# The &kcachegrind; Handbook

Josef
Weidendorfer

    Josef.Weidendorfer@gmx.de

Original author of the documentation
Federico
Zenith

    federico.zenith@member.fsf.org

Updates and corrections
2002-2004
&Josef.Weidendorfer;
2009
Federico Zenith
&FDLNotice;
2016-11-18
0.8.0 (Applications 17.04)

&kcachegrind; is a profile data visualization tool, written using &kde-frameworks;.

KDE
kdesdk
Cachegrind
Callgrind
Valgrind
Profiling

## Introduction

&kcachegrind; is a browser for data produced by profiling tools. This chapter explains what profiling is for, how it is done, and gives some examples of profiling tools available.

### Profiling

When developing a program, one of the last steps often involves performance optimizations. As it makes no sense to optimize functions rarely used, because that would be a waste of time, one needs to know in which part of a program most of the time is spent.

For sequential code, collecting statistical data of the programs runtime characteristic like time numbers spent in functions and code lines usually is enough. This is called Profiling. The program is run under control of a profiling tool, which gives the summary of an execution run at the end. In contrast, for parallel code, performance problems typically are caused when one processor is waiting for data from another. As this waiting time usually cannot easily attributed, here it is better to generate timestamped event traces. &kcachegrind; cannot visualize this kind of data.

After analyzing the produced profile data, it should be easy to see the hot spots and bottlenecks of the code: for example, assumptions about call counts can be checked, and identified code regions can be optimized. Afterwards, the success of the optimization should be verified with another profile run.

### Profiling Methods

To exactly measure the time passed or record the events happening during the execution of a code region (&eg; a function), additional measurement code needs to be inserted before and after the given region. This code reads the time, or a global event count, and calculates differences. Thus, the original code has to be changed before execution. This is called instrumentation. Instrumentation can be done by the programmer itself, the compiler, or by the runtime system. As interesting regions usually are nested, the overhead of measurement always influences the measurement itself. Thus, instrumentation should be done selectively and results have to be interpreted with care. Of course, this makes performance analysis by exact measurement a very complex process.

Exact measurement is possible because of hardware counters (including counters incrementing on a time tick) provided in modern processors, which are incremented whenever an event is happening. As we want to attribute events to code regions, without the counters, we would have to handle every event by incrementing a counter for the current code region ourself. Doing this in software is, of course, not possible; but, on the assumption that the event distribution over source code is similar when looking only at every n-th event instead of every event, a measurement method whose overhead is tunable has been developed: it is called Sampling. Time Based Sampling (&TBS;) uses a timer to regularly look at the program counter to create a histogram over the program code. Event Based Sampling (&EBS;) exploits the hardware counters of modern processors, and uses a mode where an interrupt handler is called on counter underflow to generate a histogram of the corresponding event distribution: in the handler, the event counter is always reinitialized to the n of the sampling method. The advantage of sampling is that the code does not have to be changed, but it is still a compromise: the above assumption will be more correct if n is small, but the smaller the n, the higher the overhead of the interrupt handler.

Another measurement method is to simulate things happening in the computer system when executing a given code, &ie; execution driven simulation. The simulation is always derived from a more or less accurate machine model; however, with very detailed machine models, giving very close approximations to reality, the simulation time can be unacceptably high in practice. The advantage of simulation is that arbitrarily complex measurement/simulation code can be inserted in a given code without perturbing results. Doing this directly before execution (called runtime instrumentation), using the original binary, is very comfortable for the user: no re-compilation is necessary. Simulation becomes usable when simulating only parts of a machine with a simple model; another advantage is that the results produced by simple models are often easier to understand: often, the problem with real hardware is that results include overlapping effects from different parts of the machine.

### Profiling Tools

Most known is the GCC profiling tool gprof: one needs to compile the program with option `-pg`; running the program generates a file `gmon.out`, which can be transformed into human-readable form with `gprof`. One disadvantage is the required re-compilation step to prepare the executable, which has to be statically linked. The method used here is compiler-generated instrumentation, which measures call arcs happening among functions and corresponding call counts, in conjunction with &TBS;, which gives a histogram of time distribution over the code. Using both pieces of information, it is possible to heuristically calculate inclusive time of functions, &ie; time spent in a function together with all functions called from it.

For exact measurement of events happening, libraries exist with functions able to read out hardware performance counters. Most known here is the PerfCtr patch for &Linux;, and the architecture independent libraries PAPI and PCL. Still, exact measurement needs instrumentation of code, as stated above. Either one uses the libraries itself or uses automatic instrumentation systems like ADAPTOR (for FORTRAN source instrumentation) or DynaProf (code injection via DynInst).

