Profilingprofilingcost-centre profilingGHC 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 was known as -auto-all prior to GHC 7.4.1..
-fprof-autoIf 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
option, which generates a file named
prog.prof where
prog 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.Cost centres and cost-centre stacksGHC'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
RTS option, it generates a file called
prog.prof. In this case, the file
will contain something like this:
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 ).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:entriesThe number of times this particular point in the call
tree was entered.individual %timeThe percentage of the total run time of the program
spent at this point in the call tree.individual %allocThe percentage of the total memory allocations
(excluding profiling overheads) of the program made by this
call.inherited %timeThe percentage of the total run time of the program
spent below this point in the call tree.inherited %allocThe 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 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
callerNote that this policy has changed slightly
in GHC 7.4.1 relative to earlier versions, and may yet change
further, feedback is welcome..Inserting cost centres by handCost centres are just program annotations. When you say
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
Rules for attributing costsWhile 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
.Compiler options for profilingprofilingoptionsoptionsfor profiling
:
To make use of the profiling system
all modules must be compiled and linked
with the option. Any
SCC annotations you've put in your source
will spring to life.Without a option, your
SCCs are ignored; so you can compile
SCC-laden code without changing
it.There are a few other profiling-related compilation options.
Use them in addition to
. These do not have to be used consistently
for all modules in a program.
:
All bindings not marked INLINE,
whether exported or not, top level or nested, will be given
automatic SCC annotations. Functions
marked INLINE must be given a cost centre manually.
:
cost centresautomatically insertingGHC will automatically add SCC
annotations for all top-level bindings not marked INLINE. If
you want a cost centre on an INLINE function, you have to
add it manually.
:
cost centresautomatically insertingGHC will automatically add SCC
annotations for all exported functions not marked
INLINE. If you want a cost centre on an INLINE function, you
have to add it manually.
:
Adds an automatic SCC annotation to
all call sites. This is particularly
useful when using profiling for the purposes of generating
stack traces; see the
function traceStack in the
module Debug.Trace, or
the -xc RTS flag
() for more
details.
:
The costs of all CAFs in a module are usually
attributed to one “big” CAF cost-centre. With
this option, all CAFs get their own cost-centre. An
“if all else fails” option…
:
Disables any previous ,
, or
options.
:
Disables any previous option.
:
Tells GHC not to collect information about how often
functions are entered at runtime (the "entries" column of
the time profile), for this module. This tends to make the
profiled code run faster, and hence closer to the speed of
the unprofiled code, because GHC is able to optimise more
aggressively if it doesn't have to maintain correct entry
counts. This option can be useful if you aren't interested
in the entry counts (for example, if you only intend to do
heap profiling).
Time and allocation profilingTo generate a time and allocation profile, give one of the
following RTS options to the compiled program when you run it (RTS
options should be enclosed between +RTS...-RTS
as usual):
or or :
time profileThe option produces a standard
time profile report. It is written
into the file
program.prof.The option produces a more
detailed report containing the actual time and allocation
data as well. (Not used much.)The option produces the most detailed
report containing all cost centres in addition to the actual time
and allocation data.RTS
optionSets the interval that the RTS clock ticks at, which is
also the sampling interval of the time and allocation profile.
The default is 0.02 seconds.RTS optionThis option causes the runtime to print out the
current cost-centre stack whenever an exception is raised.
This can be particularly useful for debugging the location
of exceptions, such as the notorious Prelude.head:
empty list error. See .Profiling memory usageIn addition to profiling the time and allocation behaviour
of your program, you can also generate a graph of its memory usage
over time. This is useful for detecting the causes of
space leaks, when your program holds on to
more memory at run-time that it needs to. Space leaks lead to
slower execution due to heavy garbage collector activity, and may
even cause the program to run out of memory altogether.To generate a heap profile from your program:Compile the program for profiling ().Run it with one of the heap profiling options described
below (eg. for a basic producer profile).
