e.g.: when a consumer observes that the number of elements pending in the queue keeps growing, that calls for deploying more consumer threads to drain the queue at a faster rate; mostly empty queue calls for suspending/terminating excess consumer threads.
• Decrease the rate of pushing elements into the queue. push and pop calls always incur some expensive CPU cycles to maintain the integrity of queue state in atomic/consistent/isolated fashion with respect to other threads and these costs increase super-linearly as queue contention grows. Producer batching of multiple small elements or elements resulting from one event into one queue message is often a reasonable solution.

Using a power-of-2 ring-buffer array size allows a couple of important optimizations:

• The writer and reader indexes get mapped into the ring-buffer array index using remainder binary operator % SIZE. Remainder binary operator % normally generates a division CPU instruction which isn’t cheap, but using a power-of-2 size turns that remainder operator into one cheap binary and CPU instruction and that is as fast as it gets.
• The element index within the cache line gets swapped with the cache line index, so that consecutive queue elements reside in different cache lines. This massively reduces cache line contention between multiple producers and multiple consumers. Instead of N producers together with M consumers competing on subsequent elements in the same ring-buffer cache line in the worst case, it is only one producer competing with one consumer (pedantically, when the number of CPUs is not greater than the number of elements that can fit in one cache line). This optimisation scales better with the number of producers and consumers, and element size. With low number of producers and consumers (up to about 2 of each in these benchmarks) disabling this optimisation may yield better throughput (but higher variance across runs).

The containers use unsigned type for size and internal indexes. On x86-64 platform unsigned is 32-bit wide, whereas size_t is 64-bit wide. 64-bit instructions utilise an extra byte instruction prefix resulting in slightly more pressure on the CPU instruction cache and the front-end. Hence, 32-bit unsigned indexes are used to maximise performance. That limits the queue size to 4,294,967,295 elements, which seems to be a reasonable hard limit for many applications.

While the atomic queues can be used with any moveable element types (including std::unique_ptr), for best throughput and latency the queue elements should be cheap to copy and lock-free (e.g. unsigned or T*), so that push and pop operations complete fastest.

Lock-free guarantees

Conceptually, a push or pop operation does two atomic steps:

1. Atomically and exclusively claims the queue slot index to store/load an element to/from. That’s producers incrementing head index, consumers incrementing tail index. Each slot is accessed by one producer and one consumer threads only.
2. Atomically store/load the element into/from the slot. Producer storing into a slot changes its state to be non-NIL, consumer loading from a slot changes its state to be NIL. The slot is a spinlock for its one producer and one consumer threads.

These queues anticipate that a thread doing push or pop may complete step 1 and then be preempted before completing step 2.

An algorithm is lock-free if there is guaranteed system-wide progress. These queue guarantee system-wide progress by the following properties:

• Each push is independent of any preceding push. An incomplete (preempted) push by one producer thread doesn’t affect push of any other thread.
• Each pop is independent of any preceding pop. An incomplete (preempted) pop by one consumer thread doesn’t affect pop of any other thread.
• An incomplete (preempted) push from one producer thread affects only one consumer thread poping an element from this particular queue slot. All other threads pops are unaffected.
• An incomplete (preempted) pop from one consumer thread affects only one producer thread pushing an element into this particular queue slot while expecting it to have been consumed long time ago, in the rather unlikely scenario that producers have wrapped around the entire ring-buffer while this consumer hasn’t completed its pop. All other threads pushs and pops are unaffected.

Preemption

Linux task scheduler thread preemption is something no user-space process should be able to affect or escape, otherwise any/every malicious application would exploit that.

Still, there are a few things one can do to minimize preemption of one’s mission critical application threads:

• Use real-time SCHED_FIFO scheduling class for your threads, e.g. chrt --fifo 50 <app>. A higher priority SCHED_FIFO thread or kernel interrupt handler can still preempt your SCHED_FIFO threads.
• Use one same fixed real-time scheduling priority for all threads accessing same queue objects. Real-time threads with different scheduling priorities modifying one queue object may cause priority inversion and deadlocks. Using the default scheduling class SCHED_OTHER with its dynamically adjusted priorities defeats the purpose of using these queues.
• Disable real-time thread throttling to prevent SCHED_FIFO real-time threads from being throttled.
• Isolate CPU cores, so that no interrupt handlers or applications ever run on it. Mission critical applications should be explicitly placed on these isolated cores with taskset.
• Pin threads to specific cores, otherwise the task scheduler keeps moving threads to other idle CPU cores to level voltage/heat-induced wear-and-tear accross CPU cores. Keeping a thread running on one same CPU core maximizes CPU cache hit rate. Moving a thread to another CPU core incurs otherwise unnecessary CPU cache thrashing.