&oprofile; is a system-wide profiling tool for &Linux; using Sampling.

In many aspects, a comfortable way of Profiling is using &cachegrind; or &callgrind;, which are simulators using the runtime instrumentation framework &valgrind;. Because there is no need to access hardware counters (often difficult with today's &Linux; installations), and binaries to be profiled can be left unmodified, it is a good alternative to other profiling tools. The disadvantage of simulation - slowdown - can be reduced by doing the simulation on only the interesting program parts, and perhaps only on a few iterations of a loop. Without measurement/simulation instrumentation, &valgrind;'s usage only has a slowdown factor in the range of 3 to 5. Also, when only the call graph and call counts are of interest, the cache simulator can be switched off.

Cache simulation is the first step in approximating real times, since runtime is very sensitive to the exploitation of so-called caches, small and fast buffers which accelerate repeated accesses to the same main memory cells, on modern systems. &cachegrind; does cache simulation by catching memory accesses. The data produced includes the number of instruction/data memory accesses and first- and second-level cache misses, and relates it to source lines and functions of the run program. By combining these miss counts, using miss latencies from typical processors, an estimation of spent time can be given.

&callgrind; is an extension of &cachegrind; that builds up the call graph of a program on-the-fly, &ie; how the functions call each other and how many events happen while running a function. Also, the profile data to be collected can separated by threads and call chain contexts. It can provide profiling data on an instruction level to allow for annotation of disassembled code.

### Visualization

Profiling tools typically produce a large amount of data. The wish to easily browse down and up the call graph, together with fast switching of the sorting mode of functions and display of different event types, motivates a &GUI; application to accomplish this task.

&kcachegrind; is a visualization tool for profile data fulfilling these wishes. Despite being programmed first with browsing the data from &cachegrind; and &calltree; in mind, there are converters available to be able to display profile data produced by other tools. In the appendix, a description of the &cachegrind;/&callgrind; file format is given.

Besides a list of functions sorted according exclusive or inclusive cost metrics, and optionally grouped by source file, shared library or C++ class, &kcachegrind; features various views for a selected function, namely:

- a call-graph view, which shows a section of the call graph around the selected function,

- a tree-map view, which allows nested-call relations to be visualized, together with inclusive cost metric for fast visual detection of problematic functions,

- source code and disassembler annotation views, allowing to see details of cost related to source lines and assembler instructions.

## Using &kcachegrind;

### Generate Data to Visualize

First, one wants to generate performance data by measuring aspects of the runtime characteristics of an application, using a profiling tool. &kcachegrind; itself does not include any profiling tool, but is good in being used together with &callgrind;, and by using a converter, also can be used to visualize data produced with &oprofile;. Although the scope of this manual is not to document profiling with these tools, the next section provides short quickstart tutorials to get you started.

#### &callgrind;

&callgrind; is a part of &valgrind;. Note that it previously was called &calltree;, but that name was misleading.

The most common use is to prefix the command line to start your application with ``valgrind` `--tool=callgrind`` , as in:

> ``valgrind` `--tool=callgrind` myprogram myargs`

At program termination, a file `callgrind.out.pid` will be generated, which can be loaded into &kcachegrind;.

More advanced use is to dump out profile data whenever a given function of your application is called. E.g. for &konqueror;, to see profile data only for the rendering of a Web page, you could decide to dump the data whenever you select the menu item View Reload . This corresponds to a call to `KonqMainWindow::slotReload`. Use:

> ``valgrind` `--tool=callgrind` `--dump-before=KonqMainWindow::slotReload` konqueror`

This will produce multiple profile data files with an additional sequential number at the end of the filename. A file without such an number at the end (only ending in the process PID) will also be produced; by loading this file into &kcachegrind;, all others are loaded too, and can be seen in the Parts Overview and Parts list.

#### &oprofile;

&oprofile; is available from its home page. Follow the installation instructions on the Web site, but, before you do, check whether your distribution does not already provide it as package (like &SuSE;).

System-wide profiling is only permitted to the root user, as all actions on the system can be observed; therefore, the following has to be done as root. First, configure the profiling process, using the &GUI; `oprof_start` or the command-line tool `opcontrol`. Standard configuration should be timer mode (&TBS;, see introduction). To start the measurement, run ``opcontrol` `-s``. Then run the application you are interested in and, afterwards, do a ``opcontrol` `-d``. This will write out the measurement results into files under folder `/var/lib/oprofile/samples/`. To be able to visualize the data in &kcachegrind;, do in an empty directory:

> ``opreport` `-gdf` | `op2callgrind``

This will produce a lot of files, one for every program which was running on the system. Each one can be loaded into &kcachegrind; on its own.

