プロファイリング/メモリ使用量の改善および/またはGC時間

Question

オリジナル

私は過度のメモリ使用量および/またはGCの努力[私はあることを考えるもの] CSVファイルを集約しようとして経験しています。グループの数が増えると問題が発生しているようです。キーが数百から数千になると問題はありませんが、キーが数万に達するとすぐにGCで大部分の時間が費やされます。 Data.ByteString.Short.ShortByteStringにData.ByteString.Lazy.ByteStringからの移行

更新が

が大幅に（私が合理的だと思うレベルまで）、メモリ消費量を削減しました。しかし、GCで費やされる時間は、私が必要と思われるよりもはるかに高いようです。私はData.HashMap.Strict.HashMapからData.HashTable.ST.Basic.HashTableに移動し、STの突然変異が助けになるかどうか確認しましたが、それは見られませんでした。以下は、試験サンプルを作成するためにgenerateFileを含め、現在の完全なテストコードです：

{-# LANGUAGE OverloadedStrings #-} 

module Main where 

import System.IO (withFile, IOMode(WriteMode)) 
import qualified System.Random as Random 

import qualified Data.ByteString.Short as BSS 
import qualified Data.ByteString.Lazy.Char8 as BL 
import qualified Data.Vector as V 
import qualified Data.Vector.Mutable as MV 
import qualified Control.Monad.ST as ST 

import qualified Data.HashTable.ST.Basic as HT 
import qualified Data.HashTable.Class as HT (toList) 
import Data.Hashable (Hashable, hashWithSalt) 

import Data.List (unfoldr) 

import qualified Data.Traversable as T 
import Control.Monad (forM_) 

instance Hashable a => Hashable (V.Vector a) where 
    hashWithSalt s = hashWithSalt s . V.toList 

data CSVFormat = CSVFormat { 
    csvSeparator :: Char, 
    csvWrapper :: Char 
} 

readCSV :: CSVFormat -> Int -> FilePath -> IO [V.Vector BSS.ShortByteString] 
readCSV format skip filepath = BL.readFile filepath >>= return . parseCSV format skip 

parseCSV :: CSVFormat -> Int -> BL.ByteString -> [V.Vector BSS.ShortByteString] 
parseCSV (CSVFormat sep wrp) skp = drop skp . unfoldr (\bs -> if BL.null bs then Nothing else Just (apfst V.fromList (parseLine bs))) 
    where 
    {-# INLINE apfst #-} 
    apfst f (x,y) = (f x,y) 

    {-# INLINE isCr #-} 
    isCr c = c == '\r' 

    {-# INLINE isLf #-} 
    isLf c = c == '\n' 

    {-# INLINE isSep #-} 
    isSep c = c == sep || isLf c || isCr c 

    {-# INLINE isWrp #-} 
    isWrp c = c == wrp 

    {-# INLINE parseLine #-} 
    parseLine :: BL.ByteString -> ([BSS.ShortByteString], BL.ByteString) 
    parseLine bs = 
     let (field,bs') = parseField bs in 
     case BL.uncons bs' of 
     Just (c,bs1) 
      | isLf c -> (field : [],bs1) 
      | isCr c -> 
       case BL.uncons bs1 of 
       Just (c,bs2) | isLf c -> (field : [],bs2) 
       _ -> (field : [],bs1) 
      | otherwise -> apfst (field :) (parseLine bs1) 
     Nothing -> (field : [],BL.empty) 

    {-# INLINE parseField #-} 
    parseField :: BL.ByteString -> (BSS.ShortByteString, BL.ByteString) 
    parseField bs = 
     case BL.uncons bs of 
     Just (c,bs') 
      | isWrp c -> apfst (BSS.toShort . BL.toStrict . BL.concat) (parseEscaped bs') 
      | otherwise -> apfst (BSS.toShort . BL.toStrict) (BL.break isSep bs) 
     Nothing -> (BSS.empty,BL.empty) 