This generates the file
prog.hp.Run hp2ps to produce a Postscript
file,
prog.ps. The
hp2ps utility is described in detail in
.Display the heap profile using a postscript viewer such
as Ghostview, or print it out on a
Postscript-capable printer.For example, here is a heap profile produced for the program given above in :You might also want to take a look
at hp2any,
a more advanced suite of tools (not distributed with GHC) for
displaying heap profiles.RTS options for heap profilingThere are several different kinds of heap profile that can
be generated. All the different profile types yield a graph of
live heap against time, but they differ in how the live heap is
broken down into bands. The following RTS options select which
break-down to use:RTS option(can be shortened to ). Breaks down the graph by the cost-centre stack which
produced the data.RTS optionBreak down the live heap by the module containing
the code which produced the data.RTS optionBreaks down the graph by closure
description. For actual data, the description
is just the constructor name, for other closures it is a
compiler-generated string identifying the closure.RTS optionBreaks down the graph by
type. For closures which have
function type or unknown/polymorphic type, the string will
represent an approximation to the actual type.RTS optionBreak down the graph by retainer
set. Retainer profiling is described in more
detail below ().RTS optionBreak down the graph by
biography. Biographical profiling
is described in more detail below ().In addition, the profile can be restricted to heap data
which satisfies certain criteria - for example, you might want
to display a profile by type but only for data produced by a
certain module, or a profile by retainer for a certain type of
data. Restrictions are specified as follows:name,...
RTS optionRestrict the profile to closures produced by
cost-centre stacks with one of the specified cost centres
at the top.name,...
RTS optionRestrict the profile to closures produced by
cost-centre stacks with one of the specified cost centres
anywhere in the stack.module,...
RTS optionRestrict the profile to closures produced by the
specified modules.desc,...
RTS optionRestrict the profile to closures with the specified
description strings.type,...
RTS optionRestrict the profile to closures with the specified
types.cc,...
RTS optionRestrict the profile to closures with retainer sets
containing cost-centre stacks with one of the specified
cost centres at the top.bio,...
RTS optionRestrict the profile to closures with one of the
specified biographies, where
bio is one of
lag, drag,
void, or use.For example, the following options will generate a
retainer profile restricted to Branch and
Leaf constructors:prog +RTS -hr -hdBranch,Leaf
There can only be one "break-down" option
(eg. in the example above), but there is no
limit on the number of further restrictions that may be applied.
All the options may be combined, with one exception: GHC doesn't
currently support mixing the and
options.There are three more options which relate to heap
profiling:
:
Set the profiling (sampling) interval to
secs seconds (the default is
0.1 second). Fractions are allowed: for example
will get 5 samples per second.
This only affects heap profiling; time profiles are always
sampled with the frequency of the RTS clock. See
for changing that.RTS optionInclude the memory occupied by threads in a heap
profile. Each thread takes up a small area for its thread
state in addition to the space allocated for its stack
(stacks normally start small and then grow as
necessary).This includes the main thread, so using
is a good way to see how much stack
space the program is using.Memory occupied by threads and their stacks is
labelled as “TSO” and “STACK”
respectively when displaying the profile by closure
description or type description.RTS option
Sets the maximum length of a cost-centre stack name in a
heap profile. Defaults to 25.
Retainer ProfilingRetainer profiling is designed to help answer questions
like why is this data being retained?. We start
by defining what we mean by a retainer:
A retainer is either the system stack, or an unevaluated
closure (thunk).