### User Interface Basics

When starting &kcachegrind; with a profile data file as argument, or after loading one with File Open, you will see a navigation panel containing the function list at the left; and, on the right the main part, an area with views for a selected function. This view area can be arbitrarily configured to show multiple views at once.

At first start, this area will be divided into a top and a bottom part, each with different tab-selectable views. To move views, use the tabs' context menu, and adjust the splitters between views. To switch quickly between different viewing layouts, use &Ctrl;→ View Layout Go to Next and &Ctrl;← View Layout Go to Previous.

The active event type is important for visualization: for &callgrind;, this is, for example, cache misses or cycle estimation; for &oprofile;, this is Timer in the simplest case. You can change the event type via a combobox in the toolbar or in the Event Type view. A first overview of the runtime characteristics should be given when you select function `main` in the left list; look then at the call graph view. There, you see the calls occurring in your program. Note that the call graph view only shows functions with high event count. By double-clicking a function in the graph, it will change to show the called functions around the selected one.

To explore the &GUI; further, in addition to this manual, also have a look at the documentation section on the Web site. Also, every widget in &kcachegrind; has What's this help.

## Basic Concepts

This chapter explains some concepts of the &kcachegrind;, and introduces terms used in the interface.

### The Data Model for Profile Data

#### Cost Entities

Cost counts of event types (like L2 Misses) are attributed to cost entities, which are items with relationship to source code or data structures of a given program. Cost entities not only can be simple code or data positions, but also position tuples. For example, a call has a source and a target, or a data address can have a data type and a code position where its allocation happened.

The cost entities known to &kcachegrind; are given in the following. Simple Positions:

Instruction

  An assembler instruction at a specified address.

Source Line of a Function

  All instructions that the compiler (via debug information) maps to a given source line specified by source file name and line number, and which are executed in the context of some function. The latter is needed because a source line inside of an inlined function can appear in the context of multiple functions. Instructions without any mapping to an actual source line are mapped to line number 0 in file `???`.

Function

  All source lines of a given function make up the function itself. A function is specified by its name and its location in some binary object if available. The latter is needed because binary objects of a single program each can hold functions with the same name (these can be accessed &eg; with `dlopen` or `dlsym`; the runtime linker resolves functions in a given search order of binary objects used). If a profiling tool cannot detect the symbol name of a function, &eg; because debug information is not available, either the address of the first executed instruction typically is used, or `???`.

Binary Object

  All functions whose code is inside the range of a given binary object, either the main executable or a shared library.

Source File

  All functions whose first instruction is mapped to a line of the given source file.

Class

  Symbol names of functions typically are hierarchically ordered in name spaces, &eg; C++ namespaces, or classes of object-oriented languages; thus, a class can hold functions of the class or embedded classes itself.

Profile Part

  Some time section of a profile run, with a given thread ID, process ID, and command line executed.

As can be seen from the list, a set of cost entities often defines another cost entity; thus, there is a inclusion hierarchy of cost entities.

Positions tuples:

- Call from instruction address to target function.

- Call from source line to target function.

- Call from source function to target function.

- (Un)conditional jump from source to target instruction.

- (Un)conditional jump from source to target line.

Jumps between functions are not allowed, as this makes no sense in a call graph; thus, constructs like exception handling and long jumps in C have to be translated to popping the call stack as needed.

#### Event Types

Arbitrary event types can be specified in the profile data by giving them a name. Their cost related to a cost entity is a 64-bit integer.

Event types whose costs are specified in a profile data file are called real events. Additionally, one can specify formulas for event types calculated from real events, which are called inherited events.

### Visualization State

The visualization state of a &kcachegrind; window includes:

- the primary and secondary event type chosen for display,

- the function grouping (used in the Function Profile list and entity coloring),

- the profile parts whose costs are to be included in visualization,

- an active cost entity (&eg; a function selected from the function profile sidedock),

- a selected cost entity.

This state influences the views.

Views are always shown for one cost entity, the active one. When a given view is inappropriate for a cost entity, it is disabled: when selecting &eg; an &ELF; object in the group list, source annotation makes no sense.

For example, for an active function, the callee list shows all the functions called from the active one: one can select one of these functions without making it active. Also, if the call graph is shown beside, it will automatically select the same function.

### Parts of the &GUI;

#### Sidedocks

Sidedocks are side windows which can be placed at any border of a &kcachegrind; window. They always contain a list of cost entities sorted in some way.

- The Function Profile is a list of functions showing inclusive and exclusive cost, call count, name and position of functions.

