Table of Contents
GHC comes with a time and space profiling system, so that you can answer questions like "why is my program so slow?", or "why is my program using so much memory?".
Profiling a program is a three-step process:
Re-compile your program for profiling with the
-prof option, and probably one of the options
for adding automatic annotations:
-fprof-auto is the most common[10].
If you are using external packages with
cabal, you may need to reinstall these
packages with profiling support; typically this is done with
cabal install -p .package
--reinstall
Having compiled the program for profiling, you now need to
run it to generate the profile. For example, a simple time
profile can be generated by running the program with
+RTS
-p, which generates a file named
prog.profprog is the name of your program
(without the .exe extension, if you are on
Windows).
There are many different kinds of profile that can be generated, selected by different RTS options. We will be describing the various kinds of profile throughout the rest of this chapter. Some profiles require further processing using additional tools after running the program.
Examine the generated profiling information, use the information to optimise your program, and repeat as necessary.
GHC's profiling system assigns costs to cost centres. A cost is simply the time or space (memory) required to evaluate an expression. Cost centres are program annotations around expressions; all costs incurred by the annotated expression are assigned to the enclosing cost centre. Furthermore, GHC will remember the stack of enclosing cost centres for any given expression at run-time and generate a call-tree of cost attributions.
Let's take a look at an example:
main = print (fib 30) fib n = if n < 2 then 1 else fib (n-1) + fib (n-2)
Compile and run this program as follows:
$ ghc -prof -fprof-auto -rtsopts Main.hs $ ./Main +RTS -p 121393 $
When a GHC-compiled program is run with the
-p RTS option, it generates a file called
prog.prof
Wed Oct 12 16:14 2011 Time and Allocation Profiling Report (Final)
Main +RTS -p -RTS
total time = 0.68 secs (34 ticks @ 20 ms)
total alloc = 204,677,844 bytes (excludes profiling overheads)
COST CENTRE MODULE %time %alloc
fib Main 100.0 100.0
individual inherited
COST CENTRE MODULE no. entries %time %alloc %time %alloc
MAIN MAIN 102 0 0.0 0.0 100.0 100.0
CAF GHC.IO.Handle.FD 128 0 0.0 0.0 0.0 0.0
CAF GHC.IO.Encoding.Iconv 120 0 0.0 0.0 0.0 0.0
CAF GHC.Conc.Signal 110 0 0.0 0.0 0.0 0.0
CAF Main 108 0 0.0 0.0 100.0 100.0
main Main 204 1 0.0 0.0 100.0 100.0
fib Main 205 2692537 100.0 100.0 100.0 100.0
The first part of the file gives the program name and options, and the total time and total memory allocation measured during the run of the program (note that the total memory allocation figure isn't the same as the amount of live memory needed by the program at any one time; the latter can be determined using heap profiling, which we will describe later in Section 5.4, “Profiling memory usage”).
The second part of the file is a break-down by cost centre
of the most costly functions in the program. In this case, there
was only one significant function in the program, namely
fib, and it was responsible for 100%
of both the time and allocation costs of the program.
The third and final section of the file gives a profile
break-down by cost-centre stack. This is roughly a call-tree
profile of the program. In the example above, it is clear that
the costly call to fib came from
main.
The time and allocation incurred by a given part of the program is displayed in two ways: “individual”, which are the costs incurred by the code covered by this cost centre stack alone, and “inherited”, which includes the costs incurred by all the children of this node.
The usefulness of cost-centre stacks is better demonstrated by modifying the example slightly:
main = print (f 30 + g 30)
where
f n = fib n
g n = fib (n `div` 2)
fib n = if n < 2 then 1 else fib (n-1) + fib (n-2)
Compile and run this program as before, and take a look at the new profiling results:
COST CENTRE MODULE no. entries %time %alloc %time %alloc
MAIN MAIN 102 0 0.0 0.0 100.0 100.0
CAF GHC.IO.Handle.FD 128 0 0.0 0.0 0.0 0.0
CAF GHC.IO.Encoding.Iconv 120 0 0.0 0.0 0.0 0.0
CAF GHC.Conc.Signal 110 0 0.0 0.0 0.0 0.0
CAF Main 108 0 0.0 0.0 100.0 100.0
main Main 204 1 0.0 0.0 100.0 100.0
main.g Main 207 1 0.0 0.0 0.0 0.1
fib Main 208 1973 0.0 0.1 0.0 0.1
main.f Main 205 1 0.0 0.0 100.0 99.9
fib Main 206 2692537 100.0 99.9 100.0 99.9
Now although we had two calls to fib in
the program, it is immediately clear that it was the call from
f which took all the time. The functions
f and g which are defined in
the where clause in main are
given their own cost centres, main.f and
main.g respectively.
