Solving Advent of Code ’23 “Aplenty” by Compiling

Every year I try to solve some problems from the Advent of Code (AoC) competition in a not straightforward way. Let’s solve the part one of the day 19 problem Aplenty by compiling the problem input to an executable file.

The Problem

What the problem presents as input is essentially a program. Here is the example input:

px{a<2006:qkq,m>2090:A,rfg}
pv{a>1716:R,A}
lnx{m>1548:A,A}
rfg{s<537:gd,x>2440:R,A}
qs{s>3448:A,lnx}
qkq{x<1416:A,crn}
crn{x>2662:A,R}
in{s<1351:px,qqz}
qqz{s>2770:qs,m<1801:hdj,R}
gd{a>3333:R,R}
hdj{m>838:A,pv}

{x=787,m=2655,a=1222,s=2876}
{x=1679,m=44,a=2067,s=496}
{x=2036,m=264,a=79,s=2244}
{x=2461,m=1339,a=466,s=291}
{x=2127,m=1623,a=2188,s=1013}
exinput.txt

Each line in the first section of the input is a code block. The bodies of the blocks have statements of these types:

  • Accept (A) or Reject (R) that terminate the program.
  • Jumps to other blocks by their names, for example: rfg as the last statement of the px block in the first line.
  • Conditional statements that have a condition and what to do if the condition is true, which can be only Accept/Reject or a jump to another block.

The problem calls the statements “rules”, the blocks “workflows”, and the program “system”.

All blocks of the program operates on a set of four values: x, m, a, and s. The problem calls them “ratings”, and each set of ratings is for/forms a “part”. The second section of the input specifies a bunch of these parts to run the system against.

This seems to map very well to a C program, with Accept and Reject returning true and false respectively, and jumps accomplished using gotos. So that’s what we’ll do: we’ll compile the problem input to a C program, then compile that to an executable, and run it to get the solution to the problem.

And of course, we’ll do all this in Haskell. First some imports:

{-# LANGUAGE LambdaCase #-}
{-# LANGUAGE StrictData #-}

module Main where

import qualified Data.Array as Array
import Data.Char (digitToInt, isAlpha, isDigit)
import Data.Foldable (foldl', foldr')
import Data.Function (fix)
import Data.Functor (($>))
import qualified Data.Graph as Graph
import Data.List (intercalate, (\\))
import qualified Data.Map.Strict as Map
import System.Environment (getArgs)
import qualified Text.ParserCombinators.ReadP as P
import Prelude hiding (GT, LT)

The Parser

First, we parse the input program to Haskell data types. We use the ReadP parser library built into the Haskell standard library.

data Part = Part
  { partX :: Int,
    partM :: Int,
    partA :: Int,
    partS :: Int
  } deriving (Show)

data Rating = X | M | A | S deriving (Show, Eq)

emptyPart :: Part
emptyPart = Part 0 0 0 0

addRating :: Part -> (Rating, Int) -> Part
addRating p (r, v) = case r of
  X -> p {partX = v}
  M -> p {partM = v}
  A -> p {partA = v}
  S -> p {partS = v}

partParser :: P.ReadP Part
partParser =
  foldl' addRating emptyPart
    <$> P.between (P.char '{') (P.char '}')
          (partRatingParser `P.sepBy1` P.char ',')

partRatingParser :: P.ReadP (Rating, Int)
partRatingParser =
  (,) <$> ratingParser <*> (P.char '=' *> intParser)

ratingParser :: P.ReadP Rating
ratingParser =
  P.get >>= \case
    'x' -> pure X
    'm' -> pure M
    'a' -> pure A
    's' -> pure S
    _ -> P.pfail

intParser :: P.ReadP Int
intParser =
  foldl' (\n d -> n * 10 + d) 0 <$> P.many1 digitParser

digitParser :: P.ReadP Int
digitParser = digitToInt <$> P.satisfy isDigit

parse :: (Show a) => P.ReadP a -> String -> Either String a
parse parser text = case P.readP_to_S (parser <* P.eof) text of
  [(res, "")] -> Right res
  [(_, s)] -> Left $ "Leftover input: " <> s
  out -> Left $ "Unexpected output: " <> show out

Part is a Haskell data type representing parts, and Rating is an enum for, well, ratings1. Following that are parsers for parts and ratings, written in Applicative and Monadic styles using the basic parsers and combinators provided by the ReadP library.

