The Dyon Tutorial says it uses "lifetimes" rather than garbage collection or manual memory management. But how then does that lifetime model differ from Ownership in Rust?
Dyon has a limited memory model because of the lack of a garbage collector. The language is designed to work around this limitation. - The Dyon Programming Language Tutorial
How exactly is this model limited? Is there an example of memory managing code that Dyon could not run because of this limitation?
The linked Dyon book contains an explanation to just that:
Lifetimes are about references
A lifetime is about the references stored inside a variable. All references outlive variables they are stored in. Variables can not store references to themselves, because it can not outlive itself.
In order to put a reference inside a variable, the lifetime checker must know that the reference outlives the variable.
Because of the lifetime checker, all memory in Dyon is an acyclic graph.
Therefore, the main limitation is that references cannot make any cycles. That is, it is not possible to represent circular node lists or having a child object keep a reference to its parent.
These limitations also apply to Rust, with the exception that Rust also provides workarounds. Reference-counted types (Rc and Arc), in combination with weak references (see std::rc::Weak), can create circular references. Cycles can also be made behind unsafe constructs, namely raw pointers.
See also (Rust specific, but most principles apply):
Why can't I store a value and a reference to that value in the same struct?
Related
Would it be possible for a programming language to consistently have Rust-style ownership and lifetimes (for automatic memory management) while dropping the requirement that only one mutable reference to a piece of data can exist at any time (used to suppress data races)?
In other words, are Rust-style ownership and lifetimes and Rust-style borrow checking two separable concepts? Alternatively, are these two ideas inherently entangled at a semantic level?
Would it be possible for a programming language to consistently have Rust-style ownership and lifetimes (for automatic memory management) while dropping the requirement that only one mutable reference to a piece of data can exist at any time (used to suppress data races)?
A language can do anything so sure.
The problem is that dropping this requirement would be an UB-nest in a language like Rust: if you drop mutable references being unique then they have no purpose so you just have references (always mutable) and the only thing they do is be lexically scoped, this means you can hold a reference to a sub-part of an object, and have a second reference mutate the object such that the sub-part is invalidated (e.g. a reference to a vec item and clearing the vec[0]), and the first reference is now dangling, it points to garbage.
The way to solve that would be to… add a GC? And from that point on the value of "rust-style ownership and references" becomes… limited to nonexistent, because you need a GC non-lexical automated memory management and your references can keep objects alive so having all types be affine by default isn't very useful.
Now what can be useful (and what some languages explore) is for sub-normal types to be opt-in, so types would be normal by default but could be opted into being affine, linear, or even ordered, on a needs basis. This would be solely a type safety measure.
If so, are there any existing languages which achieve this?
Not to my knowledge.
If not, why not?
Because nobody's written one? Affine types by default are useful to Rust but they're not super useful in general so most of the research and design has focused around linear types, which provide more guarantees and are therefore more useful if only a small subset of your types are going to be sub-normal.
[0] which shows that "data races" are not solely about concurrency, it's an entire class of problems which occur commonly in sequential code (e.g. iterator invalidation)
For example, an immutable CFString can store the length and the character data in the same block of memory. And, more generally, there is NSAllocateObject(), which lets you specify extra bytes to be allocated after the object’s ivars. The amount of storage is determined by the particular instance rather than being fixed by the class. This reduces memory use (one allocation instead of two) and improves locality of reference. Is there a way to do this with Swift?
A rather later reply. 😄 NSAllocateObject() is now deprecated for some reason. However, NSAllocateObject() is really a wrapper around class_createInstance which is not deprecated. So, in principle, one could use this to allocate extra bytes for an object instance.
I can't see why this wouldn't work in Swift. But accessing the extra storage would be messy because you'd have to start fooling around with unsafe pointers and the like. Moreover, if you're not the author of the original class, then you risk conflicting with Apple's ivars, especially in cases where you might be dealing with a class cluster which could potentially have a number of different instance sizes, according to the specific concrete implementation.
