C++ lockless queue.
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The main design principle these queues follow is simplicity: the bare minimum of atomic operations, fixed size buffer, value semantics.
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.
These qualities are also limitations:
Nevertheless, ultra-low-latency applications need just that and nothing more. The simplicity pays off, see the throughput and latency benchmarks.
Available containers are: *
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. *
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.
These containers have corresponding
OptimistAtomicQueueB2versions 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
popfunctions, 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 read-modify-write instructions are necessary, only the atomic loads and stores. That improves queue throughput significantly.
A few other thread-safe containers are used for reference in the benchmarks: *
std::mutex- a fixed size ring-buffer with
pthread_spinlock- a fixed size ring-buffer with
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. *
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::queuewhich 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_mutexfrom Intel Threading Building Blocks. *
tbb::concurrent_bounded_queue- eponymous queue used in non-blocking mode from Intel Threading Building Blocks.
The containers provided are header-only class templates, no building/installing is necessary.
git clone https://github.com/max0x7ba/atomic_queue.git
atomic_queue/includedirectory (use full path) to the include paths of your build system.
#includein your C++ source.
The containers provided are header-only class templates that require only
#include, 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/lib. If it is installed elsewhere you may like to modify
The containers support the following APIs: *
try_push- Appends an element to the end of the queue. Returns
falsewhen the queue is full. *
try_pop- Removes an element from the front of the queue. Returns
falsewhen the queue is empty. *
push- Appends an element to the end of the queue. Busy waits when the queue is full. Faster than
try_pushwhen the queue is not full. Optional FIFO producer queuing and total order. *
pop- Removes an element from the front of the queue. Busy waits when the queue is empty. Faster than
try_popwhen 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. *
trueif the container was empty during the call. The state may have changed by the time the return value is examined. *
trueif 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.
See example.cc for a usage example.
TODO: full API reference.
The available queues here use a ring-buffer array for storing elements. The size of the queue is fixed at compile time or construction time.
In a production multiple-producer-multiple-consumer scenario the ring-buffer size should be set to the maximum allowable queue size. When the buffer size is exhausted it means that the consumers cannot consume the elements fast enough, fixing which would require either of:
Using a power-of-2 ring-buffer array size allows a couple of important optimizations:
% SIZEbinary operator and using a power-of-2 size turns that modulo operator into one plain
andinstruction and that is as fast as it gets.
Nproducers together with
Mconsumers competing on the same ring-buffer array cache line in the worst case, it is only one producer competing with one consumer. 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
unsignedtype for size and internal indexes. On x86-64 platform
unsignedis 32-bit wide, whereas
size_tis 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
unsignedindexes 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 througput and latency the queue elements should be cheap to copy and lock-free (e.g.
T*), so that
popoperations complete fastest.
popboth perform two atomic operations: increment the counter to claim the element slot and store the element into the array. If a thread calling
popis pre-empted between the two atomic operations that causes another thread calling
push(corresondingly) on the same slot to spin on loading the element until the element is stored; other threads calling
popare not affected. Using real-time
SCHED_FIFOthreads reduces the risk of pre-emption, however, a higher priority
SCHED_FIFOthread or kernel interrupt handler can still preempt your
SCHED_FIFOthread. If the queues are used on isolated cores with real-time priority threads, in which case no pre-emption or interrupts occur, the queues operations become lock-free.
So, ideally, you may like to run your critical low-latency code on isolated cores that also no other processes can possibly use. And disable real-time thread throttling to prevent
SCHED_FIFOreal-time threads from being throttled.
Some people proposed busy-waiting with a call to
sched_yieldis a wrong tool for locking because it doesn't communicate to the OS kernel what the thread is waiting for, so that the OS scheduler can never wake up the calling thread at the "right" time, unless there are no other threads that can run on this CPU. More details about
sched_yieldand spinlocks from Linus Torvalds.
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_FIFOpriority 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. To minimise effects of that
benchmarksexecutable is run at least 33 times and then the results with the highest throughput / lowest latency are selected.
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.shand I will include your results into the benchmarks page.
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 --pool-pages-max 1GB:1 sudo hugeadm --pool-pages-min 2MB:16 --pool-pages-max 2MB:16
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
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.
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.
The project uses
.clang-formatto automate formatting. Pull requests are expected to be formatted using these settings.
scripts/run-benchmarks.shand email me the results file, or put it under
results/and submit a pull request.
Copyright (c) 2019 Maxim Egorushkin. MIT License. See the full licence in file LICENSE.