• MinnesotaGoddam@lemmy.world
    2·
    1 hour ago

    so like, my wife and i kept disagreeing on my cache. in part because she didn’t know what i was doing, in part because i hadn’t told her explicitly what i was doing. for example, i like to rewear my jeans a few times until they are dirty enough to need washing. i was hanging them on the side of the laundry basket. and would flip and fold and hang them differently to indicate how many times i had worn them i case i had forgotten because my brain is a rusty sieve lately.

  • cogman@lemmy.world
    15·
    5 hours ago

    Silly commenter.

    L1 cache shouldn’t be large. Increasing the size of the L1 cache increases the latency. Maybe if you shrink the size of the cloths you wear you can squeeze more into the chair, but the ideal L1 cache has to minimize it’s distance from processing. Oversizing adds latency.

    Your L2 cache is where you generally try and shove a much bigger cache into it, but it’s still got a size constraint for the latency you are after. Further, typically L1 and L2 only serve 1 CPU. To multi-process stuff you’ll typically need an even larger L3 cache which is shared among cores.

    So the cloths on your chair should be minimal for fast access (L1). You can put more cloths on your bed and dressers or in laundry baskets that can be promoted to the chair if you start needing them more often (L2). You can throw a bunch of cloths into a pile in the corner which sit there for a few years and serve many occasions (L3).

    The worst thing is going back to main memory (your closet) to search for specialty cloths you are ultimately going to need to send back to the closet. And heavy help you if you have to swap (do laundry).

  • durinn@programming.devEnglish
    3·
    4 hours ago

    While I miss my parents after having moved out, I do appreciate my array of clothes being nonvolatile memory. It’s become a comfort, knowing that it stays in the same configuration unless I actively recompile it.

  • Sir. Haxalot@nord.pubEnglish
    9·
    6 hours ago

    You’re saying this as if there isn’t multiple piles mixed fresh and dirty clothes with an O(n^2) complexity to find something you want.

    I personally prefer to have my clothes indexed in an ordered storage so I know exactly which row in the drawers clean shirts are in.

    • Zink@programming.dev
      3·
      4 hours ago

      That makes me think of how much it annoys me when things are really messy and disorganized in our house, which is very often.

      It’s like there’s no indexing. Where is thing X that somebody else used last? Time to start a fresh empty-cache brute force search of the whole space!

  • trougnouf@lemmy.world
    22·
    10 hours ago

    Wouldn’t a pile of clothes have O(n) complexity? They’d still have to go through them one at a time unless the clothes have a really distinct color/shape and are in a somewhat tidy pile s.t. they can be pulled from anywhere.

      • TheseusNow@lemmy.zip
        2·
        1 hour ago

        Exactly. Since n is represented by the collection of clothes, it is O(1) if you are only going through a finite subset of the clothes. If everything is in the closet, and in no particular order, you would then do O(n) because you potentially have to go through the entire collection to find one article of clothing.

        It gets even worse if your idea of organization is placing the clothes neatly yet with no real order in a closet with multiple drawers and containers inside. In this last scenario you get O(n²) because you now have to repeatedly search through n numbers of clothing containers.

    • sloppy_diffuser@sh.itjust.worksEnglish
      15·
      9 hours ago

      Items are in a hash table using color/material type/shape as the hashing method optimized for human pattern recognition providing O(1) access. The table is smaller than the number of items causing some collisions. Those items are in a randomly sorted vector. Average case is still around O(1) with an O(n) worst case.

    • ByteJunk@lemmy.world
      6·
      8 hours ago

      A well organized drawer or cabinet should still be O(1). It takes at least 2 more steps, assuming you don’t leave them open all the time, but the number of operations doesn’t change depending on the number of clothing items you need to retrieve.

      A pile of clothes is faster, but only for a small number of items. As the number of articles pile up, they hide older items and need to be pushed side before the intended article can be found and then retrieved. This is now O(N), and less efficient than just storing things in the proper place…

    • searabbit@piefed.socialEnglish
      4·
      9 hours ago

      Idk about your pile, but mine is precariously balanced on top of a chair so it’s O(1) until a literal tipping point when everything falls and then it’s O(k*n) where k is the time it takes me to put away a piece of clothing in the closet/laundry (or start a new pile elsewhere).

  • ViatorOmnium@piefed.socialEnglish
    56·
    12 hours ago

    Unironically this. In a related note, most people confuse “tidy”, which is about aesthetics, with “organised” that is about efficiency. That’s why my long term storage is extremely tidy, and my short term storage (mostly my desk, and a small table next to it) looks like a modern art installation.

    • clb92@feddit.dkEnglish
      7·
      8 hours ago

      Ah, so my problem is actually that I just fail to put things into long-term storage.

  • Zagorath@quokk.auEnglish
    7·
    10 hours ago

    I like the joke, but my pile of clothes is entirely about things that I want to reuse. They’re too dirty to hang back up in the wardrobe, but too clean to throw in the dirty clothes basket. I’m sure there’s an analogy someone could make for this, but Async’s analogy doesn’t work in my case.

  • kubica@fedia.io
    8·
    12 hours ago

    Except that my L1 cache is more like a top layer, which doesn’t hold very well in place because of analogic world being so crumbly.