In particular, constructors are not
retainers.An object B retains object A if (i) B is a retainer object and
(ii) object A can be reached by recursively following pointers
starting from object B, but not meeting any other retainer
objects on the way. Each live object is retained by one or more
retainer objects, collectively called its retainer set, or its
retainer set, or its
retainers.When retainer profiling is requested by giving the program
the option, a graph is generated which is
broken down by retainer set. A retainer set is displayed as a
set of cost-centre stacks; because this is usually too large to
fit on the profile graph, each retainer set is numbered and
shown abbreviated on the graph along with its number, and the
full list of retainer sets is dumped into the file
prog.prof.Retainer profiling requires multiple passes over the live
heap in order to discover the full retainer set for each
object, which can be quite slow. So we set a limit on the
maximum size of a retainer set, where all retainer sets larger
than the maximum retainer set size are replaced by the special
set MANY. The maximum set size defaults to 8
and can be altered with the RTS
option:sizeRestrict the number of elements in a retainer set to
size (default 8).Hints for using retainer profilingThe definition of retainers is designed to reflect a
common cause of space leaks: a large structure is retained by
an unevaluated computation, and will be released once the
computation is forced. A good example is looking up a value in
a finite map, where unless the lookup is forced in a timely
manner the unevaluated lookup will cause the whole mapping to
be retained. These kind of space leaks can often be
eliminated by forcing the relevant computations to be
performed eagerly, using seq or strictness
annotations on data constructor fields.Often a particular data structure is being retained by a
chain of unevaluated closures, only the nearest of which will
be reported by retainer profiling - for example A retains B, B
retains C, and C retains a large structure. There might be a
large number of Bs but only a single A, so A is really the one
we're interested in eliminating. However, retainer profiling
will in this case report B as the retainer of the large
structure. To move further up the chain of retainers, we can
ask for another retainer profile but this time restrict the
profile to B objects, so we get a profile of the retainers of
B:prog +RTS -hr -hcB
This trick isn't foolproof, because there might be other
B closures in the heap which aren't the retainers we are
interested in, but we've found this to be a useful technique
in most cases.Biographical ProfilingA typical heap object may be in one of the following four
states at each point in its lifetime:The lag stage, which is the
time between creation and the first use of the
object,the use stage, which lasts from
the first use until the last use of the object, andThe drag stage, which lasts
from the final use until the last reference to the object
is dropped.An object which is never used is said to be in the
void state for its whole
lifetime.A biographical heap profile displays the portion of the
live heap in each of the four states listed above. Usually the
most interesting states are the void and drag states: live heap
in these states is more likely to be wasted space than heap in
the lag or use states.It is also possible to break down the heap in one or more
of these states by a different criteria, by restricting a
profile by biography. For example, to show the portion of the
heap in the drag or void state by producer: prog +RTS -hc -hbdrag,void
Once you know the producer or the type of the heap in the
drag or void states, the next step is usually to find the
retainer(s):prog +RTS -hr -hccc...
NOTE: this two stage process is required because GHC
cannot currently profile using both biographical and retainer
information simultaneously.Actual memory residencyHow does the heap residency reported by the heap profiler relate to
the actual memory residency of your program when you run it? You might
see a large discrepancy between the residency reported by the heap
profiler, and the residency reported by tools on your system
(eg. ps or top on Unix, or the
Task Manager on Windows). There are several reasons for this:There is an overhead of profiling itself, which is subtracted
from the residency figures by the profiler. This overhead goes
away when compiling without profiling support, of course. The
space overhead is currently 2 extra
words per heap object, which probably results in
about a 30% overhead.Garbage collection requires more memory than the actual
residency. The factor depends on the kind of garbage collection
algorithm in use: a major GC in the standard
generation copying collector will usually require 3L bytes of
memory, where L is the amount of live data. This is because by
default (see the option) we allow the old
generation to grow to twice its size (2L) before collecting it, and
we require additionally L bytes to copy the live data into. When
using compacting collection (see the
option), this is reduced to 2L, and can further be reduced by
tweaking the option. Also add the size of the
allocation area (currently a fixed 512Kb).The stack isn't counted in the heap profile by default. See the
option.The program text itself, the C stack, any non-heap data (eg. data
allocated by foreign libraries, and data allocated by the RTS), and
mmap()'d memory are not counted in the heap profile.<command>hp2ps</command>––heap profile to PostScripthp2psheap profilespostscript, from heap profilesUsage:
hp2ps [flags] [<file>[.hp]]
The program
hp2pshp2ps
program converts a heap profile as produced
by the runtime option into a
PostScript graph of the heap profile. By convention, the file to
be processed by hp2ps has a
.hp extension. The PostScript output is
written to <file>@.ps. If
<file> is omitted entirely, then the
program behaves as a filter.hp2ps is distributed in
ghc/utils/hp2ps in a GHC source
distribution. It was originally developed by Dave Wakeling as part
of the HBC/LML heap profiler.The flags are:In order to make graphs more readable,
hp2ps sorts the shaded bands for each
identifier. The default sort ordering is for the bands with
the largest area to be stacked on top of the smaller ones.
The option causes rougher bands (those
representing series of values with the largest standard
deviations) to be stacked on top of smoother ones.Normally, hp2ps puts the title of
the graph in a small box at the top of the page. However, if
the JOB string is too long to fit in a small box (more than
35 characters), then hp2ps will choose to
use a big box instead. The option
forces hp2ps to use a big box.Generate encapsulated PostScript suitable for
inclusion in LaTeX documents. Usually, the PostScript graph
is drawn in landscape mode in an area 9 inches wide by 6
inches high, and hp2ps arranges for this
area to be approximately centred on a sheet of a4 paper.
