CPU atomics and orderings explained

Sometimes the question comes up about how CPU memory orderings work, and what they do. I hope this post explains it in a really accessible way.

Short Version - I wanna code!

Summary - The memory model you commonly see is from C++ and it defines:

  • Relaxed
  • Acquire
  • Release
  • Acquire/Release (sometimes AcqRel)
  • SeqCst

There are memory orderings - every operation is “atomic”, so will work correctly, but there rules define how the memory and code around the atomic are influenced.

If in doubt - use SeqCst - it’s the strongest guarantee and prevents all re-ordering of operations and will do the right thing.

The summary is:

  • Relaxed - no ordering guarantees, just execute the atomic as is.
  • Acquire - all code after this atomic, will be executed after the atomic.
  • Release - all code before this atomic, will be executed before the atomic.
  • Acquire/Release - both Acquire and Release - ie code stays before and after.
  • SeqCst - Stronger consistency of Acquire/Release.

Long Version … let’s begin …

So why do we have memory and operation orderings at all? Let’s look at some code to explain:

let mut x = 0;
let mut y = 0;
x = x + 3;
y = y + 7;
x = x + 4;
x = y + x;

Really trivial example - now to us as a human, we read this and see a set of operations that are linear by time. That means, they execute from top to bottom, in order.

However, this is not how computers work. First, compilers will optimise your code, and optimisation means re-ordering of the operations to achieve better results. A compiler may optimise this to:

let mut x = 0;
let mut y = 0;
// Note removal of the x + 3 and x + 4, folded to a single operation.
x = x + 7
y = y + 7;
x = y + x;

Now there is a second element. Your CPU presents the illusion of running as a linear system, but it’s actually an asynchronous, out-of-order task execution engine. That means a CPU will reorder your instructions, and may even run them concurrently and asynchronously.

For example, your CPU will have both x + 7 and y + 7 in the pipeline, even though neither operation has completed - they are effectively running at the “same time” (concurrently).

When you write a single thread program, you generally won’t notice this behaviour. This is because a lot of smart people write compilers and CPU’s to give the illusion of linear ordering, even though both of them are operating very differently.

Now we want to write a multithreaded application. Suddenly this is the challenge:

We write a concurrent program, in a linear language, executed on a concurrent asynchronous machine.

This means there is a challenge is the translation between our mind (thinking about the concurrent problem), the program (which we have to express as a linear set of operations), which then runs on our CPU (an async concurrent device).

Phew. How do computers even work in this scenario?!

Why are CPU’s async?

CPU’s have to be async to be fast - remember spectre and meltdown? These are attacks based on measuring the side effects of CPU’s asynchronous behaviour. While computers are “fast” these attacks will always be possible, because to make a CPU synchronous is slow - and asynchronous behaviour will always have measurable side effects. Every modern CPU’s performance is an illusion of async black magic.

A large portion of the async behaviour comes from the interaction of the CPU, cache, and memory.

In order to provide the “illusion” of a coherent synchronous memory interface there is no seperation of your programs cache and memory. When the cpu wants to access “memory” the CPU cache is utilised transparently and will handle the request, and only on a cache miss, will we retrieve the values from RAM.

(Aside: in almost all cases more CPU cache, not frequency will make your system perform better, because a cache miss will mean your task stalls waiting on RAM. Ohh no!)

CPU -> Cache -> RAM

When you have multiple CPU’s, each CPU has it’s own L1 cache:

CPU1 -> L1 Cache -> |              |
CPU2 -> L1 Cache -> | Shared L2/L3 | -> RAM
CPU3 -> L1 Cache -> |              |
CPU4 -> L1 Cache -> |              |

Ahhh! Suddenly we can see where problems can occur - each CPU has an L1 cache, which is transparent to memory but unique to the CPU. This means that each CPU can make a change to the same piece of memory in their L1 cache without the other CPU knowing. To help explain, let’s show a demo.

CPU just trash my variables fam

We’ll assume we now have two threads - my code is in rust again, and there is a good reason for the unsafes - this code really is unsafe!

// assume global x: usize = 0; y: usize = 0;

THREAD 1                        THREAD 2

if unsafe { *x == 1 } {          unsafe {
    unsafe { *y += 1 }              *y = 10;
}                                   *x = 1;
                                }

At the end of execution, what state will X and Y be in? The answer is “it depends”:

  • What order did the threads run?
  • The state of the L1 cache of each CPU
  • The possible interleavings of the operations.
  • Compiler re-ordering

In the end the result of x will always be 1 - because x is only mutated in one thread, the caches will “eventually” (explained soon) become consistent.

