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# Triple buffering in Rust
[](https://crates.io/crates/triple_buffer)
[](https://github.com/HadrienG2/triple-buffer/actions?query=workflow%3A%22Continuous+Integration%22)
## What is this?
This is an implementation of triple buffering written in Rust. You may find it
useful for the following class of thread synchronization problems:
- There is one producer thread and one consumer thread
- The producer wants to update a shared memory value periodically
- The consumer wants to access the latest update from the producer at any time
The simplest way to use it is as follows:
```rust
// Create a triple buffer:
let buf = TripleBuffer::new(0);
// Split it into an input and output interface, to be respectively sent to
// the producer thread and the consumer thread:
let (mut buf_input, mut buf_output) = buf.split();
// The producer can move a value into the buffer at any time
buf_input.write(42);
// The consumer can access the latest value from the producer at any time
let latest_value_ref = buf_output.read();
assert_eq!(*latest_value_ref, 42);
```
In situations where moving the original value away and being unable to
modify it on the consumer's side is too costly, such as if creating a new
value involves dynamic memory allocation, you can use a lower-level API
which allows you to access the producer and consumer's buffers in place
and to precisely control when updates are propagated:
```rust
// Create and split a triple buffer
use triple_buffer::TripleBuffer;
let buf = TripleBuffer::new(String::with_capacity(42));
let (mut buf_input, mut buf_output) = buf.split();
// Mutate the input buffer in place
{
// Acquire a reference to the input buffer
let input = buf_input.input_buffer();
// In general, you don't know what's inside of the buffer, so you should
// always reset the value before use (this is a type-specific process).
input.clear();
// Perform an in-place update
input.push_str("Hello, ");
}
// Publish the above input buffer update
buf_input.publish();
// Manually fetch the buffer update from the consumer interface
buf_output.update();
// Acquire a mutable reference to the output buffer
let output = buf_output.output_buffer();
// Post-process the output value before use
output.push_str("world!");
```
## Give me details! How does it compare to alternatives?
Compared to a mutex:
- Only works in single-producer, single-consumer scenarios
- Is nonblocking, and more precisely bounded wait-free. Concurrent accesses will
be slowed down by cache contention, but no deadlock, livelock, or thread
scheduling induced slowdown is possible.
- Allows the producer and consumer to work simultaneously
- Uses a lot more memory (3x payload + 3x bytes vs 1x payload + 1 bool)
- Does not allow in-place updates, as the producer and consumer do not access
the same memory location
- Should be slower if updates are rare and in-place updates are much more
efficient than moves, comparable or faster otherwise.
* Mutexes and triple buffering have comparably low overhead on the happy path
(checking a flag), which is systematically taken when updates are rare. In
this scenario, in-place updates can give mutexes a performance advantage.
Where triple buffering shines is when a reader often collides with a writer,
which is handled very poorly by mutexes.
Compared to the read-copy-update (RCU) primitive from the Linux kernel:
- Only works in single-producer, single-consumer scenarios
- Has higher dirty read overhead on relaxed-memory architectures (ARM, POWER...)
- Does not require accounting for reader "grace periods": once the reader has
gotten access to the latest value, the synchronization transaction is over
- Does not use the compare-and-swap hardware primitive on update, which is
inefficient by design as it forces its users to retry transactions in a loop.
- Does not suffer from the ABA problem, allowing much simpler code
- Allocates memory on initialization only, rather than on every update
- May use more memory (3x payload + 3x bytes vs 1x pointer + amount of
payloads and refcounts that depends on the readout and update pattern)
- Should be slower if updates are rare, faster if updates are frequent
* The RCU's happy reader path is slightly faster (no flag to check), but its
update procedure is much more involved and costly.
Compared to sending the updates on a message queue:
- Only works in single-producer, single-consumer scenarios (queues can work in
other scenarios, although the implementations are much less efficient)
- Consumer only has access to the latest state, not the previous ones
- Consumer does not *need* to get through every previous state
- Is nonblocking AND uses bounded amounts of memory (with queues, it's a choice,
unless you use one of those evil queues that silently drop data when full)
- Can transmit information in a single move, rather than two
- Should be faster for any compatible use case.
