Concurrency II

CIS 198 Lecture 11

More Concurrency Primitives!

• RwLock
• Barrier
• CondVar
• Once

std::sync::RwLock<T>

• A reader-writer lock.
• Similar to a Mutex, but has separate modes for reading and writing access.
• Allows data access to many readers or one writer.
• Trying to acquire a lock may block depending on the borrow state of the container.
• Inner T must be Send + Sync.
• May also be poisoned like a Mutex.
• Poisoning only occurs if a writer panics.

std::sync::RwLock<T>

• Useful if you have many threads requesting reads and few requesting writes.
• RwLock allows safe, concurrent access with better performance (fewer lockouts; more concurrency) in some situations.
• Compare with a Mutex, which locks everyone else out.
• Not strictly better; RwLock only works on Sync types - otherwise, you really need mutual exclusion.
• Compare with a RefCell, which also encodes the single-writer-or-multiple-reader idea, but panics instead of blocking on conflicts and is never thread-safe (Sync).
• As with Mutex, an RwLock could cause deadlock under certain conditions.

std::sync::Barrier

• A type to allow multiple threads to synchronize on a computation.
• Created for a fixed number of threads.
• Threads may call wait on a copy of the Barrier to “report” their readiness.
• Blocks n - 1 threads and wakes all blocked threads when the nth thread reports.

std::sync::Barrier

• Another primitive to help ensure consistent world views!
• Recall from last lecture: how do you ensure all threads see the same data?
• For example, readers may want to be guaranteed that all writes have completed before they read.
• Able to synchronize on this action with a Barrier.

std::sync::Condvar

• A “condition variable”.
• Blocks a thread so that it consumes no CPU time while waiting for an event.
• Generally associated with a Mutex wrapping some boolean predicate, which is the blocking predicate.
• Logically similar to having a thread do this:

1. while !predicate { }

std::sync::Condvar

• A nice way to put a thread “on hold”.
• Does not require holding any locks.
• We could have the thread spin in a loop as above, but that would waste CPU time.
• The waiting thread has to be scheduled to spin-wait.
• Repeatedly checking the predicate might also require acquiring a lock, which might cause other thread to deadlock.

std::sync::Condvar

• Motivating example: producer/consumer problem.
• Producer threads add items to a bounded queue
• Consumer threads take them off.
• Full queue: producer threads should wait until it has space.
• Empty queue: consumer threads should wait until it has items.
• Both producers & consumers can wait on a condition variable.
• Threads can be woken when the queue is ready.

std::sync::Once

• A primitive to run a one-time global initialization.
• Regardless of how many times once.call_once(function) is called, only the first one will execute.
• After that, it’s guaranteed that the initialization was run, and all memory writes performed are visible from all threads.
• Allows the code to attempt initialization more than once (e.g. one per thread).
• Useful for doing one-time initialization to call foreign functions (e.g. functions from a C library).

Atomics

• Instruction Reordering
• Atomicity
• Rust Atomics
• Data Access
• Atomic Memory Ordering

¹ Some section content borrowed from The Rustonomicon

Instruction Reordering

• Ordinarily, the compiler may reorder instructions in your program semi-arbitrarily.
• Unless there are compiler bugs, your code should at least have sequential consistency.
• Instructions may also be optimized out by the compiler.
• In single-threaded code, these optimizations are fine.
• In multi-threaded code, we might have relied on x being equal to 1 at some point, and this optimization may be bogus!

1. x = 1;
2. y = 2;  =====>  x = 3;
3. x = 3;          y = 2;

Hardware Reordering

• Even if the compiler behaves, the CPU may decide to shake things up.
• Due to memory hierarchies, hardware doesn’t guarantee that all threads see the same view.
• Events may occur in one order on thread 1, and in a totally different order on thread 2.

Hardware Reordering

• This code has two possible final states:
• y = 3 (thread 1 writes to y after thread 2 reads y)
• y = 6 (thread 1 writes to y before thread 2 reads y)
• Hardware reordering enables a third final state: y = 2.
• Here, thread 2 read x, didn’t read y = 3, and then wrote y = 2 * 1.