Finally, we have the parse function to run a parser on an input. We can try parsing parts in GHCi:

> parse partParser "{x=2127,m=1623,a=2188,s=1013}"
Right (Part {partX = 2127, partM = 1623, partA = 2188, partS = 1013})

Next, we represent and parse the program, I mean, the system:

newtype System =
  System (Map.Map WorkflowName Workflow)
  deriving (Show, Eq)

data Workflow = Workflow
  { wName :: WorkflowName,
    wRules :: [Rule]
  } deriving (Show, Eq)

type WorkflowName = String

data Rule
  = AtomicRule AtomicRule
  | If Condition AtomicRule
  deriving (Show, Eq)

data AtomicRule
  = Jump WorkflowName
  | Accept
  | Reject
  deriving (Show, Eq, Ord)

data Condition
  = Comparison Rating CmpOp Int
  deriving (Show, Eq)

data CmpOp = LT | GT deriving (Show, Eq)

A System is a map of workflows by their names. A Workflow has a name and a list of rules. A Rule is either an AtomicRule, or an If rule. An AtomicRule is either a Jump to another workflow by name, or an Accept or Reject rule. The Condition of an If rule is a less that (LT) or a greater than (GT) Comparison of some Rating of an input part with an integer value.

Now, it’s time to parse the system:

systemParser :: P.ReadP System
systemParser =
  System
    . foldl' (\m wf -> Map.insert (wName wf) wf m) Map.empty
    <$> workflowParser `P.endBy1` P.char '\n'

workflowParser :: P.ReadP Workflow
workflowParser =
  Workflow
    <$> P.many1 (P.satisfy isAlpha)
    <*> P.between (P.char '{') (P.char '}')
          (ruleParser `P.sepBy1` P.char ',')

ruleParser :: P.ReadP Rule
ruleParser =
  (AtomicRule <$> atomicRuleParser) P.<++ ifRuleParser

ifRuleParser :: P.ReadP Rule
ifRuleParser =
  If
    <$> (Comparison <$> ratingParser <*> cmpOpParser <*> intParser)
    <*> (P.char ':' *> atomicRuleParser)

atomicRuleParser :: P.ReadP AtomicRule
atomicRuleParser = do
  c : _ <- P.look
  case c of
    'A' -> P.char 'A' $> Accept
    'R' -> P.char 'R' $> Reject
    _ -> (Jump .) . (:) <$> P.char c <*> P.many1 (P.satisfy isAlpha)

cmpOpParser :: P.ReadP CmpOp
cmpOpParser = P.choice [P.char '<' $> LT, P.char '>' $> GT]

Parsing is straightforward as there are no recursive data types or complicated precedence or associativity rules here. We can exercise it in GHCi (output formatted for clarity):

> parse workflowParser "px{a<2006:qkq,m>2090:A,rfg}"
Right (
  Workflow {
    wName = "px",
    wRules = [
      If (Comparison A LT 2006) (Jump "qkq"),
      If (Comparison M GT 2090) Accept,
      AtomicRule (Jump "rfg")
    ]
  }
)

Excellent! We can now combine the part parser and the system parser to parse the problem input:

data Input = Input System [Part] deriving (Show)

inputParser :: P.ReadP Input
inputParser =
  Input
    <$> systemParser
    <*> (P.char '\n' *> partParser `P.endBy1` P.char '\n')

Before moving on to translating the system to C, let’s write an interpreter so that we can compare the output of our final C program against it for validation.

The Interpreter

Each system has a workflow named “in”, where the execution of the system starts. Running the system results in True if the run ends with an Accept rule, or in False if the run ends with a Reject rule. With this in mind, let’s cook up the interpreter:

runSystem :: System -> Part -> Bool
runSystem (System system) part = runRule $ Jump "in"
  where
    runRule = \case
      Accept -> True
      Reject -> False
      Jump wfName -> jump wfName

    jump wfName = case Map.lookup wfName system of
      Just workflow -> runRules $ wRules workflow
      Nothing ->
        error $ "Workflow not found in system: " <> wfName

    runRules = \case
      (rule : rest) -> case rule of
        AtomicRule aRule -> runRule aRule
        If cond aRule ->
          if evalCond cond
            then runRule aRule
            else runRules rest
      _ -> error "Workflow ended without accept/reject"

    evalCond = \case
      Comparison r LT value -> rating r < value
      Comparison r GT value-> rating r > value

    rating = \case
      X -> partX part
      M -> partM part
      A -> partA part
      S -> partS part