The actual meaning of the various columns in the output is:
The number of times this particular point in the call tree was entered.
The percentage of the total run time of the program spent at this point in the call tree.
The percentage of the total memory allocations (excluding profiling overheads) of the program made by this call.
The percentage of the total run time of the program spent below this point in the call tree.
The percentage of the total memory allocations (excluding profiling overheads) of the program made by this call and all of its sub-calls.
In addition you can use the -P RTS option
to
get the following additional information:
ticksThe raw number of time “ticks” which were
attributed to this cost-centre; from this, we get the
%time figure mentioned
above.
bytesNumber of bytes allocated in the heap while in this
cost-centre; again, this is the raw number from which we get
the %alloc figure mentioned
above.
What about recursive functions, and mutually recursive groups of functions? Where are the costs attributed? Well, although GHC does keep information about which groups of functions called each other recursively, this information isn't displayed in the basic time and allocation profile, instead the call-graph is flattened into a tree as follows: a call to a function that occurs elsewhere on the current stack does not push another entry on the stack, instead the costs for this call are aggregated into the caller[11].
Cost centres are just program annotations. When you say
-fprof-auto to the compiler, it automatically
inserts a cost centre annotation around every binding not marked
INLINE in your program, but you are entirely free to add cost
centre annotations yourself.
The syntax of a cost centre annotation is
{-# SCC "name" #-} <expression>
where "name" is an arbitrary string,
that will become the name of your cost centre as it appears
in the profiling output, and
<expression> is any Haskell
expression. An SCC annotation extends as
far to the right as possible when parsing. (SCC stands for "Set
Cost Centre"). The double quotes can be omitted
if name is a Haskell identifier, for example:
{-# SCC my_function #-} <expression>
Here is an example of a program with a couple of SCCs:
main :: IO ()
main = do let xs = [1..1000000]
let ys = [1..2000000]
print $ {-# SCC last_xs #-} last xs
print $ {-# SCC last_init_xs #-} last $ init xs
print $ {-# SCC last_ys #-} last ys
print $ {-# SCC last_init_ys #-}last $ init ys
which gives this profile when run:
COST CENTRE MODULE no. entries %time %alloc %time %alloc MAIN MAIN 102 0 0.0 0.0 100.0 100.0 CAF GHC.IO.Handle.FD 130 0 0.0 0.0 0.0 0.0 CAF GHC.IO.Encoding.Iconv 122 0 0.0 0.0 0.0 0.0 CAF GHC.Conc.Signal 111 0 0.0 0.0 0.0 0.0 CAF Main 108 0 0.0 0.0 100.0 100.0 main Main 204 1 0.0 0.0 100.0 100.0 last_init_ys Main 210 1 25.0 27.4 25.0 27.4 main.ys Main 209 1 25.0 39.2 25.0 39.2 last_ys Main 208 1 12.5 0.0 12.5 0.0 last_init_xs Main 207 1 12.5 13.7 12.5 13.7 main.xs Main 206 1 18.8 19.6 18.8 19.6 last_xs Main 205 1 6.2 0.0 6.2 0.0
While running a program with profiling turned on, GHC maintains a cost-centre stack behind the scenes, and attributes any costs (memory allocation and time) to whatever the current cost-centre stack is at the time the cost is incurred.
The mechanism is simple: whenever the program evaluates an
expression with an SCC annotation, {-# SCC c -#}
E, the cost centre c is pushed on
the current stack, and the entry count for this stack is
incremented by one. The stack also sometimes has to be saved
and restored; in particular when the program creates a
thunk (a lazy suspension), the current
cost-centre stack is stored in the thunk, and restored when the
thunk is evaluated. In this way, the cost-centre stack is
independent of the actual evaluation order used by GHC at
runtime.
At a function call, GHC takes the stack stored in the function being called (which for a top-level function will be empty), and appends it to the current stack, ignoring any prefix that is identical to a prefix of the current stack.
We mentioned earlier that lazy computations, i.e. thunks,
capture the current stack when they are created, and restore
this stack when they are evaluated. What about top-level
thunks? They are "created" when the program is compiled, so
what stack should we give them? The technical name for a
top-level thunk is a CAF ("Constant Applicative Form"). GHC
assigns every CAF in a module a stack consisting of the single
cost centre M.CAF, where M
is the name of the module. It is also possible to give each CAF
a different stack, using the option
-fprof-cafs.
This is especially useful when compiling with
-ffull-laziness (as is default with
-O and higher), as constants in function bodies
will be lifted to the top-level and become CAFs. You will probably
need to consult the Core (-ddump-simpl) in order
to determine what these CAFs correspond to.