Memory Management

Memory management involves handling memory resources allocated for a certain task, ensuring that the memory is freed once it is no longer needed so it can be reused for some other task. If the memory is not freed in a timely manner, the system might run out of resources or incur degraded performance. A memory resource that is never freed once no longer needed is called a leak - the resource becomes unusable, usually for the duration of the process. Another issue is use after free, in which a memory resource that was already freed is used as if it wasn’t. This usually causes unexpected behavior as the code is trying to read, modify or wrongly interpret data at a memory location. Memory management can be manual - with code explicitly handling deallocation, or automatic, in which memory gets freed once no longer needed by an automated process.

Manual Memory Management

Manual memory management is efficient, since allocations and deallocations don’t incur any overhead. In C:

typedef struct _Foo {
...
} Foo;

...

Foo* foo = (Foo*)malloc(sizeof(Foo));

...

free(foo);


The disadvantage of this approach, and the main reason automatic memory management models were invented, is that this puts the developer in charge of making sure memory doesn’t leak and that it is not used after it is freed. As the complexity of the code increases, this becomes increasingly difficult. As pointers are passed around the system and get stored in various data structures, it becomes difficult to know given some pointer that is no longer needed whether: a) this was the very last piece of code that actually needed to access the location pointed to by this pointer, in which case the memory should be freed, and b) whether the memory this pointer is pointing to is still valid and hasn’t been freed previously.

Automatic Memory Management

Automatic memory management attempts to move the responsibility of tracking when a memory resource is no longer needed (and handling its deallocation) from the developer to the system. Such a system is called garbage collected, as memory that is no longer needed (“garbage”) is reclaimed by the system automatically. The two most popular methods used to automatically free memory are tracing garbage collectors and reference counting.

Tracing Garbage Collector

Tracing garbage collectors work by tracing references to objects on the heap and checking whether a given resource allocated on the heap has at least one reference path to it from the stack. If such a path exists, it means that from the stack (an argument to a function, a local variable), there is a way to perform a set of dereference and access the memory resource. If such a path doesn’t exist, it means the memory is unreachable, so regardless of how executing code accesses other objects on the heap, there is no way to access this resource - which means the memory can be safely deallocated.

For example, a naïve tracing garbage collection algorithm, mark-and-sweep, involves adding an “in-use” bit to each memory resource allocated then, during collection, following all references starting from the stack and marking each as “in-use”. Once all used resources are marked, the sweep stage involves walking the whole heap and for each memory resource, if not marked as “in-use”, freeing it.

Tracing garbage collectors are used by many popular runtimes, like JVM and .NET. In C#:

struct Bar { }

struct Foo
{
public Bar bar;
}

...

{
Foo foo = new Foo();
foo.bar = new Bar();

// there is no stack variable pointing to the Bar object, but it can
// still be reached through foo (foo.bar), so there exists a path from
// the stack to it, meaning code can still access it.
}

// foo goes out of scope which means neither foo nor its member Bar can be
// accessed any longer, so they can be safely collected


There are a couple of disadvantages with the tracing GC approach: first, the system needs to ensure memory resources are not being allocated while a garbage collection is taking place. This means code execution is paused during collection, which obviously impacts performance. The second disadvantage of this approach is that the system is not as lean as other memory management models: memory resources are kept allocated longer than really needed, for the time interval between the last reference to them goes out of scope until the actual collection is performed.

Reference Counting

An alternative to tracing garbage collectors is reference counting. As the name implies, a memory resource in such a system has an associated reference count - the number of references to it. As soon as the last reference goes out of scope, when the reference count reaches zero, the memory can be safely deallocated. Unlike tracing, reference counting is performed as code executes: the count of a given memory resource is automatically increased with each assignment where the resource is on the right-hand-side, and is automatically decreased whenever a reference goes out of scope.

Python manages memory using reference counting:

class Foo: pass

# allocate Foo, its reference count is 1
foo1 = Foo()

# reference count is 2 after assignment
foo2 = foo1

...
# once foo1 and foo2 go out of scope, reference count becomes 0 and memory
# is automatically freed


C++ smart pointers work in a similar manner:

struct Foo { };

...

// foo1 is a shared_ptr pointing to a Foo stored on the heap. Reference
// count for the Foo object is 1
auto foo1 = std::make_shared<Foo>();

// reference count becomes 2 after assignment
auto foo2 = foo1;

...
// Once foo1 and foo2 go out of scope, reference count becomes 0 and memory
// is automatically freed


The main advantages over tracing garbage collection are the fact that execution doesn’t need to be paused in order to reclaim memory and that resources are deallocated as soon as they are no longer used (once reference count becomes 0). There are also several disadvantages with this approach: first, each memory resource needs to store an additional reference count and updating the reference count in a multi-threaded environment needs to be performed atomically. Second, and most important, this memory management model does not handle reference cycles.