1. Initially: x = 0, y = 1;

3. Thread 1  |  Thread 2
4. ----------+-------------
5. y = 3;    |  if x == 1 {
6. x = 1;    |      y *= 2;
7. |  }

Hardware Reordering

• In order to ensure events happen in the order we want them to, the compiler needs to emit special CPU instructions.
• Even so, different CPUs have different guarantees surrounding these instructions.
• Some CPUs guarantee strong event ordering by default, others weak ordering.
• Strong ordering guarantees causes less instruction reordering.
• Different hardware may have different performance depending on the type of guarantees provided.

Atomics

• What are atomics?
• An operation is atomic if it appears to occur “instantly” from the program’s perspective.
• Not time-instant, but instruction-instant.
• An atomic operation is not divisible into sub-operations.
• Atomicity is an important property to ensure that certain operations will not be broken up by thread interleaving.
• Atomic operations are a compiler intrinsic, and are handled by special CPU instructions.

std::sync::atomic

• Rust’s atomic primitives define this behavior at the type level.
• Four kinds: AtomicUsize, Isize, Bool, Ptr
• Standard atomics are safe to share between threads (Sync).
• Most non-primitive types in std::sync use these primitives (Arc, Mutex, etc.).
• If you want to build your own thread-safe types, you probably need to use atomics.

Data Access

• An atomic type’s value may not be used by simple member variable access.
• e.g., you can’t just access the value of an AtomicUsize freely.
• Atomic variables are not true language primitives like usize.
• Instead, you must load from the struct to read its value, and store a value to update it.
• More complex operations may also be performed (e.g., swap, fetch-and-add).
• We’ll see why these are useful later.

Atomic Memory Ordering

• All operations on atomics require an Ordering.
• An Ordering describes how the compiler & CPU may reorder instructions surrounding atomic operations.
• Rust uses the same memory orderings as LLVM, and the same atomic model as C11.
• You won’t usually have to worry about this unless you’re building concurrency libraries from the ground up.

Concurrency Applications

Multithreaded Networking

• Networking lends itself well to multithreading.
• Socket operations are often blocking, so running several operations in parallel is convenient.
• A program can listen on a socket on one thread, and send over a socket on another.

Multithreaded Networking

• A server listening for client connections might want to spawn one thread per connection.
• Each client thread will block until it receives a message.
• Messages can be relayed to the main thread using channels!
• The main thread could also use a channel to relay messages back to clients.

Multithreaded Networking

• This model unfortunately does not scale well.
• Spawning threads is expensive.
• Destroying threads is expensive.
• A more complex model using thread pools* is often necessary.
• A set of threads is pre-allocated, e.g. one thread per CPU core.
• Rather than blocking, each thread will poll each client socket (asynchronously).
• Data may be available immediately as a consequence.

¹ More on these in a bit.

Rayon

• A data parallelism library developed by Niko Matsakis, inspired by Cilk.
• Two primary features:
• Parallel iterators: take iterator chains and execute them in parallel.
• rayon::join, which converts recursive divide-and-conquer iterators to execute in parallel.
• Statically guarantees data race safety!
• This may seem to contradict what we said last week, but Rayon achieves this property via allowing particular, limited data access per thread.
• Still pretty experimental, so user beware.

Parallel Iterators

• Parallel iterators are really easy!
• (As long as everything is Send and Sync)
• Just use par_iter() or par_iter_mut() instead of the non-par_ variants.
• Rayon also provides a number of parallel iterator adapters (map, fold, filter, etc.).

1. // Increment all values in a slice
2. use rayon::prelude::*;
3. fn increment_all(input: &mut [i32]) {
4. input.par_iter_mut()
5. .for_each(|p| *p += 1);
6. }

Recursive Divide-And-Conquer

• Recursive divide-and-conquer problems can be parallelized using Rayon’s join method.
• Parallel iterators are built on this method, and par_iter() abtracts over it.
• join takes two closures and potentially runs them in parallel.
• Lets the program decide on the use of parallelism dynamically.
• Effectively an annotation to say “run this in parallel if you can.”