- Parts Overview

- Call Stack

#### View Area

The view area, typically the right part of a &kcachegrind; main window, is made up of one (default) or more tabs, lined up either horizontally or vertically. Each tab holds different views of only one cost entity at a time. The name of this entity is given at the top of the tab. If there are multiple tabs, only one is active. The entity name in the active tab is shown in bold, and determines the active cost entity of the &kcachegrind; window.

#### Areas of a Tab

Each tab can hold up to four view areas, namely Top, Right, Left, and Bottom. Each area can hold multiple stacked views. The visible part of an area is selected by a tab bar. The tab bars of the top and right area are at the top; the tab bars of the left and bottom area are at the bottom. You can specify which kind of view should go into which area by using the tabs' context menus.

#### Synchronized View with Selected Entity in a Tab

Besides an active entity, each tab has a selected entity. As most view types show multiple entities with the active one somehow centered, you can change the selected item by navigating inside a view (by clicking with the mouse or using the keyboard). Typically, selected items are shown in a highlighted state. By changing the selected entity in one of the views of a tab, all other views highlight the new selected entity accordingly.

#### Synchronization between Tabs

If there are multiple tabs, a selection change in one tab leads to an activation change in the next tab, be it right of the former or under it. This kind of linkage should, for example, allow for fast browsing in call graphs.

#### Layouts

The layout of all the tabs of a window can be saved (View Layout). After duplicating the current layout (&Ctrl; + View Layout Duplicate ) and changing some sizes or moving a view to another area of a tab, you can quickly switch between the old and the new layout via &Ctrl;← and &Ctrl; →. The set of layouts will be stored between &kcachegrind; sessions of the same profiled command. You can make the current set of layouts the default one for new &kcachegrind; sessions, or restore the default layout set.

### Sidedocks

#### Flat Profile

The Flat Profile contains a group list and a function list. The group list contains all groups where cost is spent in, depending on the chosen group type. The group list is hidden when grouping is switched off.

The function list contains the functions of the selected group (or all functions if grouping is switched off), ordered by some column, &eg; inclusive or self costs spent therein. There is a maximum number of functions shown in the list, configurable in Settings Configure KCachegrind.

#### Parts Overview

In a profile run, multiple profile data files can be produced, which can be loaded together into &kcachegrind;. The Parts Overview sidedock shows these, ordered horizontally according to creation time; the rectangle sizes are proportional to the cost spent each part. You can select one or several parts to constrain the costs shown in the other &kcachegrind; views to these parts only.

The parts are further subdivided between a partitioning and an inclusive cost split mode:

Partitioning Mode

  The partitioning is shown in groups for a profile data part, according to the group type selected. For example, if &ELF; object groups are selected, you see colored rectangles for each used &ELF; object (shared library or executable), sized according to the cost spent therein.

Diagram Mode

  A rectangle showing the inclusive cost of the current active function in the part is shown. This, again, is split up to show the inclusive costs of its callees.

#### Call Stack

This is a purely fictional most probable call stack. It is built up by starting with the current active function, and adds the callers and callees with highest cost at the top and to bottom.

The Cost and Calls columns show the cost used for all calls from the function in the line above.

### Views

#### Event Type

The Event Type list shows all cost types available and the corresponding self and inclusive cost of the current active function for that event type.

By choosing an event type from the list, you change the type of costs shown all over &kcachegrind; to the selected one.

#### Call Lists

These lists show calls to and from the current active function. With All Callers and All Callees are meant those functions reachable in the caller and callee direction, even when other functions are in between.

Call list views include:

- Direct Callers

- Direct Callees

- All Callers

- All Callees

#### Maps

A treemap view of the primary event type, up or down the call hierarchy. Each colored rectangle represents a function; its size is approximately proportional to the cost spent therein while the active function is running (however, there are drawing constrains).

For the Caller Map, the graph shows the nested hierarchy of all callers of the currently activated function; for the Callee Map, it shows that of all callees.

Appearance options can be found in the context menu. To get exact size proportions, choose Skip Incorrect Borders. As this mode can be very time-consuming, you may want to limit the maximum drawn nesting level before. Best determinates the split direction for children from the aspect ratio of the parent. Always Best decides on remaining space for each sibling. Ignore Proportions takes space for function name drawing before drawing children. Note that size proportions can get heavily wrong.

Keyboard navigation is available with the left and right arrow keys for traversing siblings, and up and down arrow keys to go a nesting level up and down. &Enter; activates the current item.

#### Call Graph

This view shows the call graph around the active function. The cost shown is only the cost spent while the active function was actually running, &ie; the cost shown for `main()` (if it's visible) should be the same as the cost of the active function, as that is the part of inclusive cost of `main()` spent while the active function was running.