The interpreter starts by running the rule to jump to the “in” workflow. Running a rule returns True or False for Accept or Reject rules respectively, or jumps to a workflow for Jump rules. Jumping to a workflow looks it up in the system’s map of workflows, and sequentially runs each of its rules.

An AtomicRule is run as previously mentioned. An If rule evaluates its condition, and either runs the consequent rule if the condition is true, or moves on to running the rest of the rules in the workflow.

That’s it for the interpreter. We can run it on the example input:

> inputText <- readFile "input.txt"
> Right (Input system parts) = parse inputParser inputText
> runSystem system (parts !! 0)
True
> runSystem system (parts !! 1)
False

The AoC problem requires us to return the sum total of the ratings of the parts that are accepted by the system:

solve :: Input -> Int
solve (Input system parts) =
  sum
  . map (\(Part x m a s) -> x + m + a + s)
  . filter (runSystem system)
  $ parts

Let’s run it for the example input:

> Right input <- parse inputParser <$> readFile "exinput.txt"
> solve input
19114

It returns the correct answer! Next up, we generate some C code.

The Control-flow Graph

But first, a quick digression to graphs. A Control-flow graph or CFG, is a graph of all possible paths that can be taken through a program during its execution. It has many uses in compilers, but for now, we use it to generate more readable C code.

Using the Data.Graph module from the containers package, we write the function to create a control-flow graph for our system/program, and use it to topologically sort the workflows:

type Graph' a =
  (Graph.Graph, Graph.Vertex -> (a, [a]), a -> Maybe Graph.Vertex)

cfGraph :: Map.Map WorkflowName Workflow -> Graph' WorkflowName
cfGraph system =
  graphFromMap
    . Map.toList
    . flip Map.map system
    $ \(Workflow _ rules) ->
      flip concatMap rules $ \case
        AtomicRule (Jump wfName) -> [wfName]
        If _ (Jump wfName) -> [wfName]
        _ -> []
  where
    graphFromMap :: (Ord a) => [(a, [a])] -> Graph' a
    graphFromMap m =
      let (graph, nLookup, vLookup) =
            Graph.graphFromEdges $ map (\(f, ts) -> (f, f, ts)) m
       in (graph, \v -> let (x, _, xs) = nLookup v in (x, xs), vLookup)

toposortWorkflows :: Map.Map WorkflowName Workflow -> [WorkflowName]
toposortWorkflows system =
  let (cfg, nLookup, _) = cfGraph system
   in map (fst . nLookup) $ Graph.topSort cfg

Graph' is a simpler type for a graph of nodes of type a. The cfGraph function takes a the map from workflow names to workflows — that is, a system — and returns a control-flow graph of workflow names. It does this by finding jumps from workflows to other workflows, and connecting them.

Then, the toposortWorkflows function uses the created CFG to topologically sort the workflows. We’ll see this in action in a bit. Moving on to …

The Compiler

The compiler, for now, simply generates the C code for a given system. We write a ToC typeclass for convenience:

class ToC a where
  toC :: a -> String

instance ToC Part where
  toC (Part x m a s) =
    "{" <> intercalate ", " (map show [x, m, a, s]) <> "}"

instance ToC CmpOp where
  toC = \case
    LT -> "<"
    GT -> ">"

instance ToC Rating where
  toC = \case
    X -> "x"
    M -> "m"
    A -> "a"
    S -> "s"

instance ToC AtomicRule where
  toC = \case
    Accept -> "return true;"
    Reject -> "return false;"
    Jump wfName -> "goto " <> wfName <> ";"

instance ToC Condition where
  toC = \case
    Comparison rating op val ->
      toC rating <> " " <> toC op <> " " <> show val

instance ToC Rule where
  toC = \case
    AtomicRule aRule -> toC aRule
    If cond aRule ->
      "if (" <> toC cond <> ") { " <> toC aRule <> " }"

