Peter Liniker

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An Experiment in Garbage Collection

Reinventing Garbage Collection Problems from First Principles by Aiming Way High.

Mo-gc is an experiment in garbage collection written in the Rust programming language.

Instead of scanning the stack, the mutator writes reference count increments and decrements to a journal. The journal is read concurrently by a garbage collection thread that keeps a map of objects and their absolute reference counts. The object map is divided into young and mature generations and collection is done with parallellized mark and sweep phases.

The journal is a type of snapshot-at-beginning write barrier and this project was an experiment in the feasibility, limitations and scalability of this approach.

In brief conclusion, this project was ambitions and fell short but I learned some of the hard lessons of garbage collector implementation.

This article traces my thought process and implementation from beginning to time of writing.

Contents

Irrational Exuberance, or How This Project Got Started

Early in 2015, Nick Fitzgerald published Oxischeme. With a general interest in programming languages and runtimes and a specific interest in Rust, I had been following Rust’s progress towards 1.0 with eager anticipation. At the time, Oxischeme was notable as the only published and documented language runtime written in Rust that could readily be found.

Oxischeme contains a garbage collector written in Rust because Rust itself has no garbage collector. Most hobby interpreters are built on runtimes that provide a garbage collector for free. Even more interesting, though, Nick’s article concluded with a link to David F. Bacon’s (et al) A Unified Theory of Garbage Collection.

This paper was fascinating. I had often wondered at the stark difference in apparent complexity between reference counting and tracing collectors and how distant they seemed from each other yet had the same ultimate aims. This paper made that world smaller.

Given that David F. Bacon is credited with successful garbage collectors based around some form of reference counting and given his Unified Theory, I decided I could ignore the poor reputation of reference counting and contemplate it without feeling like it was a well explored dead end in memory management.

At the end of a week of being highly distracted at all times of day with various mental visualizations of reference counting combined with tracing I felt I had some sort of idea that I hadn’t seen anywhere before. What if a mutator could run pauselessly by keeping a journal of reference count increments and decrements that a GC thread would read and reconstruct into the absolute reference count?

Since vowing (probably unreasonably) never to write C or C++ ever again several years earlier, and since my main competence was in Python, I would have to wait until I felt comfortable enough in Rust to begin experimenting.

The idea sat patiently on the back seat until one day in August I was struck with irrational exuberance about it and decided to write a draft RFC for feedback, as I was, after all, making this all up in a vacuum, yet excited about an idea I thought realistic.

Preparing the RFC to be taken seriously meant wider reading: mostly Bacon’s reference counting papers and patents, general garbage collection theory and concurrent data structures.

At the time, I thought the mechanism could only work for immutable/persistent data structures; the most convincing feedback I received was that this would be too restrictive. Quite likely nobody with any serious garbage collection experience paid any attention to the RFC or I might have been directed back to the drawing board!

Thinking the mechanism through for mutable object graphs now occupied the back of my mind while I began work on the basic data structure I would regardless need: a bitmapped vector trie. Whatever Rust I had played with until now taught me little compared with implementing this data structure, where I had to come to know unsafe and the borrow checker.

It took until Christmas to get bitmaptrie to a place where it was sufficiently correct and featured to begin to use. That seems like a long time. I am a slow but thorough learner. And I mostly only had late evenings.

The bitmapped vector trie uses word-sized indeces and is therefore O(log_WORDBITS n) access. It includes a last-access path cache which can speed up lookups on spatially dense indexed entries.

My goal for this project was to make the code performant, using parallelism where possible. The more performant the individual components were, the more the inherent bottlenecks in the overall system would stand out. In time I added the ability to shard a trie into mutable sub-tries, each of which would be independently updated in parallel.

Summary of the Design

Motivation: Hosting Languages

In the previous section I mentioned vowing never to write C or C++ again. Rust exists to address the very reasons I’d come to dislike those languages. I also mentioned an interest in programming languages and runtimes.

If a higher level programming language is not hosted in itself, there is a very high chance that it is written in C or C++. By a degree of necessity, lower level interaction or optimized extensions of those runtimes must also be in C or C++, perpetuating the pervasiveness of these two languages.

