6. Callgrind: a call-graph generating cache and branch prediction profiler

Cachegrind collects flat profile data: event counts (data reads, cache misses, etc.) are attributed directly to the function they occurred in. This cost attribution mechanism is called self or exclusive attribution.

Callgrind extends this functionality by propagating costs across function call boundaries. If function foo calls bar, the costs from bar are added into foo's costs. When applied to the program as a whole, this builds up a picture of so called inclusive costs, that is, where the cost of each function includes the costs of all functions it called, directly or indirectly.

As an example, the inclusive cost of main should be almost 100 percent of the total program cost. Because of costs arising before main is run, such as initialization of the run time linker and construction of global C++ objects, the inclusive cost of main is not exactly 100 percent of the total program cost.

Together with the call graph, this allows you to find the specific call chains starting from main in which the majority of the program's costs occur. Caller/callee cost attribution is also useful for profiling functions called from multiple call sites, and where optimization opportunities depend on changing code in the callers, in particular by reducing the call count.

Callgrind's cache simulation is based on that of Cachegrind. Read the documentation for Cachegrind: a cache and branch-prediction profiler first. The material below describes the features supported in addition to Cachegrind's features.

Callgrind's ability to detect function calls and returns depends on the instruction set of the platform it is run on. It works best on x86 and amd64, and unfortunately currently does not work so well on PowerPC, ARM, Thumb or MIPS code. This is because there are no explicit call or return instructions in these instruction sets, so Callgrind has to rely on heuristics to detect calls and returns.

As with Cachegrind, you probably want to compile with debugging info (the -g option) and with optimization turned on.

To start a profile run for a program, execute:

valgrind --tool=callgrind [callgrind options] your-program [program options]

While the simulation is running, you can observe execution with:

callgrind_control -b

This will print out the current backtrace. To annotate the backtrace with event counts, run

callgrind_control -e -b

After program termination, a profile data file named callgrind.out.<pid> is generated, where pid is the process ID of the program being profiled. The data file contains information about the calls made in the program among the functions executed, together with Instruction Read (Ir) event counts.

To generate a function-by-function summary from the profile data file, use

callgrind_annotate [options] callgrind.out.<pid>

This summary is similar to the output you get from a Cachegrind run with cg_annotate: the list of functions is ordered by exclusive cost of functions, which also are the ones that are shown. Important for the additional features of Callgrind are the following two options:

Use --auto=yes to get annotated source code for all relevant functions for which the source can be found. In addition to source annotation as produced by cg_annotate, you will see the annotated call sites with call counts. For all other options, consult the (Cachegrind) documentation for cg_annotate.

For better call graph browsing experience, it is highly recommended to use KCachegrind. If your code has a significant fraction of its cost in cycles (sets of functions calling each other in a recursive manner), you have to use KCachegrind, as callgrind_annotate currently does not do any cycle detection, which is important to get correct results in this case.

If you are additionally interested in measuring the cache behavior of your program, use Callgrind with the option --cache-sim=yes. For branch prediction simulation, use --branch-sim=yes. Expect a further slow down approximately by a factor of 2.

If the program section you want to profile is somewhere in the middle of the run, it is beneficial to fast forward to this section without any profiling, and then enable profiling. This is achieved by using the command line option --instr-atstart=no and running, in a shell: callgrind_control -i on just before the interesting code section is executed. To exactly specify the code position where profiling should start, use the client request CALLGRIND_START_INSTRUMENTATION.

If you want to be able to see assembly code level annotation, specify --dump-instr=yes. This will produce profile data at instruction granularity. Note that the resulting profile data can only be viewed with KCachegrind. For assembly annotation, it also is interesting to see more details of the control flow inside of functions, i.e. (conditional) jumps. This will be collected by further specifying --collect-jumps=yes.

Sometimes you are not interested in characteristics of a full program run, but only of a small part of it, for example execution of one algorithm. If there are multiple algorithms, or one algorithm running with different input data, it may even be useful to get different profile information for different parts of a single program run.

Profile data files have names of the form

callgrind.out.pid.part-threadID

where pid is the PID of the running program, part is a number incremented on each dump (".part" is skipped for the dump at program termination), and threadID is a thread identification ("-threadID" is only used if you request dumps of individual threads with --separate-threads=yes).

There are different ways to generate multiple profile dumps while a program is running under Callgrind's supervision. Nevertheless, all methods trigger the same action, which is "dump all profile information since the last dump or program start, and zero cost counters afterwards". To allow for zeroing cost counters without dumping, there is a second action "zero all cost counters now". The different methods are:

If you are running a multi-threaded application and specify the command line option --separate-threads=yes, every thread will be profiled on its own and will create its own profile dump. Thus, the last two methods will only generate one dump of the currently running thread. With the other methods, you will get multiple dumps (one for each thread) on a dump request.

For aggregating events (function enter/leave, instruction execution, memory access) into event numbers, first, the events must be recognizable by Callgrind, and second, the collection state must be enabled.

Event collection is only possible if instrumentation for program code is enabled. This is the default, but for faster execution (identical to valgrind --tool=none), it can be disabled until the program reaches a state in which you want to start collecting profiling data. Callgrind can start without instrumentation by specifying option --instr-atstart=no. Instrumentation can be enabled interactively with:

callgrind_control -i on

and off by specifying "off" instead of "on". Furthermore, instrumentation state can be programatically changed with the macros CALLGRIND_START_INSTRUMENTATION; and CALLGRIND_STOP_INSTRUMENTATION;.

In addition to enabling instrumentation, you must also enable event collection for the parts of your program you are interested in. By default, event collection is enabled everywhere. You can limit collection to a specific function by using --toggle-collect=function. This will toggle the collection state on entering and leaving the specified functions. When this option is in effect, the default collection state at program start is "off". Only events happening while running inside of the given function will be collected. Recursive calls of the given function do not trigger any action.

