mpl VS samsara

I'm one of the authors of this work -- I can explain a little.

"Provably efficient" means that the language provides worst-case performance guarantees.

For example in the "Automatic Parallelism Management" paper (https://dl.acm.org/doi/10.1145/3632880), we develop a compiler and run-time system that can execute extremely fine-grained parallel code without losing performance. (Concretely, imagine tiny tasks of around only 10-100 instructions each.)

The key idea is to make sure that any task which is *too tiny* is executed sequentially instead of in parallel. To make this happen, we use a scheduler that runs in the background during execution. It is the scheduler's job to decide on-the-fly which tasks should be sequentialized and which tasks should be "promoted" into actual threads that can run in parallel. Intuitively, each promotion incurs a cost, but also exposes parallelism.

In the paper, we present our scheduler and prove a worst-case performance bound. We specifically show that the total overhead of promotion will be at most a small constant factor (e.g., 1% overhead), and also that the theoretical amount of parallelism is unaffected, asymptotically.

All of this is implemented in MaPLe (https://github.com/mpllang/mpl) and you can go play with it now!

Maple is a fork of MLton: https://github.com/MPLLang/mpl

Some of us are still using SML for research and teaching, e.g. https://github.com/mpllang/mpl

> IME it's the other way around, per-object individual lifetimes is a rare special case

It depends on your application domain. But in most cases where objects have "individual lifetimes" you can still use reference counting, which has lower latency and memory overhead than tracing GC and interacts well with manual memory management. Tracing GC can then be "plugged in" for very specific cases, preferably using a high performance concurrent implementation much like https://github.com/chc4/samsara (for Rust) or https://github.com/pebal/sgcl (for C++).

> Just for example: "it needs a GC" could be the heart of such an argument

Rust can actually support high-performance concurrent GC, see https://github.com/chc4/samsara for an experimental implementation. But unlike other languages it gives you the option of not using it.

The compiler support you need is quite limited. Here's an implementation of cycle collection in Rust: https://github.com/chc4/samsara It's made possible because Rust can tell apart read-only and read-write references (except for interior mutable objects, but these are known to the compiler and references to them can be treated as read-write). This avoids a global stop-the-world for the entire program.

Cascading deletes are rare in practice, and if anything they are inherent to deterministic deletion, which is often a desirable property. When they're possible, one can often use arena allocation to avoid the issue altogether, since arenas are managed as a single object.

There are concurrent GC implementations for Rust, e.g. Samsara https://redvice.org/2023/samsara-garbage-collector/ https://github.com/chc4/samsara that avoid blocking, except to a minimal extent in rare cases of contention. That fits pretty well with the pattern of "doing a bit of GC every frame".

There are a number of efforts along these lines, the most interesting is probably Samsara https://github.com/chc4/samsara https://redvice.org/2023/samsara-garbage-collector/ which implements a concurrent, thread-safe GC with no global "stop the world" phase.

Nice blog post! I also wrote a concurrent reference counted cycle collector in Rust (https://github.com/chc4/samsara) though never published it to crates.io. It's neat to see the different choices that people made implementing similar goals, and dumpster works pretty differently from how I did it. I hit the same problems wrt concurrent mutation of the graph when trying to count in-degree of nodes, or adding references during a collection - I didn't even think of doing generational references and just have a RwLock...