This format is convenient of studying the graph in detail,
but it is unsuitable for inclusion in LaTeX documents. The
option causes the graph to be drawn in
portrait mode, with float specifying the width in inches,
millimetres or points (the default). The resulting
PostScript file conforms to the Encapsulated PostScript
(EPS) convention, and it can be included in a LaTeX document
using Rokicki's dvi-to-PostScript converter
dvips.Create output suitable for the gs
PostScript previewer (or similar). In this case the graph is
printed in portrait mode without scaling. The output is
unsuitable for a laser printer.Normally a profile is limited to 20 bands with
additional identifiers being grouped into an
OTHER band. The flag
removes this 20 band and limit, producing as many bands as
necessary. No key is produced as it won't fit!. It is useful
for creation time profiles with many bands.Normally a profile is limited to 20 bands with
additional identifiers being grouped into an
OTHER band. The flag
specifies an alternative band limit (the maximum is
20). requests the band limit to be
removed. As many bands as necessary are produced. However no
key is produced as it won't fit! It is useful for displaying
creation time profiles with many bands.Use previous parameters. By default, the PostScript
graph is automatically scaled both horizontally and
vertically so that it fills the page. However, when
preparing a series of graphs for use in a presentation, it
is often useful to draw a new graph using the same scale,
shading and ordering as a previous one. The
flag causes the graph to be drawn using
the parameters determined by a previous run of
hp2ps on file. These
are extracted from file@.aux.Use a small box for the title.Normally trace elements which sum to a total of less
than 1% of the profile are removed from the
profile. The option allows this
percentage to be modified (maximum 5%). requests no trace elements to be
removed from the profile, ensuring that all the data will be
displayed.Generate colour output.Ignore marks.Print out usage information.Manipulating the hp file(Notes kindly offered by Jan-Willem Maessen.)
The FOO.hp file produced when you ask for the
heap profile of a program FOO is a text file with a particularly
simple structure. Here's a representative example, with much of the
actual data omitted:
JOB "FOO -hC"
DATE "Thu Dec 26 18:17 2002"
SAMPLE_UNIT "seconds"
VALUE_UNIT "bytes"
BEGIN_SAMPLE 0.00
END_SAMPLE 0.00
BEGIN_SAMPLE 15.07
... sample data ...
END_SAMPLE 15.07
BEGIN_SAMPLE 30.23
... sample data ...
END_SAMPLE 30.23
... etc.
BEGIN_SAMPLE 11695.47
END_SAMPLE 11695.47
The first four lines (JOB, DATE, SAMPLE_UNIT, VALUE_UNIT) form a
header. Each block of lines starting with BEGIN_SAMPLE and ending
with END_SAMPLE forms a single sample (you can think of this as a
vertical slice of your heap profile). The hp2ps utility should accept
any input with a properly-formatted header followed by a series of
*complete* samples.
Zooming in on regions of your profile
You can look at particular regions of your profile simply by loading a
copy of the .hp file into a text editor and deleting the unwanted
samples. The resulting .hp file can be run through hp2ps and viewed
or printed.
Viewing the heap profile of a running program
The .hp file is generated incrementally as your
program runs. In principle, running hp2ps on the incomplete file
should produce a snapshot of your program's heap usage. However, the
last sample in the file may be incomplete, causing hp2ps to fail. If
you are using a machine with UNIX utilities installed, it's not too
hard to work around this problem (though the resulting command line
looks rather Byzantine):
head -`fgrep -n END_SAMPLE FOO.hp | tail -1 | cut -d : -f 1` FOO.hp \
| hp2ps > FOO.ps
The command fgrep -n END_SAMPLE FOO.hp finds the
end of every complete sample in FOO.hp, and labels each sample with
its ending line number. We then select the line number of the last
complete sample using tail and cut. This is used as a
parameter to head; the result is as if we deleted the final
incomplete sample from FOO.hp. This results in a properly-formatted
.hp file which we feed directly to hp2ps.
Viewing a heap profile in real time
The gv and ghostview programs
have a "watch file" option can be used to view an up-to-date heap
profile of your program as it runs. Simply generate an incremental
heap profile as described in the previous section. Run gv on your
profile:
gv -watch -seascape FOO.ps
If you forget the -watch flag you can still select
"Watch file" from the "State" menu. Now each time you generate a new
profile FOO.ps the view will update automatically.