It is important to note that with instrumentation disabled, the cache simulator cannot see any memory access events, and thus, any simulated cache state will be frozen and wrong without instrumentation. Therefore, to get useful cache events (hits/misses) after switching on instrumentation, the cache first must warm up, probably leading to many cold misses which would not have happened in reality. If you do not want to see these, start event collection a few million instructions after you have enabled instrumentation.

For access to shared data among threads in a multithreaded code, synchronization is required to avoid raced conditions. Synchronization primitives are usually implemented via atomic instructions. However, excessive use of such instructions can lead to performance issues.

To enable analysis of this problem, Callgrind optionally can count the number of atomic instructions executed. More precisely, for x86/x86_64, these are instructions using a lock prefix. For architectures supporting LL/SC, these are the number of SC instructions executed. For both, the term "global bus events" is used.

The short name of the event type used for global bus events is "Ge". To count global bus events, use --collect-bus=yes.

Informally speaking, a cycle is a group of functions which call each other in a recursive way.

Formally speaking, a cycle is a nonempty set S of functions, such that for every pair of functions F and G in S, it is possible to call from F to G (possibly via intermediate functions) and also from G to F. Furthermore, S must be maximal -- that is, be the largest set of functions satisfying this property. For example, if a third function H is called from inside S and calls back into S, then H is also part of the cycle and should be included in S.

Recursion is quite usual in programs, and therefore, cycles sometimes appear in the call graph output of Callgrind. However, the title of this chapter should raise two questions: What is bad about cycles which makes you want to avoid them? And: How can cycles be avoided without changing program code?

Cycles are not bad in itself, but tend to make performance analysis of your code harder. This is because inclusive costs for calls inside of a cycle are meaningless. The definition of inclusive cost, i.e. self cost of a function plus inclusive cost of its callees, needs a topological order among functions. For cycles, this does not hold true: callees of a function in a cycle include the function itself. Therefore, KCachegrind does cycle detection and skips visualization of any inclusive cost for calls inside of cycles. Further, all functions in a cycle are collapsed into artifical functions called like Cycle 1.

Now, when a program exposes really big cycles (as is true for some GUI code, or in general code using event or callback based programming style), you lose the nice property to let you pinpoint the bottlenecks by following call chains from main, guided via inclusive cost. In addition, KCachegrind loses its ability to show interesting parts of the call graph, as it uses inclusive costs to cut off uninteresting areas.

Despite the meaningless of inclusive costs in cycles, the big drawback for visualization motivates the possibility to temporarily switch off cycle detection in KCachegrind, which can lead to misguiding visualization. However, often cycles appear because of unlucky superposition of independent call chains in a way that the profile result will see a cycle. Neglecting uninteresting calls with very small measured inclusive cost would break these cycles. In such cases, incorrect handling of cycles by not detecting them still gives meaningful profiling visualization.

It has to be noted that currently, callgrind_annotate does not do any cycle detection at all. For program executions with function recursion, it e.g. can print nonsense inclusive costs way above 100%.

After describing why cycles are bad for profiling, it is worth talking about cycle avoidance. The key insight here is that symbols in the profile data do not have to exactly match the symbols found in the program. Instead, the symbol name could encode additional information from the current execution context such as recursion level of the current function, or even some part of the call chain leading to the function. While encoding of additional information into symbols is quite capable of avoiding cycles, it has to be used carefully to not cause symbol explosion. The latter imposes large memory requirement for Callgrind with possible out-of-memory conditions, and big profile data files.

A further possibility to avoid cycles in Callgrind's profile data output is to simply leave out given functions in the call graph. Of course, this also skips any call information from and to an ignored function, and thus can break a cycle. Candidates for this typically are dispatcher functions in event driven code. The option to ignore calls to a function is --fn-skip=function. Aside from possibly breaking cycles, this is used in Callgrind to skip trampoline functions in the PLT sections for calls to functions in shared libraries. You can see the difference if you profile with --skip-plt=no. If a call is ignored, its cost events will be propagated to the enclosing function.

If you have a recursive function, you can distinguish the first 10 recursion levels by specifying --separate-recs10=function. Or for all functions with --separate-recs=10, but this will give you much bigger profile data files. In the profile data, you will see the recursion levels of "func" as the different functions with names "func", "func'2", "func'3" and so on.

If you have call chains "A > B > C" and "A > C > B" in your program, you usually get a "false" cycle "B <> C". Use --separate-callers2=B --separate-callers2=C, and functions "B" and "C" will be treated as different functions depending on the direct caller. Using the apostrophe for appending this "context" to the function name, you get "A > B'A > C'B" and "A > C'A > B'C", and there will be no cycle. Use --separate-callers=2 to get a 2-caller dependency for all functions. Note that doing this will increase the size of profile data files.

If your program forks, the child will inherit all the profiling data that has been gathered for the parent. To start with empty profile counter values in the child, the client request CALLGRIND_ZERO_STATS; can be inserted into code to be executed by the child, directly after fork.

However, you will have to make sure that the output file format string (controlled by --callgrind-out-file) does contain %p (which is true by default). Otherwise, the outputs from the parent and child will overwrite each other or will be intermingled, which almost certainly is not what you want.

You will be able to control the new child independently from the parent via callgrind_control.

These options influence the name and format of the profile data files.

These options specify when actions relating to event counts are to be executed. For interactive control use callgrind_control.

These options specify when events are to be aggregated into event counts. Also see Limiting range of event collection.

These options specify how event counts should be attributed to execution contexts. For example, they specify whether the recursion level or the call chain leading to a function should be taken into account, and whether the thread ID should be considered. Also see Avoiding cycles.