instance ToC Workflow where
  toC (Workflow wfName rules) =
    wfName
      <> ":\n"
      <> intercalate "\n" (map (("  " <>) . toC) rules)

instance ToC System where
  toC (System system) =
    intercalate
      "\n"
      [ "bool runSystem(int x, int m, int a, int s) {",
        "  goto in;",
        intercalate
          "\n"
          (map (toC . (system Map.!)) $ toposortWorkflows system),
        "}"
      ]

instance ToC Input where
  toC (Input system parts) =
    intercalate
      "\n"
      [ "#include <stdbool.h>",
        "#include <stdio.h>\n",
        toC system,
        "int main() {",
        "  int parts[][4] = {",
        intercalate ",\n" (map (("    " <>) . toC) parts),
        "  };",
        "  int totalRating = 0;",
        "  for(int i = 0; i < " <> show (length parts) <> "; i++) {",
        "    int x = parts[i][0];",
        "    int m = parts[i][1];",
        "    int a = parts[i][2];",
        "    int s = parts[i][3];",
        "    if (runSystem(x, m, a, s)) {",
        "      totalRating += x + m + a + s;",
        "    }",
        "  }",
        "  printf(\"%d\", totalRating);",
        "  return 0;",
        "}"
      ]

As mentioned before, Accept and Reject rules are converted to return true and false respectively, and Jump rules are converted to gotos. If rules become if statements, and Workflows become block labels followed by block statements.

A System is translated to a function runSystem that takes four parameters, x, m, a and s, and runs the workflows translated to blocks by executing goto in.

Finally, an Input is converted to a C file with the required includes, and a main function that solves the problem by calling the runSystem function for all parts.

Let’s throw in a main function to put everything together.

main :: IO ()
main = do
  file <- head <$> getArgs
  code <- readFile file
  case parse inputParser code of
    Right input -> putStrLn $ toC input
    Left err -> error err

The main function reads the input from the file provided as the command line argument, parses it and outputs the generated C code. Let’s run it now.

The Compiler Output

We compile our compiler and run it to generate the C code for the example problem:

$ ghc --make aplenty.hs
$ ./aplenty exinput.txt > aplenty.c

This is the C code it generates:

#include <stdbool.h>
#include <stdio.h>

bool runSystem(int x, int m, int a, int s) {
  goto in;
in:
  if (s < 1351) { goto px; }
  goto qqz;
qqz:
  if (s > 2770) { goto qs; }
  if (m < 1801) { goto hdj; }
  return false;
qs:
  if (s > 3448) { return true; }
  goto lnx;
lnx:
  if (m > 1548) { return true; }
  return true;
px:
  if (a < 2006) { goto qkq; }
  if (m > 2090) { return true; }
  goto rfg;
rfg:
  if (s < 537) { goto gd; }
  if (x > 2440) { return false; }
  return true;
qkq:
  if (x < 1416) { return true; }
  goto crn;
hdj:
  if (m > 838) { return true; }
  goto pv;
pv:
  if (a > 1716) { return false; }
  return true;
gd:
  if (a > 3333) { return false; }
  return false;
crn:
  if (x > 2662) { return true; }
  return false;
}
int main() {
  int parts[][4] = {
    {787, 2655, 1222, 2876},
    {1679, 44, 2067, 496},
    {2036, 264, 79, 2244},
    {2461, 1339, 466, 291},
    {2127, 1623, 2188, 1013}
  };
  int totalRating = 0;
  for(int i = 0; i < 5; i++) {
    int x = parts[i][0];
    int m = parts[i][1];
    int a = parts[i][2];
    int s = parts[i][3];
    if (runSystem(x, m, a, s)) {
      totalRating += x + m + a + s;
    }
  }
  printf("%d", totalRating);
  return 0;
}

We see the toposortWorkflows function in action, sorting the blocks in the topological order of jumps between them, as opposed to the original input. Does this work? Only one way to know:

$ gcc aplenty.c -o solution
$ ./solution
19114

Perfect! The solution matches the interpreter output.

