Hi all,
I am interested in using miniKanren to solve some large constraint based
models. The models are of biological networks that are currently
implemented in MiniZinc <https://www.minizinc.org/> and solved using
OR-tools <https://github.com/google/or-tools> or Chuffed
<https://github.com/chuffed/chuffed>. The models are very under determined
because of the sparsity of data and how the network dynamics are modeled,
thus it is expected that many solutions exist for any given model.
To give an example of the scale that we are dealing with, using 100 cores
(via a distributed version of Chuffed) it finds ~400 solutions in 48 hours.
These solutions are great, but are limited by the MiniZinc specification
and the complexity of the solvers. miniKanren is appealing because of the
straightforward implementation of the solvers (and it has many
implementations in Lisp, huge plus in my book).
My questions are:
- Would it be possible to get somewhat similar performance using a
parallel/distributed miniKanren implementation?
- I know this is difficult to judge without seeing the nuts and bolts
of the current model, but any intuition as to the scalability of
miniKanren
would be greatly appreciated.
- Are there any parallel/distributed miniKanren implementations?
- If not, could anyone give me an estimate of how difficult it would be
to parallelize miniKanren? (e.g. afternoon, week, month, PhD thesis)
- I am also aware that difficulty is hard to judge because it is
based on my proficiency, but again any intuition would be appreciated.
(Thinking of this xkcd,
https://xkcd.com/1425/)
<https://xkcd.com/1425/>
- From William Byrd's SO answer here,
https://stackoverflow.com/a/28556223/1947159, he says that it is at least
theoretically trivially parallelizable, which gives me hope.
Please let me know if any clarification is needed.
Thank you,
Cole
If you would like to learn more about the formalism that our networks take,
you can refer to this paper:
https://bmcsystbiol.biomedcentral.com/articles/10.1186/s12918-018-0599-1
Note that this paper uses one of the smaller networks (solved in serial in
a matter of seconds using Chuffed) and we are looking to expand the number
of interactions in the network that we can model.
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