I think a safter approach would be to make use of objc_setAssociatedObject and objc_getAssociatedObject, which are accessible in Swift. E.g. Is there a way to set associated objects in Swift?
This page has been quite confusing for me.
It says:
Memory management in newLISP does not rely on a garbage collection algorithm. Memory is not marked or reference-counted. Instead, a decision whether to delete a newly created memory object is made right after the memory object is created.
newLISP follows a one reference only (ORO) rule. Every memory object not referenced by a symbol is obsolete once newLISP reaches a higher evaluation level during expression evaluation. Objects in newLISP (excluding symbols and contexts) are passed by value copy to other user-defined functions. As a result, each newLISP object only requires one reference.
Further down, I see:
All lists, arrays and strings are passed in and out of built-in functions by reference.
I can't make sense of these two.
How can newLISP "not rely on a garbage collection algorithm", and yet pass things by reference?
For example, what would it do in the case of circular references?!
Is it even possible for a LISP to not use garbage collection, without making performance go down the drain? (I assume you could always pass things by value, or you could always perform a full-heap scan whenever you think it might be necessary, but then it seems to me like that would insanely hurt your performance.)
If so, how would it deal with circular references? If not, what do they mean?
Perhaps reading http://www.newlisp.org/ExpressionEvaluation.html helps understanding the http://www.newlisp.org/MemoryManagement.html paper better. Regarding circular references: they do not exist in newLISP, there is no way to create them. The performance question is addressed in a sub chapter of that memory management paper and here: http://www.newlisp.org/benchmarks/
May be working and experimenting with newLISP - i.e. trying to create a circular reference - will clear up most of the questions.
Does anyone know of a GC algorithm which utilises type information to allow incremental collection, optimised collection, parallel collection, or some other nice feature?
By type information, I mean real semantics. Let me give an example: suppose we have an OO style class with methods to maintain a list which hide the representation. When the object becomes unreachable, the collector can just run down the list deleting all the nodes. It knows they're all unreachable now, because of encapsulation. It also knows there's no need to do a general scan of the nodes for pointers, because it knows all the nodes are the same type.
Obviously, this is a special case and easily handled with destructors in C++. The real question is whether there is way to analyse types used in a program, and direct the collector to use the resulting information to advantage. I guess you'd call this a type directed garbage collector.
The idea of at least exploiting containers for garbage collection in some way is not new, though in Java, you cannot generally assume that a container holds the only reference to objects within it, so your approach will not work in that context.
Here are a couple of references. One is for leak detection, and the other (from my research group) is about improving cache locality.
http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4814126
http://www.cs.umass.edu/~emery/pubs/06-06.pdf
You might want to visit Richard Jones's extensive garbage collection bibliography for more references, or ask the folks on gc-list.
I don't think it has anything to do with a specific algorithm.
When the GC computes the graph of objects relationship, the information that a Collection object is sole responsible for those elements of the list is implicitly present in the graph if the compiler was good enough to extract it.
Whatever the GC algorithm chosen: the information depends more on how the compiler/runtime will extract this information.
Also, I would avoid C and C++ with GC. Because of pointer arithmetic, aliasing and the possibility to point within an object (reference on a data member or in an array), it's incredibly hard to perform accurate garbage collection in these languages. They have not been crafted for it.
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In a lay-man terminology how does the garbage collection mechanism work?
How an object is identified to be available for garbage collection?
Also, what do Reference Counting, Mark and Sweep, Copying, Train mean in GC algorithms?
When you use a language with garbage collection you wont get access to the memory directly. Rather you are given access to some abstraction on top of that data. One of the things that is properly abstracted away is the the actual location in memory of the data block, as well as pointers to other datablocks. When the garbage collector runs (this happens occasionally) it will check if you still hold a reference to each of the memory blocks it has allocated for you. If you don't it will free that memory.
The main difference between the different types of garbage collectors is their efficiency as well as any limitations on what kind of allocation schemes they can handle.