I believe that if Rust is to be ultimately pervasive one day, it must itself host runtimes for languages that are more accessible, just as Python and C are currently a popular combination. (As an aside, Julia is a notable outlier that, while the runtime is written in C, does not necessarily require performance sensitive extensions to be written in C.)

The mo-gc experiment is motivated by the safety benefits of Rust over C and C++ to explore a programming language runtime written in Rust, with the ultimate aim to spread the safety that Rust encourages.

Many new programming languages seem to start with a syntax and semantics wishlist, leaving the runtime with a basic garbage collector as a second-class necessity that will eventually be optimized. As a garbage collector is a foundational requirement for most language runtimes, it may make some sense to begin there rather than deferring the problem of memory management.

Garbage Collection and Rust

As Felix S. Klock II has already written a thorough introduction to the challenges involved in integrating a garbage collector with Rust, I will not repeat what I cannot improve on.

The primary barrier to writing an effective garbage collector in and/or for Rust is the current lack of Rust compiler awareness of garbage collection needs. I understand that this is in the research phase and that some proposals may be announced this year.

The two key features that aren’t natively available are stack scanning and type maps. Because I was planning on using a journal to push stack information to the GC thread, I wouldn’t need stack scanning. I could work around the lack of type maps by giving each type it’s own tracing method.

The third question concerned ergonomics. I did not necessarily want memory management to be too distant from the host language. Oxischeme is hosted in Rust and has an arena based mark-and-sweep garbage collector, with different arenas for different object types. This is fine for the runtime it is integrated with, but far less ergonomic for more general use in Rust.

As a consequence, I decided to follow the lead of SpiderMonkey’s relationship with Servo, in that smart pointers are required to root and unroot objects. Some ergonomics are sacrificed here, but the tradeoff is already familiar.

Inside mo-gc

Tracing Concurrently

Without type maps to rely on, every object that wishes to participate in being GC managed must implement a trait:

unsafe trait Trace {
    fn traversible(&self) -> bool;
    fn trace(&self, stack: &mut TraceStack);
}

The GC thread does not know the absolute type of every object it is managing, so these methods, when called from the GC thread, are inevitably virtual function calls.

The traversible() method must return true if the object may refer to other GC-managed objects. This method is called from the mutator and the value passed through the journal to the GC as a bit flag.

By calling traversible() on the mutator side where the absolute type is known, the virtual function call on the GC thread side can be avoided, and optimized away on the mutator side if the value is a literal, which it generally would be.

This also allows the GC thread to avoid a virtual function call to trace() when the traversible flag is false.

The trace() method takes a parameter of type TraceStack which, as its name implies, is the stack of objects buffered for tracing (or the list of gray objects in a tri-color equivalent scheme.) The trace() method should call stack.push(object) for every object that it refers to.

The implementation of trace(), since it is called from the GC thread concurrently with the mutator running, must be thread safe. Any mechanism may be used, even locks if necessary. Because the thread safeness cannot be guaranteed by the compiler, just as with the Sync trait Trace is an unsafe trait.

The Journal

The journal behaves as a non-blocking unbounded queue. It is implemented as an unbounded series of one-shot single-writer SPSC buffers, making it very fast.

Testing on a Xeon E3-1271 gives a throughput of about 500 million two-word objects per second between a producer thread and a consumer thread, although that is a micro-benchmark and therefore to be taken as probably real-world unrealistic.

The type that the mutator writes to the journal is almost identical to a TraitObject with one difference: the low pointer bits are used as flags.

struct Entry {
    ptr: usize,
    vtable: usize
}

Flags used are:

Journal entries are read into a young generation heap map that keeps track of all stack roots.

Reference count decrement entries are not immediately applied, though: they are buffered to be applied after the current collection (mark and sweep) cycle is completed. This makes this design essentially snapshot-at-beginning with new objects automatically marked “black” in the tri-color notation.

The Heap Maps

There are two heap maps, a young and a mature generation, each implemented using a separate bitmapped vector trie.

The young generation heap map doubles as the root set reference count map.