Reference cycles occur when two heap objects hold references to each other even after no longer being reachable from the stack. In this case, a tracing garbage collector would mark the objects as being unreachable and deallocate them, but simple reference counting would not be able to identify this - from that point of view, each object is being referred to by another object thus it should not be collected. Example of reference cycle in Python:

class Foo: pass

a, b = Foo(), Foo()
a.other, b.other = b, a
# a.other holds a reference to b, b.other holds a reference to a
# even when a and b go out of scope, the "other" attributes still hold references
# to the objects so their reference count would not drop to 0


A similar example in C++:

struct Foo
{
std::shared_ptr<Foo> other;
};

...

auto foo1 = std::make_shared<Foo>();
auto foo2 = std::make_shared<Foo>();

foo1->other = foo2;
foo2->other = foo1;
// there are two references to each Foo object: foo1 and foo2->other for the first
// object, foo2 and foo1->other for the second object. Even if the foo1 and foo2
// variables go out of scope, neither of the objects would be collected due to the
// extra reference


Python and C++ solve this problem in different ways: Python supplements reference counting with a tracing garbage collector. So while most of the memory management is done via reference counting, a tracing garbage collector is still employed to clean up cycles like in the above example. This hybrid approach has he pros and cons of both of the mechanisms discussed above. C++ avoids the execution pauses a tracing garbage collectors would create by, instead, leveraging weak references. Weak or non-owning references point to an object but do not prevent it from being collected when all strong references go away. There are several ways to express a non-owning reference, with different advantages and drawbacks:

• A & reference has to be assigned on construction and cannot be re-assigned after being bound to an object. If used after the underlying object was destroyed, it causes undefined behavior.
• A * pointer can be nullptr-initialized and assigned later or re-assigned. Similarly, if used after the pointed-to object was destroyed, causes undefined behavior.
• A weak_ptr<T> is a standard library type implementing a non-owning reference. A weak_ptr can be converted to a shared_ptr (using its lock() method). If there is no strong (shared_ptr) reference to an object it gets destroyed, regardless of how many weak_ptr instances point to it. But once a weak_ptr successfully locks an object, it creates a strong reference which ensures the object is kept alive. The drawback of using weak_ptr is additional overhead: the control block of a smart pointer needs to store both strong and weak reference count (with similar atomic reference counting), and, even if an object gets destroyed because all strong references went out of scope, the control block stays alive until all weak references go away too.

Updating the Foo struct in the example above to use a weak_ptr instead, the reference cycle is avoided:

struct Foo
{
std::weak_ptr<Foo> other;
};

...

auto foo1 = std::make_shared<Foo>();
auto foo2 = std::make_shared<Foo>();

foo1->other = foo2;
foo2->other = foo1;
// now the two Foo objects have only one strong reference to them through
// the foo1 and foo2 variables The other pointers are weak references which
// won't prevent the objects from being destroyed when foo1 and foo2 go out
// of scope


An alternative way to think about heap objects is in terms of ownership and lifetime. In this model, a heap object is uniquely owned by some other object and gets freed automatically when the owner is destructed. In C++, this is achieved through unique_ptr:

struct Foo { };

struct Bar
{
std::unique_ptr<Foo> foo { std::make_unique<Foo>(); }
}

...

{
Bar bar; // this creates a Foo object on the heap, owned by bar
}
// the heap object gets freed once bar gets freed


Ownership of the object can be transferred by moving the unique_ptr. The main advantage of this model is that it has no overhead - unlike tracing memory which involves pausing execution or reference counting which involves atomic count of references, a unique_ptr is just a wrapper over a pointer.

Unique pointers cannot be copied though (by definition, otherwise there would no longer denote unique ownership), so when other code needs to access the heap object, it would need to get a reference from the owning object:

void UseFoo(const Foo& foo)
{
...
}

...

Bar bar;

UseFoo(*bar.foo);


The problem with this approach is that if another object ends up holding on to a reference which outlives the owning object, the reference becomes dangling and refers to an object which was already freed. This becomes the equivalent of a use after free, so here is where the concept of lifetime becomes important: none of the non-owning references of a uniquely owned heap object should outlive the object.

Unfortunately in C++ this has to be handled through sensical design and is mostly left up to the developer. Rust on the other hand provides strong static analysis and lifetime annotations to ensure such issues do not occur. In fact, the default in Rust is to have uniquely owned objects which can be “borrowed” when needed and static analysis ensures no dangling references appear. In C++:

struct Foo { };

struct Bar{
Foo* foo;
};

...

Bar bar;
{
Foo foo;
bar.foo = *foo;
}
// bar.foo is now a dangling pointer since Foo was freed


The above example used a pointer for simplicity, since a & reference (Foo&) needs to be bound at construction time, but same applies for that type of reference: once an object gets freed, & references and non-owning pointers to it are left dangling. On the other hand, this does not compile in Rust:

struct Foo {
}

struct Bar<'a> {
foo: &'a Foo
}

...

let bar;
{
let foo = Foo {};
bar = Bar { foo: &foo };
}
// compiler correctly shows foo dropped here while still borrowed


In Rust, the compiler ensures dangling references (“borrowed” objects) do not exist once owning object goes out of scope.

It seems that in most cases, the best approach to memory management is to use the latter model of ownership and lifetimes which comes with no runtime overhead and handle the dangling reference problem through static analysis. The advantages of this approach extend beyond the runtime cost of other automatic memory management techniques to a model which also works well in a multi-threaded environment, eg. if we only allow the owner of an object to modify it, we can eliminate certain data races. From a systems design perspective it is also an advantage to have a clear understanding of ownership throughout the system.

Summary

This post covered several memory management techniques, outlining their pros and cons:

• Manual - human error prone
• Automatic using a tracing garbage collector - safe but comes with runtime overhead
• Automatic using reference counting - smaller runtime cost than a tracing garbage collector but needs additional mechanisms to deal with reference cycles
• Concepts of ownership and lifetime - no runtime overhead, but should be supplemented by static analysis to avoid dangling references