Tools for analyzing performance of a Haskell program
While solving some Project Euler Problems to learn Haskell (so currently I'm a completly beginner) I came over Problem 13. I wrote this (naive) solution:
--Get Number of Divisors of n
numDivs :: Integer -> Integer
numDivs n = toInteger $ length [ x | x<-[2.. ((n `quot` 2)+1)], n `rem` x == 0] + 2
--Generate a List of Triangular Values
triaList :: [Integer]
triaList = [foldr (+) 0 [1..n] | n <- [1..]]
--The same recursive
triaList2 = go 0 1
where go cs n = (cs+n):go (cs+n) (n+1)
--Finds the first triangular Value with more than n Divisors
sol :: Integer -> Integer
sol n = head $ filter (x -> numDivs(x)>n) triaList2
This Solution for n=500 (sol 500) is extremly slow (running for more than 2 hours now), so I wondered how to find out why this solution is so slow. Are there any commands that tell me where most of the computation-time is spend so I know which part of my haskell-program is slow? Something like a simple profiler.
To make it clear, i'm not asking for a faster solution but for a way to find this solution. How would you start if you would have no haskell knowledge?
I tried to write two triaList functions but found no way to test which one is faster, so thats where my problems start.
Thanks
how to find out why this solution is so slow. Are there any commands that tell me where most of the computation-time is spend so I know which part of my haskell-program is slow?
Precisely! GHC provides many excellent tools, including:
A tutorial on using time and space profiling is part of Real World Haskell.
GC Statistics
Firstly, ensure you're compiling with ghc -O2. And you might make sure it is a modern GHC (eg GHC 6.12.x)
The first thing we can do is check that garbage collection isn't the problem. Run your program with +RTS -s
$ time ./A +RTS -s
./A +RTS -s
749700
9,961,432,992 bytes allocated in the heap
2,463,072 bytes copied during GC
29,200 bytes maximum residency (1 sample(s))
187,336 bytes maximum slop
**2 MB** total memory in use (0 MB lost due to fragmentation)
Generation 0: 19002 collections, 0 parallel, 0.11s, 0.15s elapsed
Generation 1: 1 collections, 0 parallel, 0.00s, 0.00s elapsed
INIT time 0.00s ( 0.00s elapsed)
MUT time 13.15s ( 13.32s elapsed)
GC time 0.11s ( 0.15s elapsed)
RP time 0.00s ( 0.00s elapsed)
PROF time 0.00s ( 0.00s elapsed)
EXIT time 0.00s ( 0.00s elapsed)
Total time 13.26s ( 13.47s elapsed)
%GC time **0.8%** (1.1% elapsed)
Alloc rate 757,764,753 bytes per MUT second
Productivity 99.2% of total user, 97.6% of total elapsed
./A +RTS -s 13.26s user 0.05s system 98% cpu 13.479 total
Which already gives us a lot of information: you only have a 2M heap, and GC takes up 0.8% of time. So no need to worry that allocation is the problem.
Time Profiles
Getting a time profile for your program is straight forward: compile with -prof -auto-all
$ ghc -O2 --make A.hs -prof -auto-all
[1 of 1] Compiling Main ( A.hs, A.o )
Linking A ...
And, for N=200:
$ time ./A +RTS -p
749700
./A +RTS -p 13.23s user 0.06s system 98% cpu 13.547 total
which creates a file, A.prof, containing:
Sun Jul 18 10:08 2010 Time and Allocation Profiling Report (Final)
A +RTS -p -RTS
total time = 13.18 secs (659 ticks @ 20 ms)
total alloc = 4,904,116,696 bytes (excludes profiling overheads)
COST CENTRE MODULE %time %alloc
numDivs Main 100.0 100.0
Indicating that all your time is spent in numDivs, and it is also the source of all your allocations.
Heap Profiles
You can also get a break down of those allocations, by running with +RTS -p -hy, which creates A.hp, which you can view by converting it to a postscript file (hp2ps -c A.hp), generating:
which tells us there's nothing wrong with your memory use: it is allocating in constant space.
So your problem is algorithmic complexity of numDivs:
toInteger $ length [ x | x<-[2.. ((n `quot` 2)+1)], n `rem` x == 0] + 2
Fix that, which is 100% of your running time, and everything else is easy.
Optimizations
This expression is a good candidate for the stream fusion optimization, so I'll rewrite it to use Data.Vector, like so:
numDivs n = fromIntegral $
2 + (U.length $
U.filter (x -> fromIntegral n `rem` x == 0) $
(U.enumFromN 2 ((fromIntegral n `div` 2) + 1) :: U.Vector Int))
Which should fuse into a single loop with no unnecessary heap allocations. That is, it will have better complexity (by constant factors) than the list version. You can use the ghc-core tool (for advanced users) to inspect the intermediate code after optimization.
Testing this, ghc -O2 --make Z.hs
$ time ./Z
749700
./Z 3.73s user 0.01s system 99% cpu 3.753 total
So it reduced running time for N=150 by 3.5x, without changing the algorithm itself.
Conclusion
Your problem is numDivs. It is 100% of your running time, and has terrible complexity. Think about numDivs, and how, for example, for each N you are generating [2 .. n div 2 + 1] N times. Try memoizing that, since the values don't change.
To measure which of your functions is faster, consider using criterion, which will provide statistically robust information about sub-microsecond improvements in running time.
Addenda
Since numDivs is 100% of your running time, touching other parts of the program won't make much difference, however, for pedagogical purposes, we can also rewrite those using stream fusion.
We can also rewrite trialList, and rely on fusion to turn it into the loop you write by hand in trialList2, which is a "prefix scan" function (aka scanl):
triaList = U.scanl (+) 0 (U.enumFrom 1 top)
where
top = 10^6
Similarly for sol:
sol :: Int -> Int
sol n = U.head $ U.filter (x -> numDivs x > n) triaList
With the same overall running time, but a bit cleaner code.
Dons' answer is great without being a spoiler by giving a direct solution to the problem.
Here I want to suggest a little tool that I wrote recently. It saves you the time to write SCC annotations by hand when you want a more detailed profile than the default ghc -prof -auto-all . Besides that it's colorful!
Here's an example with the code you gave(*), green is OK, red is slow: 
All the time goes in creating the list of divisors. This suggests a few things you can do:
1. Make the filtering n rem x == 0 faster, but since it's a built-in function probably it's already fast.
2. Create a shorter list. You've already done something in that direction by checking only up to n quot 2 .
3. Throw away the list generation completely and use some math to get a faster solution. This is the usual way for project Euler problems.
(*) I got this by putting your code in a file called eu13.hs , adding a main function main = print $ sol 90 . Then running visual-prof -px eu13.hs eu13 and the result is in eu13.hs.html .
Haskell related note: triaList2 is of course faster than triaList because the latter performs a lot of unnecessary computations. It will take quadratic time to compute n first elements of triaList , but linear for triaList2 . There is another elegant (and efficient) way to define an infinite lazy list of triangle numbers:
triaList = 1 : zipWith (+) triaList [2..]
Math related note: there is no need to check all divisors up to n / 2, it's enough to check up to sqrt(n).
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