Recursive Divide-And-Conquer

• We can rewrite increment_all() using join().
• Even though this actually complicates the implementation.

1. // Increment all values in slice.
2. fn increment_all(slice: &mut [i32]) {
3. if slice.len() < 1000 {
4. for p in slice { *p += 1; }
5. } else {
6. let mid_point = slice.len() / 2;
7. let (left, right) = slice.split_at_mut(mid_point);
8. rayon::join(|| increment_all(left),
9. || increment_all(right));
10. }
11. }

Recursive Divide-And-Conquer

• join() can also be used to implement things like parallel quicksort:

1. fn quick_sort<T:PartialOrd+Send>(v: &mut [T]) {
2. if v.len() <= 1 {
3. return;
4. }

6. let mid = partition(v); // Choose some partition
7. let (lo, hi) = v.split_at_mut(mid);
8. rayon::join(|| quick_sort(lo), || quick_sort(hi));
9. }

Recursive Divide-And-Conquer

• Be careful: join is not the same as just spawning two threads (one per closure).
• If all CPUs are already busy, Rayon will opt to run the closures in sequence.
• join is designed to have low overhead, but may have performance implications on small workloads.
• You may want to have a serial fallback for smaller workloads.
• Parallel iterators already have this in place, but join is lower-level.

Safety

• Basic examples that would intuitively violate borrowing rules are invalid in Rayon:

1. // This fails to compile, since both threads in `join`
2. // try to borrow `slice` mutably.
3. fn increment_all(slice: &mut [i32]) {
4. rayon::join(|| process(slice), || process(slice));
5. }

Safety

• Rayon is guaranteed to be free of data races.
• Rayon itself cannot cause deadlocks.
• As a consequence, you can’t use any types with Rayon which are not thread-safe.
• The Rayon docs suggest these conversions for thread-unsafe types:
• Cell -> AtomicUsize, AtomicBool, etc.
• RefCell -> RwLock
• Rc -> Arc
• However, there are some nuances to these conversions, and they can’t be done blindly.
• This is true when using concurrency in general.

Safety

• Using (Ref)Cell-like structures in parallel has some pitfalls due to code interleaving.
• Something like an Rc<Cell<usize>> can’t be blindly logically converted to an Arc<AtomicUsize>.
• Consider this example, where ts is an Arc<AtomicUsize>:

1. Thread 1                        |  Thread 2
2. --------------------------------+--------------------------------
3. let value =                     |
4. ts.load(Ordering::SeqCst);    |
5. // value = X                    |  let value =
6. |    ts.load(Ordering::SeqCst);
7. |  // value = X
8. ts.store(value+1);              |  ts.store(value+1);
9. // ts = X+1                     |  // ts = X+1

Safety

• Above, ts only gets incremented by 1, but we expect it to get incremented twice.
• To guarantee that this happens, we need something better than just load and store.
• fetch_add is more appropriate (and correct!) in this case.
• This method performs an atomic load & store in one operation.
• Atomicity of operations needs to be accounted for in these cases.
• Once again, concurrency primitives are not magic bullets.

Safety

• Why doesn’t Rust automatically protect from the above example? Isn’t that the whole point of Rust?
• The type system prevents you from making basic mistakes, but thread interleaving is sometimes useful.
• Example: a shortest-route search in parallel that terminates a thread when it goes beyond the current shortest path length.

Internals

• Rayon is built on the concept of work stealing.
• Conceptually, there is a pool of threads in memory, typically one per CPU, that can run tasks.
• When you call join(a, b), a is started, and b gets put on a queue of pending work.
• Any idle threads will scan the queue of pending tasks and choose one to execute.
• Once a completes, the thread that ran a checks to see if b was taken off the queue, and runs it if not.

