我正在尝试在Haskell配置文件输出中解码各种成本中心名称的含义。以下是.prof
文件
COST CENTRE MODULE no. entries %time %alloc %time %alloc
...
runSiT.\.\.readBufResults SiT.SiT 3487 0 0.0 46.3 51.9
...
...
readBuffer.(...) SiT.SiT 3540 1 0.0 0.2 0.0 0.2
readBuffer.tm0_vals SiT.SiT 3539 1 0.0 0.0 0.0 0.0
readBuffer.\ SiT.SiT 3499 0 18.4 12.8 31.0 27.7
...
点似乎是一个点分隔嵌套的成本中心(例如readBuffer.n_threads
表示n_threads
内的绑定readBuffer
),但我不确定其他一些元素。 .\.\.
表示嵌套的lambda函数(例如来自forM_ ... $ \arg -> do
之类的东西)。 但是,(...)
中readBuffer.(...)
的含义是什么?
修改 作为第二个例子,我有:
statsFields.mkStr.\ Main 3801 4 0.0 0.0 0.0 0.0
statsFields.fmtModePct Main 3811 2 0.0 0.0 0.0 0.0
statsFields.fmtModePct.pct_str Main 3815 2 0.0 0.0 0.0 0.0
ssN SiT.SiT 3817 2 0.0 0.0 0.0 0.0
statsFields.fmtPctI Main 3816 2 0.0 0.0 0.0 0.0
statsFields.fmtModePct.(...) Main 3813 2 0.0 0.0 0.0 0.0
ssMode SiT.SiT 3814 2 0.0 0.0 0.0 0.0
statsFields.fmtModePct.m_fq Main 3812 2 0.0 0.0 0.0 0.0
其来源是:
where ...
fmtModePct :: SiTStats -> String
fmtModePct ss = fmtI64 m_fq ++ " (" ++ pct_str ++ ")"
where (m_val,m_fq) = ssMode ss
pct_str = fmtPctI m_fq (ssN ss)
fmtF64 :: Double -> String
fmtF64 = commafy . printf "%.1f"
-- turns 1000 -> 1,000
commafy :: String -> String
commafy str
| head str == '-' = '-':commafy (tail str)
| otherwise = reverse (go (reverse sig)) ++ frac
where (sig,frac) = span (/='.') str
go (a:b:c:cs@(_:_)) = a : b : c : ',' : go cs
go str = str
答案 0 :(得分:2)
(...)表示可重复的操作,如递归调用。在调查我的计划时,我有同样的问题。请看以下简单示例,我以递归方式评估count
和mergeAndCount
:
count :: [Int] -> (Int, [Int])
count [] = (0, [])
count (x:[]) = (0, [x])
count xs =
let halves = splitAt (length xs `div` 2) xs
(ac, a) = count $ fst halves
(bc, b) = count $ snd halves
(mc, merged) = mergeAndCount a b
in
(ac + bc + mc, merged)
mergeAndCount :: [Int] -> [Int] -> (Int, [Int])
mergeAndCount [] [] = (0, [])
mergeAndCount xs [] = (0, xs)
mergeAndCount [] ys = (0, ys)
mergeAndCount xs@(x:xs') ys@(y:ys') =
let (larger, thisCount, (counted, merged))
= if x < y
then (x, 0, mergeAndCount xs' ys)
else (y, length xs, mergeAndCount xs ys')
in
(thisCount + counted, larger : merged)
将生成类似
的分析输出 count Invariant 103 199999 0.1 4.3 99.2 37.5
count.merged Invariant 118 99998 0.0 0.0 0.0 0.0
count.a Invariant 113 99999 0.0 0.0 0.0 0.0
count.b Invariant 112 99999 0.0 0.0 0.0 0.0
count.(...) Invariant 110 99999 0.0 0.0 99.0 25.2
mergeAndCount Invariant 111 1636301 98.9 25.2 99.0 25.2
mergeAndCount.merged Invariant 122 726644 0.0 0.0 0.0 0.0
mergeAndCount.merged Invariant 121 709659 0.0 0.0 0.0 0.0
mergeAndCount.(...) Invariant 120 776644 0.0 0.0 0.0 0.0
mergeAndCount.cnt Invariant 119 776644 0.0 0.0 0.0 0.0
mergeAndCount.(...) Invariant 117 759658 0.0 0.0 0.0 0.0
mergeAndCount.cnt Invariant 116 759658 0.0 0.0 0.0
其中count.merged
表示总体结果,count.a
count.b
成本中心用于功能模式匹配。每次调用(...)
时,此mergeAndCount
清晰可见。
如果您的函数包含许多不同的数据处理方法,那么您的分析输出将与您发送的数据不同且高度相关。