polle changes.

[unix-history] / usr / src / usr.bin / gprof / PSD.doc / present.me

\"	@(#)present.me	1.2 %G%
.sh 1 "Data Presentation"
.pp
The data is presented to the user in two different formats.
The first presentation simply lists the routines
without regard to the amount of time their descendants use.
The second presentation incorporates the call graph of the
program.
.sh 2 "The Flat Profile
.pp
The flat profile consists of a list of all the routines 
that are called during execution of the program,
with the count of the number of times they are called
and the number of seconds of execution time for which they
are themselves accountable.
The routines are listed in decreasing order of execution time.
A list of the routines that are never called during execution of
the program is also available
to verify that nothing important is omitted by
this execution.
The flat profile gives a quick overview of the routines that are used,
and shows the routines that are themselves responsible
for large fractions of the execution time.
In practice,
this profile usually indicates that no single function
is overwhelmingly responsible for 
the total time of the program.
Notice that for this profile,
the individual times sum to the total execution time.
.sh 2 "The Call Graph Profile"
.pp
Ideally, we would like to print the call graph of the program,
but we are limited by the two-dimensional nature of our output
devices.
We cannot assume that a call graph is planar,
and even if it is, that we can print a planar version of it.
Instead, we choose to print each routine,
together with information about
the routines that are its direct parents and children.
This listing presents a window into the call graph.
Based on our experience,
both parent information and child information
is important,
and should be available without searching
through the output.
.pp
The major entries of the call graph profile are the entries from the
flat profile, augmented by the time propagated to each 
routine from its descendants.
This profile is sorted by the sum of the time for the routine
itself plus the time inherited from its descendants.
The profile shows which of the higher level routines 
spend large portions of the total execution time 
in the routines that they call.
For each routine, we show the amount of time passed by each child
to the routine, which includes time for the child itself
and for the descendants of the child
(and thus the descendants of the routine).
We also show the percentage these times represent of the total time
accounted to the child.
Similarly, the parents of each routine are listed, 
along with time,
and percentage of total routine time,
propagated to each one.
.pp
Cycles are handled as single entities.
The cycle as a whole is shown as though it were a single routine,
except that members of the cycle are listed in place of the children.
Although the number of calls of each member
from within the cycle are shown,
they do not affect time propagation.
When a child is a member of a cycle,
the time shown is the appropriate fraction of the time
for the whole cycle.
Self-recursive routines have their calls broken
down into calls from the outside and self-recursive calls.
Only the outside calls affect the propagation of time.
.pp
The following example a typical fragment of a call graph.
.(b
.TS
center;
c c c.
Caller1		Caller2