The simplest is properly reference counting. When ever you create a reference to an object an internal counter on that object is incremented, when you chance the reference or it is no longer in scope, the counter on the (former) target object is decremented. When this counter reaches zero, the object is no longer referred at all and can be freed.
The problem with reference counting garbage collectors is that they cannot deal with circular data. If object A has a reference to object B and that in turn has some (direct or indirect) reference to object A, they can never be freed, even if none of the objects in the chain are refereed outside the chain (and therefore aren't accessible to the program at all).
The Mark and sweep algorithm on the other hand can handle this. The mark and sweep algorithm works by periodically stopping the execution of the program, mark each item the program has allocated as unreachable. The program then runs through all the variables the program has and marks what they point to as reachable. If either of these allocations contain references to other data in the program, that data is then likewise marked as reachable, etc.
This is the mark part of the algorithm. At this point everything the program can access, no matter how indirectly, is marked as reachable and everything the program can't reach is marked as unreachable. The garbage collector can now safely reclaim the memory associated with the objects marked as unreachable.
The problem with the mark and sweep algorithm is that it isn't that efficient -- the entire program has to be stopped to run it, and a lot of the object references aren't going to change.
To improve on this, the mark and sweep algorithm can be extended with so called "generational garbage collection". In this mode objects that have been in the system for some number of garbage collections are promoted to the old generation, which is not checked that often.
This improves efficiency because objects tend to die young (think of a string being changed inside a loop, resulting in perhaps a lifetime of a few hundred cycles) or live very long (the objects used to represent the main window of an application, or the database connection of a servlet).
Much more detailed information can be found on wikipedia.
Added based on comments:
With the mark and sweep algorithm (as well as any other garbage collection algorithm except reference counting) the garbage collection do not run in the context of your program, since it has to be able to access stuff that your program is not capable of accessing directly. Therefore it is not correct to say that the garbage collector runs on the stack.
Reference counting - Each object has
a count which is incremented when
someone takes a reference to the
object, and decremented when someone
releases the reference. When the reference count goes to zero, the object is deleted. COM uses
this approach.
Mark and sweep - Each object has a flag if it is in use. Starting at the root of the object graph (global variables, locals on stacks, etc.) each referenced object gets its flag set, and so on down the chain. At the end, all objects that are not referenced in the graph are deleted.
The garbage collector for the CLR is described in this slidedeck. "Roots" on slide 15 are the sources for the objects that first go into the graph. Their member fields and so on are used to find the other objects in the graph.
Wikipedia describes several of these approaches in much more and better detail.
Garbage collection is simply knowing if there is any future need for variables in your program, and if not, collect and delete them.
Emphasis is on the word Garbage, something that is completely used out in your house is thrown in the trash and the garbage man handles it for you by coming to pick it up and take it away to give you more room in your house trash can.
Reference Counting, Mark and Sweep, Copying, Train etc. are discussed in good detail at GC FAQ
The general way it is done is that the number of references to an object are kept track of in the background, and when that number goes to zero, the object is SUBJECT TO garbage collection, however the GC will not fire up until it is explicitly needed because it is an expensive operation. What happens when it starts is that the GC goes through the managed area of memory and finds every object that has no references left. The gc deletes those objects by first calling their destructors, allowing them to clean up after themselves, then frees the memory. Commonly the GC will then compact the managed memory area by moving every surviving object to one area of memory, allowing more allocations to take place.
Like i said this is one method that i know of, and there is a lot of research being done in this area.
Garbage collection is a big topic, and there are a lot of ways to implement it.
But for the most common in a nutshell, the garbage collector keeps a record of all references to anything created via the new operator, even if that operator's use was hidden from you (for example, in a Type.Create() method). Each time you add a new reference to the object, the root of that reference is determined and added to the list, if needed. A reference is removed whenever it goes out of scope.
When there are no more references to an object, it can (not "will") be collected. To improve performance and make sure necessary cleanup is done correctly, collections are batched for several objects at once and happen over multiple generations.