As the number of CPU cores keeps growing, the scalability of concurrent data structures becomes increasingly important. A data structure that works fine on 4 cores can become a bottleneck on 32, not because of algorithmic limitations, but because of how it synchronizes access.
We illustrate that with a simple binary tree. Usually these data structures are protected by some kind of lock:
struct Node {
mutex lock;
key_type key;
value_type value;
Node* left, *right;
};
struct Tree {
mutex lock;
Node* root;
};When searching a value, we can traverse the data structure, lock the parts of the data we are currently touching, and release locks when we are done (“lock coupling”):
option<value_type> Tree::lookup(key_type key) {
lock.lock_shared();
mutex* currentLock = &lock;
Node* iter = root;
option<value_type> result;
while (iter) {
if (key == iter->key) {
result = iter->value;
break;
}
Node* next = (key < iter->key) ? iter->left : iter->right;
if (next) next->lock.lock_shared();
currentLock->unlock();
currentLock = next ? &next->lock : nullptr;
iter = next;
}
currentLock->unlock();
return result;
}While conceptually simple, lock coupling has quite poor performance in practice. The problem is that it creates contention on the locks, in particular for the root node. Every lookup goes through the root node, thus the root node is constantly locked and unlocked. While there is no semantic contention between lookups, as all readers can read the root concurrently, there is physical contention on the lock itself, which limits scalability. This can be seen below, with concurrent lookups in a tree of 100,000 elements, executed on a 16-core / 32-thread 9950X3D.
This contention problem can be solved by using Optimistic Lock Coupling, a synchronization technique where readers do not perform any writes. The key idea here is that writers lock as usual, and increase a version number when they are done updating. Readers read the version number before access, read the elements they are interested in, and then re-check the version number. If the version number changed (or the element is currently locked), the read fails and the reader tries again. In (slightly simplified) code it looks like this:
struct Node {
version_lock lock;
key_type key;
value_type value;
Node* left, *right;
};
struct Tree {
version_lock lock;
Node* root;
};
option<value_type> Tree::lookup(key_type key) {
restart: lock_guard guard = lock.lock_optimistic();
Node* iter = root;
if (!guard.validate()) goto restart;
option<value_type> result;
while (iter) {
auto currentKey = iter->key;
if (!guard.validate()) goto restart;
if (key == currentKey) {
auto currentValue = iter->value;
if (!guard.validate()) goto restart;
result = currentValue;
break;
}
iter = (key < currentKey) ? iter->left : iter->right;
if (!guard.validate()) goto restart;
auto nextGuard = iter->lock.lock_optimistic();
if (!guard.validate()) goto restart;
guard = nextGuard;
}
return result;
}It is not that different from the classic lock coupling code above, except that we always have to check for concurrent writes before acting on the read values. The great benefit of this strategy is that the lookup code is purely read-only, which allows it to scale nicely with the number of cores:
While Optimistic Lock Coupling offers excellent performance, it is a bit dangerous to use. If you act upon a value before validating, you effectively have a race condition in your code. In this small example it is clear when we have to validate, but in complex code fragments it is easy to forget to validate.
The best way to mitigate that is to get compiler support, by encoding the fact that we need to validate in the type system. We can achieve that by representing unvalidated values as a dedicated type, and having only the lock guard expose that value. Conceptually it looks like this (variants that validate multiple values omitted for simplicity):
template <class T>
class unvalidated {
T value;
friend class lock_guard;
};
class lock_guard {
...
template <class T>
optional<T> validate(unvalidated<T> value);
};Basically we only allow access to the original value by validating,
which makes that construct safe. But how do we ensure that code properly
wraps everything in unvalidated<T>? By exposing only
an optimistic view over the data. Conceptually we do the
following:
struct Node {
class OptimisticView;
... // as above
};
template <class T>
class OptimisticPtr
{
T* rawPtr;
public:
// We cannot use operator-> here unfortunately due to C++ constraints
typename T::OptimisticView data() const { return T::OptimisticView(rawPtr); }
};
// Exposes each member as unvalidated<T> value
class Node::OptimisticView {
class Node* rawData;
public:
unvalidated<key_type> key() { return unvalidated(atomic_ref(rawData->key).load(memory_order_relaxed)); }
unvalidated<value_type> value() { return unvalidated(atomic_ref(rawData->value).load(memory_order_relaxed)); }
unvalidated<OptimisticPtr<Node>> left() { return unvalidated(OptimisticPtr(atomic_ref(rawData->left).load(memory_order_relaxed))); }
unvalidated<OptimisticPtr<Node>> right() { return unvalidated(OptimisticPtr(atomic_ref(rawData->right).load(memory_order_relaxed))); }
unvalidated<lock_guard> lock() { return unvalidated(lock_guard(atomic_ref(rawData->lock).load(memory_order_seq_cst))); }
};Note that the lock() accessor returns an
unvalidated<lock_guard>: before we can hand off to
the next node’s lock, we must first validate the current guard to ensure
we actually read a valid lock. This ensures that lock acquisition itself
is part of the validated chain.
With this design, the optimistic code only accesses data via an OptimisticPtr, which makes it impossible to access the data without prior validation. This greatly improves the robustness of the approach. It is a bit annoying that we have to manually implement accessor functions in the OptimisticView, but hopefully the compiler will do that in the future automatically.
Using these abstractions, our lookup code now becomes:
option<value_type> Tree::lookup(key_type key) {
restart: lock_guard guard = lock.lock_optimistic();
auto iterOpt = guard.validate(getRootOptimistic());
if (!iterOpt) goto restart;
OptimisticPtr<Node> iter = *iterOpt;
option<value_type> result;
while (iter) {
auto currentKey = guard.validate(iter.data().key());
if (!currentKey) goto restart;
if (key == *currentKey) {
auto currentValue = guard.validate(iter.data().value());
if (!currentValue) goto restart;
result = *currentValue;
break;
}
auto next = guard.validate((key < *currentKey) ? iter.data().left() : iter.data().right());
if (!next) goto restart;
iter = *next;
auto nextGuard = guard.validate(iter.data().lock());
if (!nextGuard) goto restart;
guard = *nextGuard;
}
return result;
}The code is nearly identical to the unsafe version above, but now it becomes impossible to forget to validate, as the compiler complains otherwise. This makes this highly attractive concurrency paradigm robust and easy to use for all kinds of data structures.
In summary, Optimistic Lock Coupling gives us near-lockfree read scalability while still supporting safe concurrent writes. And by encoding validation requirements in the type system, we get the performance benefits without sacrificing correctness. The compiler catches the mistakes that would otherwise become subtle race conditions at runtime.