The real question is y. y could be:

  • 10
  • 11
  • 1

10 - This can occur because in thread 2, x = 1 is re-ordered above y = 10, causing the thread 1 “y += 1” to execute, followed by thread 2 assign 10 directly to y. It can also occur because the check for x == 1 occurs first, so y += 1 is skipped, then thread 2 is run, causing y = 10. Two ways to achieve the same result!

11 - This occurs in the “normal” execution path - all things considered it’s a miracle :)

1 - This is the most complex one - The y = 10 in thread 2 is applied, but the result is never sent to THREAD 1’s cache, so x = 1 occurs and is made available to THREAD 1 (yes, this is possible to have different values made available to each cpu …). Then thread 1 executes y (0) += 1, which is then sent back trampling the value of y = 10 from thread 2.

If you want to know more about this and many other horrors of CPU execution, Paul McKenny is an expert in this field and has many talks at LCA and others on the topic. He can be found on twitter and is super helpful if you have questions.

So how does a CPU work at all?

Obviously your system (likely a multicore system) works today - so it must be possible to write correct concurrent software. Cache’s are kept in sync via a protocol called MESI. This is a state machine describing the states of memory and cache, and how they can be synchronised. The states are:

  • Modified
  • Exclusive
  • Shared
  • Invalid

What’s interesting about MESI is that each cache line is maintaining it’s own state machine of the memory addresses - it’s not a global state machine. To coordinate CPU’s asynchronously message each other.

A CPU can be messaged via IPC (Inter-Processor-Communication) to say that another CPU wants to “claim” exclusive ownership of a memory address, or to indicate that it has changed the content of a memory address and you should discard your version. It’s important to understand these messages are asynchronous. When a CPU modifies an address it does not immediately send the invalidation message to all other CPU’s - and when a CPU recieves the invalidation request it does not immediately act upon that message.

If CPU’s did “synchronously” act on all these messages, they would be spending so much time handling IPC traffic, they would never get anything done!

As a result, it must be possible to indicate to a CPU that it’s time to send or acknowledge these invalidations in the cache line. This is where barriers, or the memory orderings come in.

  • Relaxed - No messages are sent or acknowledged.
  • Release - flush all pending invalidations to be sent to other CPUS
  • Acquire - Acknowledge and process all invalidation requests in my queue
  • Acquire/Release - flush all outgoing invalidations, and process my incomming queue
  • SeqCst - as AcqRel, but with some other guarantees around ordering that are beyond this discussion.

Understand a Mutex

With this knowledge in place, we are finally in a position to understand the operations of a Mutex

// Assume mutex: Mutex<usize> = Mutex::new(0);

THREAD 1                            THREAD 2

{                                   {
    let guard = mutex.lock()            let guard = mutex.lock()
    *guard += 1;                        println!(*guard)
}                                   }

We know very clearly that this will print 1 or 0 - it’s safe, no weird behaviours. Let’s explain this case though:

THREAD 1

{
    let guard = mutex.lock()
    // Acquire here!
    // All invalidation handled, guard is 0.
    // Compiler is told "all following code must stay after .lock()".
    *guard += 1;
    // content of usize is changed, invalid req is queue
}
// Release here!
// Guard goes out of scope, invalidation reqs sent to all CPU's
// Compiler told all proceeding code must stay above this point.

            THREAD 2

            {
                let guard = mutex.lock()
                // Acquire here!
                // All invalidations handled - previous cache of usize discarded
                // and read from THREAD 1 cache into S state.
                // Compiler is told "all following code must stay after .lock()".
                println(*guard);
            }
            // Release here!
            // Guard goes out of scope, no invalidations sent due to
            // no modifications.
            // Compiler told all proceeding code must stay above this point.

And there we have it! How barriers allow us to define an ordering in code and a CPU, to ensure our caches and compiler outputs are correct and consistent.

Benefits of Rust

A nice benefit of Rust, and knowing these MESI states now, we can see that the best way to run a system is to minimise the number of invalidations being sent and acknowledged as this always causes a delay on CPU time. Rust variables are always mutable or immutable. These map almost directly to the E and S states of MESI. A mutable value is always exclusive to a single cache line, with no contention - and immutable values can be placed into the Shared state allowing each CPU to maintain a cache copy for higher performance.

This is one of the reasons for Rust’s amazing concurrency story is that the memory in your program map to cache states very clearly.

It’s also why it’s unsafe to mutate a pointer between two threads (a global) - because the cache of the two cpus’ won’t be coherent, and you may not cause a crash, but one threads work will absolutely be lost!

Finally, it’s important to see that this is why using the correct concurrency primitives matter - it can highly influence your cache behaviour in your program and how that affects cache line contention and performance.

For comments and more, please feel free to email me!

Shameless Plug

I’m the author and maintainer of Conc Read - a concurrently readable datastructure library for Rust. Check it out on crates.io!