Scoped Threads

• Threads which are guaranteed to terminate before the current stack frame goes away.
• The thread is “scoped” to the stack frame in which it was created.
• Such threads can access their parent stack frame directly.
• This data is guaranteed to always be valid from the thread’s view.
• Simple, right? 😼

Scoped Threads

• Rust does not have a standard scoped thread library anymore.
• Instead, there are three notable ones:
• scoped_threadpool
• scoped_pool
• crossbeam
• These are all relatively* similar, so we’ll look at some high-level features from each.

*Your personal value of relativity may vary

Scoped Threads

• Crossbeam’s scoped threads work a bit differently.
• crossbeam::Scope::defer(function) schedules some code to be executed at the end of its scope.

• crossbeam::Scope::spawn creates a standalone scoped thread, tied to a parent scope.

• Created just like regular threads.

• A scoped thread mandates that its parent scope must wait for it to finish before its scope exits.

Scoped Threads

• When you spawn a std thread, the function the thread executes must have the 'static lifetime.
• This ensures that the thread lives long enough.
• Because Scope is tied to its parent thread’s liftetime, the function the thread executes need only have some 'a lifetime of its parent!
• This avoids the need to:
• Explicitly join on threads from some main thread.
• Potentially put some data in a heavy Arc wrapper just to share it with local threads.

Scoped Threads

• Crossbeam scoped threads can also defer their destruction until after they would have been destructed.
• Crossbeam scoped threads are also totally black magic, you guys.
• Crossbeam also has a few other concurrency features, but they’re somewhat out of scope here.

Thread Pools

• Two of these libraries provide thread pools.
• Thread pools are a collection of threads that can be scheduled to a set of tasks.
• Threads are created up-front and stored in memory in the pool, and can be dispatched as needed.

Thread Pools

• Threads are not destroyed when they complete, but are saved for re-use.
• Ad hoc thread creation & destruction can be very expensive.
• Choosing a good thread pool size is often an indeterminate problem.
• Pool size is best chosen based on system requirements.

Thread Pools

• scoped_pool provided pools with both scoped and unscoped threads.
• scoped_threadpool only has scoped pools.
• Unscoped threads will dispatch from the pool, but will not be waited on to complete.
• Scoped threads must be waited on by the pool.

Thread Pools

• One nice feature of Rust is that thread lifetimes can be expressed explicitly.
• It’s very easy to statically reason about how long threads will live.
• Can also enforce guarantees about what thread scopes are tied to what lifetimes.

Interlude: Thread Unsoundness Bug

• Remember how we said scoped threads used to be in std?
• Before Rust 1.0, a serious bug was discovered with scoped threads and a few other types.
• In short, it was possible to create a scoped thread, leak it with mem::forget, and have it end up accessing freed memory.
• See discussion here and here.

Eventual

• A library for performing asynchronous computations.
• Employs Futures and Streams, which represent asynchronous computations.
• Futures are similar to Promises (in JavaScript).

The term promise was proposed in 1976 by Daniel P. Friedman and David Wise, and Peter Hibbard called it eventual. A somewhat similar concept future was introduced in 1977 in a paper by Henry Baker and Carl Hewitt. —Wikipedia

Futures & Streams

• A Future is a proxy for a value which is being computed asynchronously.
• The computation behind a Future may be run in another thread, or may be kicked off as a callback to some other operation.
• You can think of a Future like a Result whose value is computed asynchronously.
• Futures can be polled for their information, blocked on, ANDed together with other Futures, etc.
• Streams are similar to Futures, but represent a sequence of values instead of just one.

Futures & Streams

1. extern crate eventual;
2. use eventual::*;

4. let f1 = Future::spawn(|| { 1 });
5. let f2 = Future::spawn(|| { 2 });
6. let res = join((f1, f2))
7. .and_then(|(v1, v2)| Ok(v1 + v2))
8. .await().unwrap();
9. println!("{}", res); // 3

Futures

• Futures are useful for offloading slow computational tasks into the “background”.
• Easier than running a raw thread with the desired computation.
• Example: Requesting large images from an HTTP server might be slow, so it might be better to fetch images in parallel asynchronously.