This section shows how to profile an Android application. Some examples are Here.
Profiling an Android application involves three steps:
Based on the profiling situation, we may need to customize the build script to generate an apk file specifically for profiling. Below are some suggestions.
For the debug build type, Android studio sets android::debuggable=“true” in AndroidManifest.xml, enables JNI checks and may not optimize C/C++ code. It can be profiled by simpleperf without any change.
For the release build type, Android studio sets android::debuggable=“false” in AndroidManifest.xml, disables JNI checks and optimizes C/C++ code. However, security restrictions mean that only apps with android::debuggable set to true can be profiled. So simpleperf can only profile a release build under these three circumstances: If you are on a rooted device, you can profile any app.
If you are on Android >= Q, you can add profileableFromShell flag in AndroidManifest.xml, this makes a released app profileable by preinstalled profiling tools. In this case, simpleperf downloaded by adb will invoke simpleperf preinstalled in system image to profile the app.
<manifest ...>
<application ...>
<profileable android:shell="true" />
</application>
</manifest>
If you are on Android >= O, we can use wrap.sh to profile a release build: Step 1: Add android::debuggable=“true” in AndroidManifest.xml to enable profiling.
<manifest ...>
<application android::debuggable="true" ...>
Step 2: Add wrap.sh in lib/arch directories. wrap.sh runs the app without passing any debug flags to ART, so the app runs as a release app. wrap.sh can be done by adding the script below in app/build.gradle.
android {
buildTypes {
release {
sourceSets {
release {
resources {
srcDir {
"wrap_sh_lib_dir"
}
}
}
}
}
}
}
task createWrapShLibDir
for (String abi : ["armeabi", "armeabi-v7a", "arm64-v8a", "x86", "x86_64"]) {
def dir = new File("app/wrap_sh_lib_dir/lib/" + abi)
dir.mkdirs()
def wrapFile = new File(dir, "wrap.sh")
wrapFile.withWriter { writer ->
writer.write('#!/system/bin/sh\n\$@\n')
}
}
}
Android studio strips symbol table and debug info of native libraries in the apk. So the profiling results may contain unknown symbols or broken callgraphs. To fix this, we can pass app_profiler.py a directory containing unstripped native libraries via the -lib option. Usually the directory can be the path of your Android Studio project.
On Android >= P, simpleperf supports profiling Java code, no matter whether it is executed by the interpreter, or JITed, or compiled into native instructions. So you don't need to do anything.
On Android O, simpleperf supports profiling Java code which is compiled into native instructions, and it also needs wrap.sh to use the compiled Java code. To compile Java code, we can pass app_profiler.py the --compile_java_code option.
On Android N, simpleperf supports profiling Java code that is compiled into native instructions. To compile java code, we can pass app_profiler.py the --compile_java_code option.
On Android <= M, simpleperf doesn't support profiling Java code.
Below I use application SimpleperfExampleCpp. It builds an app-debug.apk for profiling.
$ git clone https://android.googlesource.com/platform/system/extras $ cd extras/simpleperf/demo # Open SimpleperfExampleCpp project with Android studio, and build this project # successfully, otherwise the `./gradlew` command below will fail. $ cd SimpleperfExampleCpp # On windows, use "gradlew" instead. $ ./gradlew clean assemble $ adb install -r app/build/outputs/apk/debug/app-debug.apk
We can use app-profiler.py to profile Android applications.
# Cd to the directory of simpleperf scripts. Record perf.data. # -p option selects the profiled app using its package name. # --compile_java_code option compiles Java code into native instructions, which isn't needed on # Android >= P. # -a option selects the Activity to profile. # -lib option gives the directory to find debug native libraries. $ ./app_profiler.py -p simpleperf.example.cpp -a .MixActivity -lib path_of_SimpleperfExampleCpp
This will collect profiling data in perf.data in the current directory, and related native binaries in binary_cache/.
Normally we need to use the app when profiling, otherwise we may record no samples. But in this case, the MixActivity starts a busy thread. So we don't need to use the app while profiling.
# Report perf.data in stdio interface. $ ./report.py Cmdline: /data/data/simpleperf.example.cpp/simpleperf record ... Arch: arm64 Event: task-clock:u (type 1, config 1) Samples: 10023 Event count: 10023000000 Overhead Command Pid Tid Shared Object Symbol 27.04% BusyThread 5703 5729 /system/lib64/libart.so art::JniMethodStart(art::Thread*) 25.87% BusyThread 5703 5729 /system/lib64/libc.so long StrToI<long, ... ...
report.py reports profiling data in stdio interface. If there are a lot of unknown symbols in the report, check here.