	Example

Sub1	Sub2	Sub3
.TE
.)b
This example would have the following entry
in the profile listing.
.(b
.TS
box center;
c c c c c l l
c c c c c l l
c c c c c l l
l n n n c l l.
				called/total	\ \ parents
index	%time	self	descendants	called+self	name	index
				called/total	\ \ children
_
		0.20	1.20	4/10	\ \ Caller1	[7]
		0.30	1.80	6/10	\ \ Caller2	[1]
[2]	41.5	0.50	3.00	10+4	Example	[2]
		1.50	1.00	20/40	\ \ Sub1 <cycle1>	[4]
		0.00	0.50	1/5	\ \ Sub2 	[9]
		0.00	0.00	0/5	\ \ Sub3 	[11]
.TE
.)b
.pp
The entry is for routine Example, which has
the Caller routines as its parents,
and the Sub routines as its children.
The reader should keep in mind that all information
is given \fIwith respect to Example\fP.
The index in the first column indicates that Example
is the second entry in the profile listing.
The Example routine is called ten times, four times by Caller1,
and six times by Caller2.
Consequently 40% of Example's time is propagated to Caller1,
and 60% of Example's time is propagated to Caller2.
The self and descendant fields of the parents
show the amount of self and descendant time Example
propagates to them (but not the time used by
the parents directly).
Note that Example calls itself recursively four times.
The routine Example calls routine Sub1 twenty times, Sub2 once,
and never calls Sub3.
Since Sub2 is called a total of five times,
20% of its self and descendant time is propagated to Example's
descendant time field.
Because Sub1 is a member of \fIcycle 1\fR,
the self and descendant times are those for the cycle as a whole.
Since Sub1 is called a total of forty times from outside
of its cycle, it propagates 50% of the cycle's self and descendant
time to Example's descendant time field.
Finally each name is followed by an index that indicates
where on the listing to find the entry for that routine.
.sh 1 "Using the Profiles"
.pp
The profiler is a useful tool for improving
a set of routines that implement an abstraction.
It can be helpful in identifying poorly coded routines,
and in evaluating the new algorithms and code that replace them.
Taking full advantage of the profiler 
requires a careful examination of the call graph profile,
and a thorough knowledge of the abstractions underlying
the program.
.pp
One of the easiest optimizations that can be performed
is a small change
to a control construct or data structure that improves the
running time of the program.
An obvious starting point
is a routine that is called many times.
For example, suppose an output 
routine is the only parent
of a routine that formats the data.
If this format routine is expanded inline in the
output routine, the overhead of a function call and
return can be saved for each datum that needs to be formatted.
.pp
The drawback to inline expansion is that the data abstractions
in the program may become less parameterized,
hence less clearly defined.
The profiling will also become less useful since the loss of 
routines will make its output more granular.
For example,
if the symbol table functions ``lookup'', ``insert'', and ``delete''
are all merged into a single parameterized routine,
it will be impossible to determine the costs
of any one of these individual functions from the profile.
.pp
Further potential for optimization lies in routines that
implement data abstractions whose total execution
time is long.
For example, a lookup routine might be called only a few
times, but use an inefficient linear search algorithm,
that might be replaced with a binary search.
Alternately, the discovery that a rehashing function is being
called excessively, can lead to a different
hash function or a larger hash table.
If the data abstraction function cannot easily be speeded up,
it may be advantageous to cache its results,
and eliminate the need to rerun
it for identical inputs.
.pp
This tool is best used in an iterative approach:
profiling the program,
eliminating one bottleneck,
then finding some other part of the program
that begins to dominate execution time.
For instance, we have used \fBgprof\fR on itself;
eliminating, rewriting, and inline expanding routines,
until reading
data files (hardly a target for optimization!)
represents the dominating factor in its execution time.
.pp
Certain types of programs are not easily analyzed by \fBgprof\fR.
They are typified by programs that exhibit a large degree of 
recursion, such as recursive descent compilers.
The problem is that most of the major routines are grouped
into a single monolithic cycle.
As in the case of the symbol table abstraction that is placed
in one routine,
it is impossible to distinguish which members of the cycle are
responsible for the execution time.
Unfortunately there are no easy modifications to these programs that
make them amenable to analysis.
.pp
A completely different use of the profiler is to analyze the control
flow of an unfamiliar program.
If you receive a program from another user that you need to modify
in some small way,
it is often unclear where the changes need to be made.
By running the program on an example and then using \fBgprof\fR,
you can get a view of the structure of the program.
Consider an example in which you need to change the output format
of the program.
For purposes of this example suppose that the call graph
of the output portion of the program has the following structure:
.(b
.TS
center;
c c c.
calc1	calc2	calc3

format1		format2

	``write''
.TE
.)b
Initially you look through the \fBgprof\fR
output for the system call ``write''.
The format routine you will need to change is probably
among the parents of the ``write'' procedure.
The next step is to look at the profile entry for each
of parents of ``write'',
in this example either ``format1'' or ``format2'',
to determine which one to change.
Each format routine will have one or more parents,
in this example ``calc1'', ``calc2'', and ``calc3''.
By inspecting the source code for each of these routines
you can determine which format routine generates the output that
you wish to modify.
Since the \fBgprof\fR entry indicates all the
potential calls to the format routine you intend to change,
you can determine if your modifications will affect output that
should be left alone.
If you desire to change the output of ``calc2'', but not ``calc3'',
then formatting routine ``format2'' needs to be split
into two separate routines,
one of which implements the new format.
You can then retarget just the call by ``calc2''
that needs the new format.
It should be noted that the static call information is particularly
useful in this case since the test case you run probably will not
exercise the entire program.
.sh 1 "Conclusions"
.pp
We have created a profiler that aids in the evaluation
of modular programs.
For each routine in the program,
the profile shows the extent to which that routine
helps support various abstractions,
and how that routine uses other abstractions.
The profile accurately assesses the cost of routines
at all levels of the program decomposition.
The profiler is easily used,
and does not require any prior planning on the part
of the programmer.
It does not add excessively to either the execution
or the I/O of the program being measured,
and allows programs to be measured in their actual
environments.
Finally, the profiler runs on a time-sharing system 
using only the normal services provided by the operating system.
.(q
polle says:  make sure each of these points is clearly shown by the
paper!  this is a nice clear paragraph and maybe should also appear
(in a different form) earlier to motivate or describe what the paper
will say.
.)q