Collecting the young generation is implemented by sharding the trie into at least as many immutable parts as there are CPUs available to parallelize tracing in a thread pool. Each shard is scanned for non-zero reference counted objects and all non-newly-allocated objects (marked black on allocation). They form the first set of gray objects, which are traced to find more gray objects to add to the trace stack.

During marking, each thread has it’s own trace stack, avoiding the need to synchronize between threads, but making it possible that two or more threads might attempt to trace the same object concurrently.

For sweeping, the heap is sharded mutably across the thread pool, with each shard being swept concurrently with others.

Since there are two distinct categories of objects in the young generation map: reference counts for mature objects and counted or uncounted newly allocated objects. Only newly allocated object entries are candidates for sweeping as the mature heap owns mature objects. This distinction does not exist in the mature generation.

I had originally thought that since the root set would include objects in the mature generation that this would suffice as a precise remembered set. When tracing the young generation, the root set would simply be all pointers, new or mature, with a positive reference count.

The invariant required in a generational garbage collector is that:

| Every live mature object that points to a live object in the young generation must be discoverable and considered a root. | |-|

Typically a generational garbage collector will implement a remembered set or a card table that is updated with a write barrier to discover these roots.

My original assumption does not uphold the invariant since mature generation objects that are stack roots may point indirectly to young generation objects. My implementation does not take indirect mature generation roots into consideration, making the remembered set incomplete. The result is that some object graph modifications may result in live objects being freed. But more on that later.

Using mo-gc

Usage is superficially straightforward, as this basic example demonstrates:

extern crate mo_gc;

use mo_gc::{GcRoot, GcThread};


fn app() {
    let something = GcRoot::new(String::from("I am a GC owned string"));
    println!("String says {}", *something);
}


fn main() {
    let gc = GcThread::spawn_gc();

    let handle = gc.spawn_app(|| app());

    handle.join().expect("app thread failed");
    gc.join().expect("gc thread failed");
}

When the time comes to implement a data structure, the Trace trait comes into play. The example below illustrates the basic API usage:

extern crate mo_gc;

use mo_gc::{Gc, GcRoot, GcThread, Trace, TraceStack};


struct Node {
    next: Gc<Node>,
}


unsafe impl Trace for Node {
    fn traversible(&self) -> bool {
        true
    }

    fn trace(&self, stack: &mut TraceStack) {
        if let Some(ptr) = self.next.as_raw() {
            stack.push_to_trace(&*ptr);
        }
    }
}

Because the mutator thread runs in parallel with the GC thread, the immediate question that must be asked is “is this data structure and the trace() function thread safe?”

As long as the data structure itself is not mutably aliased, only the trace() function’s behavior is significant. It must essentially provide a snapshot of the data structure’s contents to the GC thread. At best it is challenging to prove thread safety and because of other problems described later I did not begin to implement any data structures.

Performance and Behavior

Measures

Points of garbage collection performance interest are:

In the case of mo-gc, maximum latency is close to the speed of allocation.

A brief list of test cases and their descriptions is given here:

Test Description
1 tight loop allocating 25,000,000 8-byte objects
2 as test 1 but with 50ms pause every 4096 allocations

Some rudimentary results, conducted on an 8-core Xeon E3-1271, are listed below:

Test Allocs/sec Mut wall-clock GC deallocs/sec GC CPU time
1 22,400,000 1115ms 10,200,000 2460ms
2 81,000 30,800ms 2,000,000 1200ms

In the first test case, the mutator gets near 100% of a CPU as the GC is not running on all eight cores at all times. The GC and mutator threads do spend a significant portion of time contending in the allocator - the mutator allocating and the GC thread deallocating.

The second test shows a GC performance of 20% the deallocation rate of that in the first test. This is due to the lack of tuning of when a collection should occur. Currently a collection is made every time the journal returns non-empty, but in test 2 the number of journal entries per collection is low, reducing efficiency.

The contention between the mutator and the GC in the allocator is low in test 2, though. Just how bad the contention is in test 1 is shown by how much more GC CPU time test 1 requires than test 2.

Overall, the CPU burden relative to the mutator is unscalably high.