This can all be encapsulated in a little script:
#!/bin/sh
head -`fgrep -n END_SAMPLE FOO.hp | tail -1 | cut -d : -f 1` FOO.hp \
| hp2ps > FOO.ps
gv -watch -seascape FOO.ps &
while [ 1 ] ; do
sleep 10 # We generate a new profile every 10 seconds.
head -`fgrep -n END_SAMPLE FOO.hp | tail -1 | cut -d : -f 1` FOO.hp \
| hp2ps > FOO.ps
done
Occasionally gv will choke as it tries to read an incomplete copy of
FOO.ps (because hp2ps is still running as an update
occurs). A slightly more complicated script works around this
problem, by using the fact that sending a SIGHUP to gv will cause it
to re-read its input file:
#!/bin/sh
head -`fgrep -n END_SAMPLE FOO.hp | tail -1 | cut -d : -f 1` FOO.hp \
| hp2ps > FOO.ps
gv FOO.ps &
gvpsnum=$!
while [ 1 ] ; do
sleep 10
head -`fgrep -n END_SAMPLE FOO.hp | tail -1 | cut -d : -f 1` FOO.hp \
| hp2ps > FOO.ps
kill -HUP $gvpsnum
done
Profiling Parallel and Concurrent ProgramsCombining
and is perfectly fine, and indeed it is
possible to profile a program running on multiple processors
with the option.This feature
was added in GHC 7.4.1.
Some caveats apply, however. In the current implementation, a
profiled program is likely to scale much less well than the
unprofiled program, because the profiling implementation uses
some shared data structures which require locking in the runtime
system. Furthermore, the memory allocation statistics collected
by the profiled program are stored in shared memory
but not locked (for speed), which means
that these figures might be inaccurate for parallel programs.
We strongly recommend that you
use when compiling a
program to be profiled on multiple cores, because the entry
counts are also stored in shared memory, and continuously
updating them on multiple cores is extremely slow.
We also recommend
using ThreadScope
for profiling parallel programs; it offers a GUI for visualising
parallel execution, and is complementary to the time and space
profiling features provided with GHC.
Observing Code Coveragecode coverageHaskell Program Coveragehpc
Code coverage tools allow a programmer to determine what parts
of their code have been actually executed, and which parts have
never actually been invoked. GHC has an option for generating
instrumented code that records code coverage as part of the
Haskell Program Coverage (HPC) toolkit, which is included with
GHC. HPC tools can be used to render the generated code coverage
information into human understandable format.
Correctly instrumented code provides coverage information of two
kinds: source coverage and boolean-control coverage. Source
coverage is the extent to which every part of the program was
used, measured at three different levels: declarations (both
top-level and local), alternatives (among several equations or
case branches) and expressions (at every level). Boolean
coverage is the extent to which each of the values True and
False is obtained in every syntactic boolean context (ie. guard,
condition, qualifier).
HPC displays both kinds of information in two primary ways:
textual reports with summary statistics (hpc report) and sources
with color mark-up (hpc markup). For boolean coverage, there
are four possible outcomes for each guard, condition or
qualifier: both True and False values occur; only True; only
False; never evaluated. In hpc-markup output, highlighting with
a yellow background indicates a part of the program that was
never evaluated; a green background indicates an always-True
expression and a red background indicates an always-False one.
A small example: Reciprocation
For an example we have a program, called Recip.hs, which computes exact decimal
representations of reciprocals, with recurring parts indicated in
brackets.
reciprocal :: Int -> (String, Int)
reciprocal n | n > 1 = ('0' : '.' : digits, recur)
| otherwise = error
"attempting to compute reciprocal of number <= 1"
where
(digits, recur) = divide n 1 []
divide :: Int -> Int -> [Int] -> (String, Int)
divide n c cs | c `elem` cs = ([], position c cs)
| r == 0 = (show q, 0)
| r /= 0 = (show q ++ digits, recur)
where
(q, r) = (c*10) `quotRem` n
(digits, recur) = divide n r (c:cs)
position :: Int -> [Int] -> Int
position n (x:xs) | n==x = 1
| otherwise = 1 + position n xs
showRecip :: Int -> String
showRecip n =
"1/" ++ show n ++ " = " ++
if r==0 then d else take p d ++ "(" ++ drop p d ++ ")"
where
p = length d - r
(d, r) = reciprocal n
main = do
number <- readLn
putStrLn (showRecip number)
main
HPC instrumentation is enabled with the -fhpc flag:
$ ghc -fhpc Recip.hs
GHC creates a subdirectory .hpc in the
current directory, and puts HPC index (.mix)
files in there, one for each module compiled. You don't need to
worry about these files: they contain information needed by the
hpc tool to generate the coverage data for
compiled modules after the program is run.