* Queues force you to move data twice, once in, once out, which will incur a
significant cost for any nontrivial data. If the inner data requires
allocation, they force you to allocate for every transaction. By design,
they force you to store and go through every update, which is not useful
when you're only interested in the latest version of the data.
In short, triple buffering is what you're after in scenarios where a shared
memory location is updated frequently by a single writer, read by a single
reader who only wants the latest version, and you can spare some RAM.
- If you need multiple producers, look somewhere else
- If you need multiple consumers, you may be interested in my related "SPMC
buffer" work, which basically extends triple buffering to multiple consumers
- If you can't tolerate the RAM overhead or want to update the data in place,
try a Mutex instead (or possibly an RWLock)
- If the shared value is updated very rarely (e.g. every second), try an RCU
- If the consumer must get every update, try a message queue
## How do I know your unsafe lock-free code is working?
By running the tests, of course! Which is unfortunately currently harder than
I'd like it to be.
First of all, we have sequential tests, which are very thorough but obviously
do not check the lock-free/synchronization part. You run them as follows:
$ cargo test
Then we have concurrent tests where, for example, a reader thread continuously
observes the values from a rate-limited writer thread, and makes sure that he
can see every single update without any incorrect value slipping in the middle.
These tests are more important, but also harder to run because one must first
check some assumptions:
- The testing host must have at least 2 physical CPU cores to test all possible
race conditions
- No other code should be eating CPU in the background. Including other tests.
- As the proper writing rate is system-dependent, what is configured in this
test may not be appropriate for your machine.
- You must test in release mode, as compiler optimizations tend to create more
opportunities for race conditions.
Taking this and the relatively long run time (~10-20 s) into account, the
concurrent tests are ignored by default. To run them, make sure nothing is
eating CPU in the background and do:
$ cargo test --release -- --ignored --nocapture --test-threads=1
Finally, we have benchmarks, which allow you to test how well the code is
performing on your machine. We are now using `criterion` for said benchmarks,
which seems that to run them, you can simply do:
$ cargo bench
These benchmarks exercise the worst-case scenario of `u8` payloads, where
synchronization overhead dominates as the cost of reading and writing the
actual data is only 1 cycle. In real-world use cases, you will spend more time
updating buffers and less time synchronizing them.
However, due to the artificial nature of microbenchmarking, the benchmarks must
exercise two scenarios which are respectively overly optimistic and overly
pessimistic:
1. In uncontended mode, the buffer input and output reside on the same CPU core,
which underestimates the overhead of transferring modified cache lines from
the L1 cache of the source CPU to that of the destination CPU.
* This is not as bad as it sounds, because you will pay this overhead no
matter what kind of thread synchronization primitive you use, so we're not
hiding `triple-buffer` specific overhead here. All you need to do is to
ensure that when comparing against another synchronization primitive, that
primitive is benchmarked in a similar way.
2. In contended mode, the benchmarked half of the triple buffer is operating
under maximal load from the other half, which is much more busy than what is
actually going to be observed in real-world workloads.
* In this configuration, what you're essentially measuring is the performance
of your CPU's cache line locking protocol and inter-CPU core data
transfers under the shared data access pattern of `triple-buffer`.
Therefore, consider these benchmarks' timings as orders of magnitude of the best
and the worst that you can expect from `triple-buffer`, where actual performance
will be somewhere inbetween these two numbers depending on your workload.
On an Intel Core i3-3220 CPU @ 3.30GHz, typical results are as follows:
* Clean read: 0.9 ns
* Write: 6.9 ns
* Write + dirty read: 19.6 ns
* Dirty read (estimated): 12.7 ns
* Contended write: 60.8 ns
* Contended read: 59.2 ns
## License
This crate is distributed under the terms of the MPLv2 license. See the LICENSE
file for details.
More relaxed licensing (Apache, MIT, BSD...) may also be negociated, in
exchange of a financial contribution. Contact me for details at
knights_of_ni AT gmx DOTCOM.