The Bonus: Optimizations

By studying the output C code, we spot some possibilities for optimizing the compiler output. Notice how the lnx block returns same value (true) regardless of which branch it takes:

lnx:
  if (m > 1548) { return true; }
  return true;

So, we should be able to replace it with:

lnx:
  return true;

If we do this, the lnx block becomes degenerate, and hence the jumps to the block can be inlined, turning the qs block from:

qs:
  if (s > 3448) { return true; }
  goto lnx;

to:

qs:
  if (s > 3448) { return true; }
  return true;

which makes the if statement in the qs block redundant as well. Hence, we can repeat the previous optimization and further reduce the generated code.

Another possible optimization is to inline the blocks to which there are only single jumps from the rest of the blocks, for example the qqz block.

Let’s write these optimizations.

Simplify Workflows

simplifyWorkflows :: System -> System
simplifyWorkflows (System system) =
  System $ Map.map simplifyWorkflow system
  where
    simplifyWorkflow (Workflow name rules) =
      Workflow name
        $ foldr'
          ( \r rs -> case rs of
              [r'] | ruleOutcome r == ruleOutcome r' -> rs
              _ -> r : rs
          )
          [last rules]
        $ init rules

    ruleOutcome = \case
      If _ aRule -> aRule
      AtomicRule aRule -> aRule

simplifyWorkflows goes over all workflows and repeatedly removes the statements from the end of the blocks that has same outcome as the statement previous to them.

Inline Redundant Jumps

inlineRedundantJumps :: System -> System
inlineRedundantJumps (System system) =
  System $
    foldl' (flip Map.delete) (Map.map inlineJumps system) $
      Map.keys redundantJumps
  where
    redundantJumps =
      Map.map (\wf -> let ~(AtomicRule rule) = head $ wRules wf in rule)
        . Map.filter (\wf -> length (wRules wf) == 1)
        $ system

    inlineJumps (Workflow name rules) =
      Workflow name $ map inlineJump rules

    inlineJump = \case
      AtomicRule (Jump wfName)
        | Map.member wfName redundantJumps ->
            AtomicRule $ redundantJumps Map.! wfName
      If cond (Jump wfName)
        | Map.member wfName redundantJumps ->
            If cond $ redundantJumps Map.! wfName
      rule -> rule

inlineRedundantJumps find the jumps to degenerate workflows and inlines them. It does this by first going over all workflows and creating a map of degenerate workflow names to the only rule in them, and then replacing the jumps to such workflows with the only rules.

Remove Jumps

removeJumps :: System -> System
removeJumps (System system) =
  let system' =
        foldl' (flip $ Map.adjust removeJumpsWithSingleJumper) system $
          toposortWorkflows system
   in System
        . foldl' (flip Map.delete) system'
        . (\\ ["in"])
        $ workflowsWithNJumpers 0 system'
  where
    removeJumpsWithSingleJumper (Workflow name rules) =
      Workflow name $
        init rules <> case last rules of
          AtomicRule (Jump wfName)
            | wfName `elem` workflowsWithSingleJumper ->
                let (Workflow _ rules') = system Map.! wfName
                 in rules'
          rule -> [rule]

    workflowsWithSingleJumper = workflowsWithNJumpers 1 system

    workflowsWithNJumpers n sys =
      let (cfg, nLookup, _) = cfGraph sys
       in map (fst . nLookup . fst)
            . filter (\(_, d) -> d == n)
            . Array.assocs
            . Graph.indegree
            $ cfg

removeJumps does two things: first, it finds blocks with only one jumper, and inlines their statements to the jump location. Then it finds blocks to which there are no jumps, and removes them entirely from the program. It uses the workflowsWithNJumpers helper function that uses the control-flow graph of the system to find all workflows to which there are n number of jumps, where n is provided as an input to the function. Note the usage of the toposortWorkflows function here, which makes sure that we remove the blocks in topological order, accumulating as many statements as possible in the final program.

With these functions in place, we write the optimize function:

optimize :: System -> System
optimize =
  applyTillUnchanged
    (removeJumps . inlineRedundantJumps . simplifyWorkflows)
  where
    applyTillUnchanged :: (Eq a) => (a -> a) -> a -> a
    applyTillUnchanged f =
      fix (\recurse x -> if f x == x then x else recurse (f x))

We execute the three optimization functions repeatedly till a fixed point is reached for the resultant System, that is, till there are no further possibilities of optimization.

