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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!
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> 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++).
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> 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++).
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For (2) Virgil has several features that allow you to layout memory with various levels of control. I assume you meaning "array of structs", and you can do that with arrays of tuples, which will naturally be flattened and normalized based on the target (i.e. will be array-of-structs on native targets). You can define byte-exact layouts[1] (mostly for interfacing with other software and parsing binary formats), unbox ADTs, and soon you can even control the exact encoding of ADTs.
Virgil is GC'd.
[1] https://github.com/titzer/virgil/blob/master/doc/tutorial/La...
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completely-unscientific-benchmarks
Naive performance comparison of a few programming languages (JavaScript, Kotlin, Rust, Swift, Nim, Python, Go, Haskell, D, C++, Java, C#, Object Pascal, Ada, Lua, Ruby)
Not true. Visit the repositorium and see the benchmark results: https://github.com/frol/completely-unscientific-benchmarks
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