Standard ML Parallel Projects

mpl

7 285 8.4 Standard ML

The MaPLe compiler for efficient and scalable parallel functional programming

Project mention: Garbage Collection for Systems Programmers | news.ycombinator.com | 2024-03-30

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!

sml-parseq

1 4 4.5 Standard ML

parallel sequences library in Standard ML
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NOTE: The open source projects on this list are ordered by number of github stars. The number of mentions indicates repo mentiontions in the last 12 Months or since we started tracking (Dec 2020).