clingo VS libclc

One of the easiest to get started on Datalog in my opinion is really clingo https://potassco.org/clingo/ , which can be pip installed and has python bindings. Answer Set Programming goes beyond datalog, but it holds datalog semantics as a sublanguage. It is unfortunate this is not well advertised.

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Love this article and the push to build awareness of what modern SAT solvers can do.

The thing it misses, though, is that there are higher level abstractions that are far more accessible than SAT. If I were teaching a course on this, I would start with either Answer Set Programming or Satisfiability Modulo Theories (SMT). The most widely used solvers for those are clingo [0] and Z3 [1]:

With ASP, you write in a much clearer Prolog-like syntax that does not require nearly as much encoding effort as your typical SAT problem. Z3 is similar -- you can code up problems in a simple Python API, or write them in the smtlib language.

Both of these make it easy to add various types of optimization, constraints, etc. to your problem, and they're much better as modeling languages than straight SAT. Underneath, they have solvers that leverage all the modern CDCL tricks.

We wrote up a paper [2] on how to formulate a modern dependency solver in ASP; it's helped tremendously for adding new types of features like options, variants, and complex compiler/arch dependencies to Spack [3]. You could not get good solutions to some of these problems without a capable and expressive solver.

[0] https://github.com/potassco/clingo

Answer Set Programming is an incredibly powerful tool to declaratively solve combinatorial problems. Clingo is one of the best open source implementations in my opinion: https://github.com/potassco/clingo

This is something I planned (2015) on sharing at some point but then years flew by and here we are .. :}

It is a cacheline sized 'container' (CLC) of machine-word length records, with one record used to store the record order and remaining bits for metadata. So you can project different kinds of container semantics, such as FIFO or LRU -- any ordered set semantics -- on this meta record. Using arrays of CLCs you can create e.g. a segmented LRU, where the overhead per item is substantially less than a conventional pointer-based datastructure, and, is naturally suited for concurrent operations (for example by assigning a range to distinct worker threads), and ops require a few or couple of lines to be touched. The LRU (or whatever) semantics in aggregate will be probabilistic, as the LRU order is deterministic per unit container only. It is very fast :)

https://github.com/alphazero/libclc/blob/master/include/libc...

https://github.com/alphazero/libclc/blob/master/include/libc...

As for the non-deterministic aspects: Since container semantics e.g. LRU order is only applied at unit level, the overall cache is ~LRU. We can strictly quantify the 'ordering error' by observing the age of items in FIFO mode as they are removed: for a deterministic container the age of the item is equal to the total capacity of the queue, for a segmented (array) composed of FIFO queues, the age will have a effectively gaussian distribution around the capacity (number of units x unit capacity). But since these containers can use as few as 9 bits per entry vs 24 or more bytes for pointer based solutions (which use linked-lists), for the same allocation of memory, the capacity of the array of CLCs will be much greater, so, the distribution tail of 'short-lived' items will actually be longer lived than items in a strict queue for the same exact memory. Additional techniques, such as n-array hashing, and low order 'clock' bits at container level, can tighten this distribution significantly (i.e. ~LRU -> LRU) via better loading factors.