    {-# INLINE parseEscaped #-} 
    parseEscaped :: BL.ByteString -> ([BL.ByteString], BL.ByteString) 
    parseEscaped bs = 
     let (chunk,bs') = BL.break isWrp bs in 
     case BL.uncons bs' of 
     Just (_,bs1) -> 
      case BL.uncons bs1 of 
      Just (c,bs2) 
       | isWrp c -> apfst (\xs -> chunk : BL.singleton wrp : xs) (parseEscaped bs2) 
       | otherwise -> (chunk : [],bs1) 
      Nothing -> (chunk : [],BL.empty) 
     Nothing -> error "EOF within quoted string" 

aggregate :: [Int] 
      -> Int 
      -> [V.Vector BSS.ShortByteString] 
      -> [V.Vector BSS.ShortByteString] 
aggregate groups size records = 
    let indices = [0..size - 1] in 

    ST.runST $ do 
    state <- HT.new 

    forM_ records (\record -> do 
     let key = V.fromList (map (\g -> record V.! g) groups) 

     existing <- HT.lookup state key 
     case existing of 
      Just x -> 
      forM_ indices (\i -> do 
       current <- MV.read x i 
       MV.write x i $! const current (record V.! i) 
      ) 
      Nothing -> do 
      x <- MV.new size 
      forM_ indices (\i -> MV.write x i $! record V.! i) 
      HT.insert state key x 
    ) 

    HT.toList state >>= T.traverse V.unsafeFreeze . map snd 

filedata :: IO ([Int],Int,[V.Vector BSS.ShortByteString]) 
filedata = do 
    records <- readCSV (CSVFormat ',' '"') 1 "file.csv" 
    return ([0,1,2],18,records) 

main :: IO() 
main = do 
    (key,len,records) <- filedata 
    print (length (aggregate key len records)) 

generateFile :: IO() 
generateFile = do 
    withFile "file.csv" WriteMode $ \handle -> do 
    forM_ [0..650000] $ \_ -> do 
     x <- BL.pack . show . truncate . (* 15) <$> (Random.randomIO :: IO Double) 
     y <- BL.pack . show . truncate . (* 50) <$> (Random.randomIO :: IO Double) 
     z <- BL.pack . show . truncate . (* 200) <$> (Random.randomIO :: IO Double) 
     BL.hPut handle (BL.intercalate "," (x:y:z:replicate 15 (BL.replicate 20 ' '))) 
     BL.hPut handle "\n" 

私は、次のプロファイリング結果受信：

17,525,392,208 bytes allocated in the heap 
27,394,021,360 bytes copied during GC 
    285,382,192 bytes maximum residency (129 sample(s)) 
    3,714,296 bytes maximum slop 
      831 MB total memory in use (0 MB lost due to fragmentation) 

            Tot time (elapsed) Avg pause Max pause 
Gen 0  577 colls,  0 par 1.576s 1.500s  0.0026s 0.0179s 
Gen 1  129 colls,  0 par 25.335s 25.663s  0.1989s 0.2889s 

TASKS: 3 (1 bound, 2 peak workers (2 total), using -N1) 

SPARKS: 0 (0 converted, 0 overflowed, 0 dud, 0 GC'd, 0 fizzled) 

INIT time 0.000s ( 0.002s elapsed) 
MUT  time 11.965s (23.939s elapsed) 
GC  time 15.148s (15.400s elapsed) 
RP  time 0.000s ( 0.000s elapsed) 
PROF time 11.762s (11.763s elapsed) 
EXIT time 0.000s ( 0.088s elapsed) 
Total time 38.922s (39.429s elapsed) 

Alloc rate 1,464,687,582 bytes per MUT second 

Productivity 30.9% of total user, 30.5% of total elapsed 

gc_alloc_block_sync: 0 
whitehole_spin: 0 
gen[0].sync: 0 
gen[1].sync: 0 

そして、次のヒープの可視化：

Answer 1

をこれは、十分に厳格ではないコールがV.!であることが判明しました。 indexMに置き換えると、メモリ消費量が大幅に削減されました。