People often propose limiting busy-waiting with a subsequent call to std::this_thread::yield()/sched_yield/pthread_yield. However, sched_yield is a wrong tool for locking because it doesn’t communicate to the OS kernel what the thread is waiting for, so that the OS thread scheduler can never schedule the calling thread to resume at the right time when the shared state has changed (unless there are no other threads that can run on this CPU core, so that the caller resumes immediately). See notes section in man sched_yield and a Linux kernel thread about sched_yield and spinlocks for more details.

In Linux, there is mutex type PTHREAD_MUTEX_ADAPTIVE_NP which busy-waits a locked mutex for a number of iterations and then makes a blocking syscall into the kernel to deschedule the waiting thread. In the benchmarks it was the worst performer and I couldn’t find a way to make it perform better, and that’s the reason it is not included in the benchmarks.

On Intel CPUs one could use the 4 debug control registers to monitor the spinlock memory region for write access and wait on it using select (and its friends) or sigwait (see perf_event_open and uapi/linux/hw_breakpoint.h for more details). A spinlock waiter could suspend itself with select or sigwait until the spinlock state has been updated. But there are only 4 of these registers, so that such a solution wouldn’t scale.

Benchmarks

View throughput and latency benchmarks charts.

Methodology

There are a few OS behaviours that complicate benchmarking:

• CPU scheduler can place threads on different CPU cores each run. To avoid that the threads are pinned to specific CPU cores.
• CPU scheduler can preempt threads. To avoid that real-time SCHED_FIFO priority 50 is used to disable scheduler time quantum expiry and make the threads non-preemptable by lower priority processes/threads.
• Real-time thread throttling disabled.
• Adverse address space randomisation may cause extra CPU cache conflicts, as well as other processes running on the system. To minimise effects of that benchmarks executable is run at least 33 times. The benchmark charts display average values. The chart tooltip also displays the standard deviation, minimum and maximum values.

I only have access to a few x86-64 machines. If you have access to different hardware feel free to submit the output file of scripts/run-benchmarks.sh and I will include your results into the benchmarks page.

Huge pages

When huge pages are available the benchmarks use 1x1GB or 16x2MB huge pages for the queues to minimise TLB misses. To enable huge pages do one of:

sudo hugeadm --pool-pages-min 1GB:1
sudo hugeadm --pool-pages-min 2MB:16

Alternatively, you may like to enable transparent hugepages in your system and use a hugepage-aware allocator, such as tcmalloc.

Real-time thread throttling

By default, Linux scheduler throttles real-time threads from consuming 100% of CPU and that is detrimental to benchmarking. Full details can be found in Real-Time group scheduling. To disable real-time thread throttling do:

echo -1 | sudo tee /proc/sys/kernel/sched_rt_runtime_us >/dev/null

Throughput and scalability benchmark

N producer threads push a 4-byte integer into one same queue, N consumer threads pop the integers from the queue. All producers posts 1,000,000 messages in total. Total time to send and receive all the messages is measured. The benchmark is run for from 1 producer and 1 consumer up to (total-number-of-cpus / 2) producers/consumers to measure the scalability of different queues.

Ping-pong benchmark

One thread posts an integer to another thread through one queue and waits for a reply from another queue (2 queues in total). The benchmarks measures the total time of 100,000 ping-pongs, best of 10 runs. Contention is minimal here (1-producer-1-consumer, 1 element in the queue) to be able to achieve and measure the lowest latency. Reports the average round-trip time.

Contributing

Contributions are more than welcome. .editorconfig and .clang-format can be used to automatically match code formatting.

Reading material

Some books on the subject of multi-threaded programming I found quite instructive:

• Programming with POSIX Threads by David R. Butenhof.
• The Art of Multiprocessor Programming by Maurice Herlihy, Nir Shavit.

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Copyright (c) 2019 Maxim Egorushkin. MIT License. See the full licence in file LICENSE.

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