# Report perf.data in html interface. $ ./report_html.py # Add source code and disassembly. Change the path of source_dirs if it not correct. $ ./report_html.py --add_source_code --source_dirs path_of_SimpleperfExampleCpp \ --add_disassembly
report_html.py generates report in report.html, and pops up a browser tab to show it.
We can record and report call graphs as below.
# Record dwarf based call graphs: add "-g" in the -r option. $ ./app_profiler.py -p simpleperf.example.cpp \ -r "-e task-clock:u -f 1000 --duration 10 -g" -lib path_of_SimpleperfExampleCpp # Record stack frame based call graphs: add "--call-graph fp" in the -r option. $ ./app_profiler.py -p simpleperf.example.cpp \ -r "-e task-clock:u -f 1000 --duration 10 --call-graph fp" \ -lib path_of_SimpleperfExampleCpp # Report call graphs in stdio interface. $ ./report.py -g # Report call graphs in python Tk interface. $ ./report.py -g --gui # Report call graphs in html interface. $ ./report_html.py # Report call graphs in flamegraphs. # On Windows, use inferno.bat instead of ./inferno.sh. $ ./inferno.sh -sc
We can use report_html.py to show profiling results in a web browser. report_html.py integrates chart statistics, sample table, flamegraphs, source code annotation and disassembly annotation. It is the recommended way to show reports.
$ ./report_html.py
To show flamegraphs, we need to first record call graphs. Flamegraphs are shown by report_html.py in the “Flamegraph” tab. We can also use inferno to show flamegraphs directly.
# On Windows, use inferno.bat instead of ./inferno.sh. $ ./inferno.sh -sc
We can also build flamegraphs using https://github.com/brendangregg/FlameGraph. Please make sure you have perl installed.
$ git clone https://github.com/brendangregg/FlameGraph.git $ ./report_sample.py --symfs binary_cache >out.perf $ FlameGraph/stackcollapse-perf.pl out.perf >out.folded $ FlameGraph/flamegraph.pl out.folded >a.svg
simpleperf report-sample command can convert perf.data into protobuf format accepted by Android Studio cpu profiler. The conversion can be done either on device or on host. If you have more symbol info on host, then prefer do it on host with --symdir option.
$ simpleperf report-sample --protobuf --show-callchain -i perf.data -o perf.trace # Then open perf.trace in Android Studio to show it.
We can record both on CPU time and off CPU time.
First check if trace-offcpu feature is supported on the device.
$ ./run_simpleperf_on_device.py list --show-features dwarf-based-call-graph trace-offcpu
If trace-offcpu is supported, it will be shown in the feature list. Then we can try it.
$ ./app_profiler.py -p simpleperf.example.cpp -a .SleepActivity \ -r "-g -e task-clock:u -f 1000 --duration 10 --trace-offcpu" \ -lib path_of_SimpleperfExampleCpp $ ./report_html.py --add_disassembly --add_source_code \ --source_dirs path_of_SimpleperfExampleCpp
We can profile from launch of an application.
# Start simpleperf recording, then start the Activity to profile. $ ./app_profiler.py -p simpleperf.example.cpp -a .MainActivity # We can also start the Activity on the device manually. # 1. Make sure the application isn't running or one of the recent apps. # 2. Start simpleperf recording. $ ./app_profiler.py -p simpleperf.example.cpp # 3. Start the app manually on the device.
Simpleperf supports controlling recording from application code. Below is the workflow:
Run api_profiler.py prepare to enable simpleperf recording on a device. The script needs to run every time the device reboots.
Link simpleperf app_api code in the application. The app needs to be debuggable or profileableFromShell as described here. Then the app can use the api to start/pause/resume/stop recording. To start recording, the app_api forks a child process running simpleperf, and uses pipe files to send commands to the child process. After recording, a profiling data file is generated.
Run api_profiler.py collect -p <package_name> to collect profiling data files to host.
Examples are CppApi and JavaApi in demo.
We can also write python scripts to parse profiling data manually, by using simpleperf_report_lib.py. Examples are report_sample.py, report_html.py.