Finally, we change our main function to apply the optimizations:

main :: IO ()
main = do
  file <- head <$> getArgs
  code <- readFile file
  case parse inputParser code of
    Right (Input system parts) ->
      putStrLn . toC $ Input (optimize system) parts
    Left err -> error err

Compiling the optimized compiler and running it as earlier, generates this C code for the runSystem function now:

bool runSystem(int x, int m, int a, int s) {
  goto in;
in:
  if (s < 1351) { goto px; }
  if (s > 2770) { return true; }
  if (m < 1801) { goto hdj; }
  return false;
px:
  if (a < 2006) { goto qkq; }
  if (m > 2090) { return true; }
  if (s < 537) { return false; }
  if (x > 2440) { return false; }
  return true;
qkq:
  if (x < 1416) { return true; }
  if (x > 2662) { return true; }
  return false;
hdj:
  if (m > 838) { return true; }
  if (a > 1716) { return false; }
  return true;
}

It works well2. We now have 1.7x fewer lines of code as compared to before3.

The Conclusion

This was another attempt to solve Advent of Code problems in somewhat unusual ways. This year we learned some basics of compilation. Swing by next year for more weird ways to solve simple problems.

The full code for this post is available here.


  1. I love how I have to write XMAS horizontally and vertically a couple of time.↩︎

  2. I’m sure many more optimizations are possible yet. After all, this program is essentially a decision tree.↩︎

  3. For the actual problem input with 522 blocks, the optimizations reduce the LoC by 1.5x.↩︎

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14 comments

Very cool. One question that I had, was why compile via C?

Thanks! Since the problem maps almost directly to C, it made sense to compile via C. Another possibility is to directly generate assembly though that’s a bit more cumbersome. Other approaches are also possible like this solution that generates Common Lisp code and compiles it.

nickgirardo

Great post!

I was curious how much of an effect the optimizations performed in Haskell have on the machine code emitted by the whichever C compiler transforms the output into machine code. It does appear as though with -O2 gcc produces similar (although not identical) output for both the optimized and non-optimized C

Thanks!

You are right here. The optimizations in the post have no effect whatsoever on the execution time of the final executable produced by GCC. I’m guessing that’s because GCC is very good at optimizing even the naively generated code.

Yes, GCC implements jump threading optimizations already.

Compiling works for part 2 as well, here’s my take on it in Common Lisp (which has the advantage of having a compiler available at runtime by default):

https://github.com/ak-coram/advent2023/blob/main/19.lisp#L106

It all nicely fits into a single tagbody form. Part 2 was a bit trickier, I had to move some of the logic into separate functions otherwise the compilation took a long time (10+ seconds).

I’m not versed in Common Lisp, so I can’t tell what’s exactly happening in your code. Do you compile the input to a bunch of if statements? Do you apply any particular optimizations?

A quick overview:

  • All the defrules on the top of the file are just for parsing the input.

  • There’s a big if form in the function day19 separating solutions for parts one and two.

  • For part one I create a lambda function basically consisting of a single tagbody, the generated form looks like this: https://gist.github.com/ak-coram/4071c0c656b9ca794eae63f0df30cc80

  • I pass the above to the compile function and the compiler is free to optimize it (I didn’t do any manual optimization besides declaring the integer variables as FIXNUMs). Then I call the function for every part and sum the ratings when it returns T. Runs in 0.175 seconds of real time on my machine including the parsing and compilation steps. It looks like a lot of machine code is generated, but it’s mostly just the comparisons and jumps. I felt this was already pretty fast, so I didn’t tweak the compiler (SBCL) further.

  • For part two I moved the logic of dealing with ranges of values into separate functions as it was slowing compilation down when inlined. When a condition splits a range, I also run the other half of the range through the same tagbody. This one takes 0.191 seconds on my machine.

770grappenmaker

Nicely done!

topaz2078

YES

That is some next level wizardry. Very impressive.

@abnv yeah, that’s essentially how I did it (for part A) in #julialang just using the expression evaluation tools. It does feel like cheating :D

@abnv This is a nifty approach. Thank you for sharing!

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7 Reposts abhin4vAbhinav ⁉️Craig StuntzKim WallmarkTim LavoieVedang ManerikarAnupam 《ミ》λ≡