highfleet-ship-opt
libclc
highfleet-ship-opt | libclc | |
---|---|---|
4 | 1 | |
6 | 8 | |
- | - | |
10.0 | 10.0 | |
about 2 years ago | almost 6 years ago | |
C++ | C | |
GNU General Public License v3.0 or later | - |
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highfleet-ship-opt
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Enhancing the Factorio experience with SAT solvers
This is my favorite pastime as well! Well done. I've also dipped my toes in the "over engineer a game" with:
A highfleet ship optimizer which chooses optimal module sets using ILP at
https://hfopt.jodavaho.io
And a hunt showdown loadout A/B test package that lets you run stats queries on your game journal at (for now)
https://crates.io/crates/kda-tools
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Ask HN: Tell us about your project that's not done yet but you want feedback on
If you play "Highfleet" at all, can you poke around https://hfopt.jodavaho.io and see if you can get it to generate module lists that make sense?
It's an optimizer, just add what you know you want, set some cost / range / speed limits, and it will output the cheapest version of the ship that includes all required modules and has all requested stats.
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Ask HN: What is new in Algorithms / Data Structures these days?
I used a MILP solver to optimize my ship loadouts in Highfleet. It's rugged-looking, but works great. https://hfopt.jodavaho.io
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Any have updated modules requirements and stats calculations for 1.16?
If you are a bit more techy, a working python version is available here: https://github.com/jodavaho/highfleet-ship-opt
libclc
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Ask HN: What is new in Algorithms / Data Structures these days?
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.
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