$ ./Recip
1/3
= 0.(3)
Running the program generates a file with the
.tix suffix, in this case
Recip.tix, which contains the coverage data
for this run of the program. The program may be run multiple
times (e.g. with different test data), and the coverage data from
the separate runs is accumulated in the .tix
file. To reset the coverage data and start again, just remove the
.tix file.
Having run the program, we can generate a textual summary of
coverage:
$ hpc report Recip
80% expressions used (81/101)
12% boolean coverage (1/8)
14% guards (1/7), 3 always True,
1 always False,
2 unevaluated
0% 'if' conditions (0/1), 1 always False
100% qualifiers (0/0)
55% alternatives used (5/9)
100% local declarations used (9/9)
100% top-level declarations used (5/5)
We can also generate a marked-up version of the source.
$ hpc markup Recip
writing Recip.hs.html
This generates one file per Haskell module, and 4 index files,
hpc_index.html, hpc_index_alt.html, hpc_index_exp.html,
hpc_index_fun.html.
Options for instrumenting code for coverageEnable code coverage for the current module or modules
being compiled.Modules compiled with this option can be freely mixed
with modules compiled without it; indeed, most libraries
will typically be compiled without .
When the program is run, coverage data will only be
generated for those modules that were compiled with
, and the hpc tool
will only show information about those modules.
The hpc toolkitThe hpc command has several sub-commands:
$ hpc
Usage: hpc COMMAND ...
Commands:
help Display help for hpc or a single command
Reporting Coverage:
report Output textual report about program coverage
markup Markup Haskell source with program coverage
Processing Coverage files:
sum Sum multiple .tix files in a single .tix file
combine Combine two .tix files in a single .tix file
map Map a function over a single .tix file
Coverage Overlays:
overlay Generate a .tix file from an overlay file
draft Generate draft overlay that provides 100% coverage
Others:
show Show .tix file in readable, verbose format
version Display version for hpc
In general, these options act on a
.tix file after an instrumented binary has
generated it.
The hpc tool assumes you are in the top-level directory of
the location where you built your application, and the .tix
file is in the same top-level directory. You can use the
flag to use hpc for any other directory, and use
multiple times to analyse programs compiled from
difference locations, as is typical for packages.
We now explain in more details the major modes of hpc.
hpc reporthpc report gives a textual report of coverage. By default,
all modules and packages are considered in generating report,
unless include or exclude are used. The report is a summary
unless the flag is used. The option
allows for tools to use hpc to glean coverage.
$ hpc help report
Usage: hpc report [OPTION] .. <TIX_FILE> [<MODULE> [<MODULE> ..]]
Options:
--per-module show module level detail
--decl-list show unused decls
--exclude=[PACKAGE:][MODULE] exclude MODULE and/or PACKAGE
--include=[PACKAGE:][MODULE] include MODULE and/or PACKAGE
--srcdir=DIR path to source directory of .hs files
multi-use of srcdir possible
--hpcdir=DIR sub-directory that contains .mix files
default .hpc [rarely used]
--xml-output show output in XML
hpc markuphpc markup marks up source files into colored html.
$ hpc help markup
Usage: hpc markup [OPTION] .. <TIX_FILE> [<MODULE> [<MODULE> ..]]
Options:
--exclude=[PACKAGE:][MODULE] exclude MODULE and/or PACKAGE
--include=[PACKAGE:][MODULE] include MODULE and/or PACKAGE
--srcdir=DIR path to source directory of .hs files
multi-use of srcdir possible
--hpcdir=DIR sub-directory that contains .mix files
default .hpc [rarely used]
--fun-entry-count show top-level function entry counts
--highlight-covered highlight covered code, rather that code gaps
--destdir=DIR path to write output to
hpc sumhpc sum adds together any number of .tix files into a single
.tix file. hpc sum does not change the original .tix file; it generates a new .tix file.
