Heaptrack traces all memory allocations and annotates these events with stack traces.
Dedicated analysis tools then allow you to interpret the heap memory profile to:
find hotspots that need to be optimized to reduce the memory footprint of your application
find memory leaks, i.e. locations that allocate memory which is never deallocated
find allocation hotspots, i.e. code locations that trigger a lot of memory allocation calls
find temporary allocations, which are allocations that are directly followed by their deallocation
Using heaptrack
The recommended way is to launch your application and start tracing from the beginning:
heaptrack <your application and its parameters>
heaptrack output will be written to "/tmp/heaptrack.APP.PID.gz"
starting application, this might take some time...
...
heaptrack stats:
allocations: 65
leaked allocations: 60
temporary allocations: 1
Heaptrack finished! Now run the following to investigate the data:
heaptrack_gui "/tmp/heaptrack.APP.PID.gz"
Alternatively, you can attach to an already running process:
heaptrack --pid $(pidof <your application>)
heaptrack output will be written to "/tmp/heaptrack.APP.PID.gz"
injecting heaptrack into application via GDB, this might take some time...
injection finished
...
Heaptrack finished! Now run the following to investigate the data:
heaptrack_gui "/tmp/heaptrack.APP.PID.gz"
Building heaptrack
Heaptrack is split into two parts: The data collector, i.e. heaptrack itself, and the
analyzer GUI called heaptrack_gui. The following summarizes the dependencies for these
two parts as they can be build independently. You will find corresponding development
packages on all major distributions for these dependencies.
On an embedded device or older Linux distribution, you will only want to build heaptrack.
The data can then be analyzed on a different machine with a more modern Linux distribution
that has access to the required GUI dependencies.
Both parts require the following tools and libraries:
cmake 2.8.9 or higher
a C++11 enabled compiler like g++ or clang++
zlib
optionally: zstd for faster (de)compression
libdl
pthread
libc
heaptrack dependencies
The heaptrack data collector and the simplistic heaptrack_print analyzer depend on the
following libraries:
boost 1.41 or higher: iostreams, program_options
libunwind
For runtime-attaching, you will need gdb installed.
heaptrack_gui dependencies
The graphical user interface to interpret and analyze the data collected by heaptrack
depends on Qt 5 and some KDE libraries:
extra-cmake-modules
Qt 5.2 or higher: Core, Widgets
KDE Frameworks 5: CoreAddons, I18n, ItemModels, ThreadWeaver, ConfigWidgets, KIO, IconThemes
When any of these dependencies is missing, heaptrack_gui will not be build.
Optionally, install the following dependencies to get additional features in
the GUI:
KDiagram: KChart (for chart visualizations)
Compiling
Run the following commands to compile heaptrack. Do pay attention to the output
of the CMake command, as it will tell you about missing dependencies!
cd heaptrack # i.e. the source folder
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release .. # look for messages about missing dependencies!
make -j$(nproc)
Compile heaptrack_gui on macOS using homebrew
heaptrack_print and heaptrack_gui can be built on platforms other than Linux, using the dependencies mentioned above.
On macOS the dependencies can be installed easily using homebrew and the KDE homebrew tap.
To compile make sure to use Qt from homebrew and to have gettext in the path:
cd heaptrack # i.e. the source folder
mkdir build
cd build
CMAKE_PREFIX_PATH=/opt/homebrew/opt/qt@5 PATH=$PATH:/opt/homebrew/opt/gettext/bin cmake ..
cmake -DCMAKE_BUILD_TYPE=Release .. # look for messages about missing dependencies!
make heaptrack_gui heaptrack_print
Interpreting the heap profile
Heaptrack generates data files that are impossible to analyze for a human. Instead, you need
to use either heaptrack_print or heaptrack_gui to interpret the results.
heaptrack_gui
The highly recommended way to analyze a heap profile is by using the heaptrack_gui tool.
It depends on Qt 5 and KF 5 to graphically visualize the recorded data. It features:
a summary page of the data
bottom-up and top-down tree views of the code locations that allocated memory with
their aggregated cost and stack traces
flame graph visualization
graphs of allocation costs over time
heaptrack_print
The heaptrack_print tool is a command line application with minimal dependencies. It takes
the heap profile, analyzes it, and prints the results in ASCII format to the command line.
In its most simple form, you can use it like this:
heaptrack_print heaptrack.APP.PID.gz | less
By default, the report will contain three sections:
MOST CALLS TO ALLOCATION FUNCTIONS
PEAK MEMORY CONSUMERS
MOST TEMPORARY ALLOCATIONS
Each section then lists the top ten hotspots, i.e. code locations that triggered e.g.
the most memory allocations.
Have a look at heaptrack_print --help for changing the output format and other options.
Note that you can use this tool to convert a heaptrack data file to the Massif data format.
You can generate a collapsed stack report for consumption by flamegraph.pl.
Comparison to Valgrind's massif
The idea to build heaptrack was born out of the pain in working with Valgrind's massif.
Valgrind comes with a huge overhead in both memory and time, which sometimes prevent you
from running it on larger real-world applications. Most of what Valgrind does is not
needed for a simple heap profiler.
Advantages of heaptrack over massif
speed and memory overhead
Multi-threaded applications are not serialized when you trace them with heaptrack and
even for single-threaded applications the overhead in both time and memory is significantly
lower. Most notably, you only pay a price when you allocate memory -- time-intensive CPU
calculations are not slowed down at all, contrary to what happens in Valgrind.
more data
Valgrind's massif aggregates data before writing the report. This step loses a lot of
useful information. Most notably, you are not longer able to find out how often memory
was allocated, or where temporary allocations are triggered. Heaptrack does not aggregate the
data until you interpret it, which allows for more useful insights into your allocation patterns.
Advantages of massif over heaptrack
ability to profile page allocations as heap
This allows you to heap-profile applications that use pool allocators that circumvent
malloc & friends. Heaptrack can in principle also profile such applications, but it
requires code changes to annotate the memory pool implementation.
ability to profile stack allocations
This is inherently impossible to implement efficiently in heaptrack as far as I know.
Contributing to heaptrack
As a FOSS project, we welcome contributions of any form. You can help improve the project by:
Libunwind may produce bogus backtraces when unwinding from code linked with old versions of the gold linker.
In such cases, recording with heaptrack seems to work and produces data files. But parsing these data files
with heaptrack_gui will often lead to out-of-memory crashes. Looking at the data with heaptrack_print, one
will see garbage backtraces that are completely broken.
If you encounter such issues, try to relink your application and also libunwind with ld.bfd instead of ld.gold.
You can see if you are affected by running the libunwind unit tests via make check. But do note that you
need to relink your application too, not only libunwind.
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