For cycles, blue call arrows indicate that this is an artificial call, which never actually happened, added for correct drawing.

If the graph is larger than the drawing area, a bird's eye view is shown on a side. There are view options similar to those of the call maps; the selected function is highlighted.

#### Annotations

The annotated source or assembler lists show the source lines or disassembled instructions of the current active function together with the (self) cost spent executing the code of a source line or instruction. If there was a call, lines with details on the call are inserted into the source: the (inclusive) cost spent inside of the call, the number of calls happening, and the call destination.

Select such a call information line to activate the call destination.

## Command Reference

### The main &kcachegrind; window

#### The File Menu

&Ctrl;N File New

  Opens an empty top-level window in which you can load profile data. This action is not really necessary, as File Open gives you a new top-level window if the current one already shows some data.

&Ctrl;O File Open

  Pops up the &kde; file selector to choose a profile data file to be loaded. If there is some data already shown in the current top-level window, this will open a new window; if you want to open additional profile data in the current window, use File Add.

  The name of profile data files usually ends in `.pid.part-threadID`, where part and threadID are optional. pid and part are used for multiple profile data files belonging to one application run. By loading a file ending only in `pid`, any existing data files for this run with additional endings are loaded as well.

  If there exist profile data files `cachegrind.out.123` and `cachegrind.out.123.1`, by loading the first, the second will be automatically loaded too.

File Add

  Adds a profile data file to the current window. Using this, you can force multiple data files to be loaded into the same top-level window even if they are not from the same run, as given by the profile data file naming convention. For example, this can be used for side-by-side comparison.

F5 File Reload

  Reload the profile data. This is useful when another profile data file was generated for an already loaded application run.

&Ctrl;Q File Quit

  Quits &kcachegrind;

## Questions and Answers

What is &kcachegrind; for? I have no idea.

&kcachegrind; is a helpful at a late stage in software development, called profiling. If you don't develop applications, you don't need &kcachegrind;.

What is the difference between Incl. and Self?

These are cost attributes for functions regarding some event type. As functions can call each other, it makes sense to distinguish the cost of the function itself (Self Cost) and the cost including all called functions (Inclusive Cost). Self is sometimes also referred to as Exclusive costs.

So, for example, for `main()`, you will always have an inclusive cost of almost 100%, whereas the self cost is negligible when the real work is done in another function.

If I double-click on a function down in the Call Graph view, it shows for function `main()` the same cost as the selected function. Isn't this supposed to be constant at 100%?

You have activated a function below `main()`, which obviously costs less than `main()` itself. For every function, it is shown only the part of the cost spent while the activated function is running; that is, the cost shown for any function can never be higher than the cost of the activated function.

Cost Entity

  An abstract item related to source code to which event counts can be attributed. Dimensions for cost entities are code location (&eg; source line, function), data location (&eg; accessed data type, data object), execution location (&eg; thread, process), and tuples or triples of the aforementioned positions (&eg; calls, object access from statement, evicted data from cache).

Event Costs

  Sum of events of some event type occurring while the execution is related to some cost entity. The cost is attributed to the entity.

Event Type

  The kind of event of which costs can be attributed to a cost entity. There are real event types and inherited event types.

Inherited Event Type

  A virtual event type only visible in the view, defined by a formula to be calculated from real event types.

Profile Data File

  A file containing data measured in a profile experiment, or part of one, or produced by post-processing a trace. Its size is typically linear with the code size of the program.

Profile Data Part

  Data from a profile data file.

Profile Experiment

  A program run supervised by a profiling tool, producing possibly multiple profile data files from parts or threads of the run.

Profile Project

  A configuration for profile experiments used for one program to profile, perhaps in multiple versions. Comparisons of profile data typically only makes sense between profile data produced in experiments of one profile project.

Profiling

  The process of collecting statistical information about runtime characteristics of program runs.

Real Event Type

  An event type that can be measured by a tool. This requires the existence of a sensor for the given event type.

Trace

  A sequence of timestamped events that occurred while tracing a program run. Its size is typically linear with the execution time of the program run.

Trace Part
Tracing

  The process of supervising a program run and storing its events, sorted by a timestamp, in an output file, the trace.

## Credits and License

Thanks to Julian Seward for his excellent &valgrind;, and Nicholas Nethercote for the &cachegrind; addition. Without these programs, &kcachegrind; would not exist. Some ideas for this &GUI; were from them, too.

Thanks for all the bug reports and suggestions from different users.

&underFDL;

&documentation.index;