$ hpc help sum
Usage: hpc sum [OPTION] .. <TIX_FILE> [<TIX_FILE> [<TIX_FILE> ..]]
Sum multiple .tix files in a single .tix file
Options:
--exclude=[PACKAGE:][MODULE] exclude MODULE and/or PACKAGE
--include=[PACKAGE:][MODULE] include MODULE and/or PACKAGE
--output=FILE output FILE
--union use the union of the module namespace (default is intersection)
hpc combinehpc combine is the swiss army knife of hpc. It can be
used to take the difference between .tix files, to subtract one
.tix file from another, or to add two .tix files. hpc combine does not
change the original .tix file; it generates a new .tix file.
$ hpc help combine
Usage: hpc combine [OPTION] .. <TIX_FILE> <TIX_FILE>
Combine two .tix files in a single .tix file
Options:
--exclude=[PACKAGE:][MODULE] exclude MODULE and/or PACKAGE
--include=[PACKAGE:][MODULE] include MODULE and/or PACKAGE
--output=FILE output FILE
--function=FUNCTION combine .tix files with join function, default = ADD
FUNCTION = ADD | DIFF | SUB
--union use the union of the module namespace (default is intersection)
hpc maphpc map inverts or zeros a .tix file. hpc map does not
change the original .tix file; it generates a new .tix file.
$ hpc help map
Usage: hpc map [OPTION] .. <TIX_FILE>
Map a function over a single .tix file
Options:
--exclude=[PACKAGE:][MODULE] exclude MODULE and/or PACKAGE
--include=[PACKAGE:][MODULE] include MODULE and/or PACKAGE
--output=FILE output FILE
--function=FUNCTION apply function to .tix files, default = ID
FUNCTION = ID | INV | ZERO
--union use the union of the module namespace (default is intersection)
hpc overlay and hpc draft
Overlays are an experimental feature of HPC, a textual description
of coverage. hpc draft is used to generate a draft overlay from a .tix file,
and hpc overlay generates a .tix files from an overlay.
% hpc help overlay
Usage: hpc overlay [OPTION] .. <OVERLAY_FILE> [<OVERLAY_FILE> [...]]
Options:
--srcdir=DIR path to source directory of .hs files
multi-use of srcdir possible
--hpcdir=DIR sub-directory that contains .mix files
default .hpc [rarely used]
--output=FILE output FILE
% hpc help draft
Usage: hpc draft [OPTION] .. <TIX_FILE>
Options:
--exclude=[PACKAGE:][MODULE] exclude MODULE and/or PACKAGE
--include=[PACKAGE:][MODULE] include MODULE and/or PACKAGE
--srcdir=DIR path to source directory of .hs files
multi-use of srcdir possible
--hpcdir=DIR sub-directory that contains .mix files
default .hpc [rarely used]
--output=FILE output FILE
Caveats and Shortcomings of Haskell Program Coverage
HPC does not attempt to lock the .tix file, so multiple concurrently running
binaries in the same directory will exhibit a race condition. There is no way
to change the name of the .tix file generated, apart from renaming the binary.
HPC does not work with GHCi.
Using “ticky-ticky” profiling (for implementors)ticky-ticky profiling(ToDo: document properly.)It is possible to compile Haskell programs so that
they will count lots and lots of interesting things, e.g., number
of updates, number of data constructors entered, etc., etc. We
call this “ticky-ticky”
profiling,ticky-ticky
profilingprofiling,
ticky-ticky because that's the sound a CPU
makes when it is running up all those counters
(slowly).Ticky-ticky profiling is mainly intended for implementors;
it is quite separate from the main “cost-centre”
profiling system, intended for all users everywhere.
You don't need to build GHC, the libraries, or the RTS a special
way in order to use ticky-ticky profiling. You can decide on a
module-by-module basis which parts of a program have the
counters compiled in, using the
compile-time option. Those modules that
were not compiled with won't contribute
to the ticky-ticky profiling results, and that will normally
include all the pre-compiled packages that your program links
with.
To get your compiled program to spit out the ticky-ticky
numbers:
Link the program with
( is a synonym
for at link-time). This links in
the debug version of the RTS, which includes the code for
aggregating and reporting the results of ticky-ticky
profiling.
Run the program with the RTS
option-r RTS option.
