[Haskell-beginners] CAF taking all the memory
Ben Gamari
ben at smart-cactus.org
Tue May 3 10:06:38 UTC 2016
Mahdi <mdibaiee at aol.com> writes:
> Hello there,
>
Hi!
> I’m pretty new to Haskell and I’m trying to write a Neural Network in
> Haskell for educational purposes.
>
> So, I have my neural network working, it can learn XOR in 500
> iterations, but the thing is, if I increase the iterations to
> something like 5 milion times, the process just drains my RAM until
> either it’s killed or the OS drowns. Here is the code: [0]
>
> I searched online and tried to get information on why this is
> happening, I profiled the memory usage and found that the memory is
> taken by CAF, searching online,
>
Great! The next question you should then ask is "which CAF"? A CAF is
simply a top-level "constant" in your program. Indeed it sounds like you
have not defined any cost-centers, which means that GHC will attribute
the entire cost of your program to the ambiguous cost-center "CAF"
(which in this case really just means "the whole program").
As discussed in the users guide [1] one way to define cost-centers
within your program is to manually annotate expressions with SCC
pragmas. However, in this case we simply want to let GHC do this for us,
for which we can use the `-fprof-auto -fprof-cafs` flags (which
automatically annotate top-level definitions and CAFs with
cost-centers),
$ ghc -O examples/xor.hs -fprof-auto -fprof-cafs
Now your program should give a much more useful profile (run having set
the iteration count to 50000),
$ time examples/xor +RTS -p
[[0.99548584],[4.5138146e-3],[0.9954874],[4.513808e-3]]
real 0m4.019s
user 0m0.004s
sys 0m4.013s
$ cat xor.prof
Tue May 3 11:15 2016 Time and Allocation Profiling Report (Final)
xor +RTS -p -RTS
total time = 1.11 secs (1107 ticks @ 1000 us, 1 processor)
total alloc = 1,984,202,600 bytes (excludes profiling overheads)
COST CENTRE MODULE %time %alloc
matrixMap Utils.Math 21.4 26.8
matadd Utils.Math 20.8 22.7
matmul.\ Utils.Math 10.4 16.0
dot Utils.Math 7.1 13.4
column Utils.Math 7.0 2.9
dot.\ Utils.Math 6.9 1.9
rowPairs Utils.Math 5.8 6.5
sigmoid' NN 4.7 0.8
train.helper Main 4.0 1.3
sigmoid NN 3.3 0.8
matmul Utils.Math 2.2 2.0
hadamard Utils.Math 1.8 2.1
columns Utils.Math 1.2 1.3
individual inherited
COST CENTRE MODULE no. entries %time %alloc %time %alloc
MAIN MAIN 79 0 0.0 0.0 100.0 100.0
[snip]
CAF:main3 Main 143 0 0.0 0.0 100.0 100.0
(...) Main 162 1 0.0 0.0 100.0 100.0
train Main 163 1 0.0 0.0 100.0 100.0
train.helper Main 164 50001 4.0 1.3 100.0 100.0
train.helper.hweights Main 258 50001 0.5 0.0 0.5 0.0
train.helper.oweights Main 235 50001 0.4 0.0 0.4 0.0
train.helper.oback Main 207 50000 0.3 0.1 19.0 20.9
backward' NN 208 50000 0.3 0.6 18.7 20.8
[snip]
So, here we see that costs are actually spread throughout the program.
Without diving any deeper into this particular program it's hard to give
more guidance however I will say that your lazy list Matrix
representation is very rarely the right choice for even toy linear
algebra problems.
First, consider the fact that even just a list cons constructor requires
three words (an info table pointer, a pointer to payload, and a pointer
to tail) plus the size of the payload (which in the case of an evaluated
`Float` is 2 words: one info table pointer and the floating point value
itself). So, a list of n *evaluated* `Float`s (which would require only
4*n bytes if packed densely) will require 40*n bytes if represented as a
lazy list.
Then, consider the fact that indexing into a lazy list is an O(n)
operation: this means that your `Math.column` operation on an n x m
matrix may be O(n*m). Even worse, `Math.columns`, as used by
`Math.matmul` is O(n * m!).
Finally, consider the fact that whenever you "construct" a lazy list you
aren't actually performing any computation: you are actually
constructing a single thunk which represents the entire result; however,
if you then go to index into the middle of that list you will end up
constructing n cons cells and a thunk for the payload of each. In the
case of primitive linear algebra operations the cost of constructing
this payload thunk can be greater than simply computing the result.
For these reasons I wouldn't recommend that lazy lists are used in this
way. If you have a dense matrix use an array (probably even unboxed;
see, for instance, the `array`, `vector`, and `repa` libraries); if
you have a sparse matrix then use an appropriate sparse representation
(although sadly good sparse linear algebra tools are hard to come by in
Haskell) Not only will the result be significantly more efficient in
space and time but the runtime behavior of the program will be
significantly easier to follow since you can more easily ensure that
evaluation occurs when you expect it to.
Hopefully this helps. Good luck and let us know if there are further
issues.
Cheers,
- Ben
[1] http://downloads.haskell.org/~ghc/master/users-guide//profiling.html#cost-centres-and-cost-centre-stacks
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