Problem difficulty so far (up to day 16)

  1. Day 15 - Warehouse Woes: 30m00s
  2. Day 12 - Garden Groups: 17m42s
  3. Day 14 - Restroom Redoubt: 15m48s
  4. Day 09 - Disk Fragmenter: 14m05s
  5. Day 16 - Reindeer Maze: 13m47s
  6. Day 13 - Claw Contraption: 11m04s
  7. Day 06 - Guard Gallivant: 08m53s
  8. Day 08 - Resonant Collinearity: 07m12s
  9. Day 11 - Plutonian Pebbles: 06m24s
  10. Day 04 - Ceres Search: 05m41s
  11. Day 02 - Red Nosed Reports: 04m42s
  12. Day 10 - Hoof It: 04m14s
  13. Day 07 - Bridge Repair: 03m47s
  14. Day 05 - Print Queue: 03m43s
  15. Day 03 - Mull It Over: 03m22s
  16. Day 01 - Historian Hysteria: 02m31s
  • zogwarg@awful.systems
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    5 days ago

    22!

    spoilers!

    Well it’s not a grid! My chosen language does not have bitwise operators so it’s a bit slow. Have to resort to manual parallelization.

    • gerikson@awful.systemsOP
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      5 days ago

      so many off by one errors

      also first time I had to run the code on a desktop machine because my VPS was too slow

    • Architeuthis@awful.systems
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      5 days ago
      22-2 commentary

      I got a different solution than the one given on the site for the example data, the sequence starting with 2 did not yield the expected solution pattern at all, and the one I actually got gave more bananas anyway.

      The algorithm gave the correct result for the actual puzzle data though, so I’m leaving it well alone.

      Also the problem had a strong map/reduce vibe so I started out with the sequence generation and subsequent transformations parallelized already from pt1, but ultimately it wasn’t that intensive a problem.

      Toddler’s sick (but getting better!) so I’ve been falling behind, oh well. Doubt I’ll be doing 24 & 25 on their release days either as the off-days and festivities start kicking in.

    • zogwarg@awful.systems
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      5 days ago
      Hacky Manual parallelization

      22-b.jq

      Massive time gains with parallelization + optimized next function (2x speedup) by doing the 3 xor operation in “one operation”, Maybe I prefer the grids ^^:

      #!/usr/bin/env jq -n -f
      
      #────────────────── Big-endian to_bits ───────────────────#
      def to_bits:
        if . == 0 then [0] else { a: ., b: [] } | until (.a == 0;
            .a /= 2 |
            if .a == (.a|floor) then .b += [0]
                                else .b += [1] end | .a |= floor
        ) | .b end;
      #────────────────── Big-endian from_bits ────────────────────────#
      def from_bits: [ range(length) as $i | .[$i] * pow(2; $i) ] | add;
      
      ( # Get index that contribute to next xor operation.
        def xor_index(a;b): [a, b] | transpose | map(add);
        [ range(24) | [.] ]
        | xor_index([range(6) | [-1]] + .[0:18] ; .[0:24])
        | xor_index(.[5:29] ; .[0:24])
        | xor_index([range(11) | [-1]] + .[0:13]; .[0:24])
        | map(
            sort | . as $indices | map(
              select( . as $i |
                $i >= 0 and ($indices|indices($i)|length) % 2 == 1
              )
            )
          )
      ) as $next_ind |
      
      # Optimized Next, doing XOR of indices simultaneously a 2x speedup #
      def next: . as $in | $next_ind | map( [ $in[.[]] // 0 ] | add % 2 );
      
      #  Still slow, because of from_bits  #
      def to_price($p): $p | from_bits % 10;
      
      # Option to run in parallel using xargs, Eg:
      #
      # seq 0 9 | \
      # xargs -P 10 -n 1 -I {} bash -c './2024/jq/22-b.jq input.txt \
      # --argjson s 10 --argjson i {} > out-{}.json'
      # cat out-*.json | ./2024/jq/22-b.jq --argjson group true
      # rm out-*.json
      #
      # Speedup from naive ~50m -> ~1m
      def parallel: if $ARGS.named.s and $ARGS.named.i  then
         select(.key % $ARGS.named.s == $ARGS.named.i)  else . end;
      
      #════════════════════════════ X-GROUP ═══════════════════════════════#
      if $ARGS.named.group then
      
      # Group results from parallel run #
      reduce inputs as $dic ({}; reduce (
            $dic|to_entries[]
        ) as {key: $k, value: $v} (.; .[$k] += $v )
      )
      
      else
      
      #════════════════════════════ X-BATCH ═══════════════════════════════#
      reduce (
        [ inputs ] | to_entries[] | parallel
      ) as { value: $in } ({};  debug($in) |
        reduce range(2000) as $_ (
          .curr = ($in|to_bits) | .p = to_price(.curr) | .d = [];
          .curr |= next | to_price(.curr) as $p
          | .d = (.d+[$p-.p])[-4:]  | .p = $p # Four differences to price
          | if .a["\($in)"]["\(.d)"]|not then # Record first price
               .a["\($in)"]["\(.d)"] = $p end # For input x 4_diff
        )
      )
      
      # Summarize expected bananas per 4_diff sequence #
      | [ .a[] | to_entries[] ]
      | group_by(.key)
      | map({key: .[0].key, value: ([.[].value]|add)})
      | from_entries
      
      end |
      
      #═══════════════════════════ X-FINALLY ══════════════════════════════#
      if $ARGS.named.s | not then
      
      #     Output maximum expexted bananas      #
      to_entries | max_by(.value) | debug | .value
      
      end
      
      • Architeuthis@awful.systems
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        5 days ago

        If nothing else, you’ve definitely stopped me forever from thinking of jq as sql for json. Depending on how much I hate myself by next year I think I might give kusto a shot for AOC '25