[Haskell-cafe] Diagnosing stack overflow
haskell at brecknell.org
Sat Aug 18 06:40:15 EDT 2007
> Would "retainer profiling" help me see what was building up
> this large thunk/closure?
I'm not really familiar enough with GHC's profiling to answer that, but
I'll take a guess.
My guess is that profiling will only sometimes be useful in diagnosing
stack overflows, because I suspect that memory stats reported by the
profiler will usually be dominated by heap usage. So profiling *might*
point you towards some big thunks on the heap which might cause a stack
overflow on evaluation. If so, then you're in luck.
But the problem is that you don't actually *need* a huge unevaluated
thunk to cause a stack overflow. Sure, the foldl example had one, but
consider what happens if we use foldr instead:
print (foldr (+) 0 [1..])
=> print (1+(foldr (+) 0 [2..]))
=> print (1+(2+(foldr (+) 0 [3..])))
=> print (1+(2+(3+(foldr (+) 0 [4..]))))
=> print (1+(2+(3+(...+(foldr (+) 0 [...]))))
=> stack overflow
It's a bit more tricky to explain what's going on here, which may be one
reason why foldr is not the usual stack overflow example. While the
nested additions in the foldl example represented a long chain of
unevaluated thunks on the heap, here they represent partially executed
computations on the stack. There is no big thunk! But there are still
many nested contexts on the stack, so we still get an overflow.
Another way of contrasting the foldl and foldr examples is to realise
that foldl always consumes its entire input list, while foldr only
consumes as much as its asked to. In the former, foldl drives the
process of thunk building. In the latter, it is the evaluation of the
innermost (+) function that drives foldr to generate the next iteration.
I suspect that explanation is not very clear, so I give a small
experiment which will at least show that I'm not lying. :-)
Run a basic GHC profile (without optimisations) on each of the
following, and observe the total memory usage. With foldl, memory usage
is very high, because the entire list is consumed to produce a huge
thunk on the heap. With foldr, memory usage is only about 16M, just
enough to blow the stack.
-- trial 1: stack overflow, lots of memory consumed
main = print (foldl (+) 0 [1..10000000] :: Int)
-- trial 2: stack overflow, minimal memory consumption
main = print (foldr (+) 0 [1..10000000] :: Int)
In fact, we could give foldr an infinite list, and get exactly the same
result. Curiously, if we give foldl an infinite list, we don't get a
stack overflow, because we never get to the point of evaluating the
thunk. Instead, we get heap exhaustion, because we just keep building
-- trial 4: heap exhaustion, nasty
main = print (foldl (+) 0 [1..] :: Int)
-- trial 5: stack overflow, minimal memory consumption
main = print (foldr (+) 0 [1..] :: Int)
It's also instructive to run these tests with optimisations (no
profiling), to see how they are affected by strictness analysis. Note
that strictness analysis doesn't work for the default Integer type, so
the Int type annotations are necessary.
More information about the Haskell-Cafe