See .
Here is a sample ticky-ticky statistics file, generated by
the invocation
foo +RTS -rfoo.ticky.
foo +RTS -rfoo.ticky
ALLOCATIONS: 3964631 (11330900 words total: 3999476 admin, 6098829 goods, 1232595 slop)
total words: 2 3 4 5 6+
69647 ( 1.8%) function values 50.0 50.0 0.0 0.0 0.0
2382937 ( 60.1%) thunks 0.0 83.9 16.1 0.0 0.0
1477218 ( 37.3%) data values 66.8 33.2 0.0 0.0 0.0
0 ( 0.0%) big tuples
2 ( 0.0%) black holes 0.0 100.0 0.0 0.0 0.0
0 ( 0.0%) prim things
34825 ( 0.9%) partial applications 0.0 0.0 0.0 100.0 0.0
2 ( 0.0%) thread state objects 0.0 0.0 0.0 0.0 100.0
Total storage-manager allocations: 3647137 (11882004 words)
[551104 words lost to speculative heap-checks]
STACK USAGE:
ENTERS: 9400092 of which 2005772 (21.3%) direct to the entry code
[the rest indirected via Node's info ptr]
1860318 ( 19.8%) thunks
3733184 ( 39.7%) data values
3149544 ( 33.5%) function values
[of which 1999880 (63.5%) bypassed arg-satisfaction chk]
348140 ( 3.7%) partial applications
308906 ( 3.3%) normal indirections
0 ( 0.0%) permanent indirections
RETURNS: 5870443
2137257 ( 36.4%) from entering a new constructor
[the rest from entering an existing constructor]
2349219 ( 40.0%) vectored [the rest unvectored]
RET_NEW: 2137257: 32.5% 46.2% 21.3% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
RET_OLD: 3733184: 2.8% 67.9% 29.3% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
RET_UNBOXED_TUP: 2: 0.0% 0.0%100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
RET_VEC_RETURN : 2349219: 0.0% 0.0%100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
UPDATE FRAMES: 2241725 (0 omitted from thunks)
SEQ FRAMES: 1
CATCH FRAMES: 1
UPDATES: 2241725
0 ( 0.0%) data values
34827 ( 1.6%) partial applications
[2 in place, 34825 allocated new space]
2206898 ( 98.4%) updates to existing heap objects (46 by squeezing)
UPD_CON_IN_NEW: 0: 0 0 0 0 0 0 0 0 0
UPD_PAP_IN_NEW: 34825: 0 0 0 34825 0 0 0 0 0
NEW GEN UPDATES: 2274700 ( 99.9%)
OLD GEN UPDATES: 1852 ( 0.1%)
Total bytes copied during GC: 190096
**************************************************
3647137 ALLOC_HEAP_ctr
11882004 ALLOC_HEAP_tot
69647 ALLOC_FUN_ctr
69647 ALLOC_FUN_adm
69644 ALLOC_FUN_gds
34819 ALLOC_FUN_slp
34831 ALLOC_FUN_hst_0
34816 ALLOC_FUN_hst_1
0 ALLOC_FUN_hst_2
0 ALLOC_FUN_hst_3
0 ALLOC_FUN_hst_4
2382937 ALLOC_UP_THK_ctr
0 ALLOC_SE_THK_ctr
308906 ENT_IND_ctr
0 E!NT_PERM_IND_ctr requires +RTS -Z
[... lots more info omitted ...]
0 GC_SEL_ABANDONED_ctr
0 GC_SEL_MINOR_ctr
0 GC_SEL_MAJOR_ctr
0 GC_FAILED_PROMOTION_ctr
47524 GC_WORDS_COPIED_ctr
The formatting of the information above the row of asterisks
is subject to change, but hopefully provides a useful
human-readable summary. Below the asterisks all
counters maintained by the ticky-ticky system are
dumped, in a format intended to be machine-readable: zero or more
spaces, an integer, a space, the counter name, and a newline.In fact, not all counters are
necessarily dumped; compile- or run-time flags can render certain
counters invalid. In this case, either the counter will simply
not appear, or it will appear with a modified counter name,
possibly along with an explanation for the omission (notice
ENT_PERM_IND_ctr appears
with an inserted ! above). Software analysing
this output should always check that it has the counters it
expects. Also, beware: some of the counters can have
large values!