Qualitative Summary of Performance

  1. Since the journal is a form of write barrier, where every rooting, unrooting and new object must be journaled, it is undoubtable that this implementation is less efficient than an incremental garbage collector where a write barrier is also required, which in turn is less efficient than non-incremental stop-the-world where no write barrier is needed.

  2. The journal itself appears to scale somewhat, at least with x86’s memory ordering. Writing a two-word struct to the journal adds roughly 25% to the cost of allocating a 64 byte object on the heap.

    Since Rust’s borrow mechanism may be used to alleviate unnecessary root reference count adjustments (just as an Rc<T> may be borrowed rather than cloned) in real world applications it is possible that the journal write barrier cost may be ameliorated some.

  3. Rather than using a custom allocator, the object map is implemented using a bitmapped trie with compressed nodes and a path cache. This is somewhat slower than a custom allocator might allow as the trie requires multiple pointer indirections on every access. A custom allocator can use a bitmap, for example, as the mark flags.

  4. The parallel mark and sweep phases and the journal itself are sufficiently performant that the throughput bottleneck in the system is very evident: processing the journal into the object map. With a mutator thread allocating new objects in a tight loop, the GC thread’s throughput is about half the rate at which they are allocated. This is very unscalable and, in performance terms, is the most obviously flawed part of the overall design.

  5. Requiring data structures managed by the GC to be concurrent, or at minimum provide a concurrency-safe trace() implementation, may be fraught with pitfalls.

The Journal as a type of Write Barrier

There is a use-after-free condition in the current implementation where, during the mark phase of collection, the mutator reads a pointer from the heap, roots it, and then overwrites the heap location with a new pointer or null before the heap location has been traced. The object pointed to has been rooted and a journal entry been written, but the mark phase is not reading the journal at this point. The sweep phase will then drop the object leaving the mutator in a use-after-free state.

This means that the mutator threads cannot currently use mo-gc in it’s present form as fully general purpose, or rather that data structures must be persistent or designed to avoid this scenario.

The fix is not obvious. At first it may seem that we just need to read journal entries that were written during the mark phase and trace those too. But we end up back where we started, as that itself is a mark phase and we then need to repeat the operation potentially indefinitely.

The problem that must be solved looks like this:

  1. object LittleCatA contains a reference to LittleCatB, which in turn refers to LittleCatC all the way through in a linked list to LittleCatZ
  2. the mutator has rooted LittleCatA
  3. the GC enters the mark phase and begins tracing objects
  4. before LittleCatA is traced, the pointer to LittleCatB is popped off and replaced with null
  5. the mutator roots LittleCatB by writing an entry to the journal
  6. the GC traces LittleCatA and finds nothing inside
  7. the GC enters the sweep phase, dropping LittleCatB all the way through LittleCatZ

This is essentially the problem that incremental garbage collectors solve with a write barriers that sends the pointer value that the mutator is about to overwrite to a buffer. The GC reads the buffer and marks all objects therein gray. Synchronization is also required to stop the mutator from adding to the buffer while the GC completes the mark phase.

The Remembered Set

If an object in the mature generation is rooted and by way of indirection points at an object in the young generation, that mature object root is insufficient in mo-gc to result in the young object being marked. The young object, if not reachable also in the young generation, will be freed.

This problem is solved in generational garbage collectors with a write barrier that writes the mature object address to a remembered set. The remembered set is used as an additional set of roots when tracing the young generation.

Conclusions

First, a question: can this design be made to work?

With synchronization points between the mutator and GC threads, yes. Extending the journal to include the write barrier functions of generational and incremental garbage collectors would be sufficient to provide coherence between the mutator and GC threads.

But is it worth it? While this design cannot be truly pauseless (some synchronization is always needed), the mutator pauses might still be insignificant enough to make this design worth considering. However, the performance overhead of maintaining the reference counted root set data structure is too significant to ignore. The performance of the Very Concurrent Garbage Collector may be instructive.

In conclusion, I aimed way high and missed. But in aiming so high I experienced the same problems that have been solved decades ago and I learned why those problems exist and why the solutions are what they are. I also learned a great deal of Rust. Most of all, this has been a hugely enjoyable and rewarding deep dive into garbage collection. It was worth it.

Further Reading