一个很好用的Android Profiling Native层性能分析工具
由于公司项目对功耗有比较严格标准，所以需要降低功耗，Simpleperf可以很好的分析Native层,并可以输出火焰图，当然我们的高标准要求（手动狗头），不仅仅局限Native层，GPU也要优化，下一遍主要介绍GPU profiling
Android application profiling
This section shows how to profile an Android application. Some examples are Here.
Profiling an Android application involves three steps: 1. Prepare an Android application. 2. Record profiling data. 3. Report profiling data.
Table of Contents
- Prepare an Android application
- Record and report profiling data
- Record and report call graph
- Report in html interface
- Show flamegraph
- Record both on CPU time and off CPU time
- Profile from launch
- Parse profiling data manually
Prepare an Android application
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.
- If you want to profile a debug build of an application:
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.
- If you want to profile a release build of an application:
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 two circumstances: If you are on a rooted device, you can profile any app.
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 { "wrapshlib_dir" } } } } } } }
task createWrapShLibDir for (String abi : ["armeabi", "armeabi-v7a", "arm64-v8a", "x86", "x8664"]) { def dir = new File("app/wrapshlibdir/lib/" + abi) dir.mkdirs() def wrapFile = new File(dir, "wrap.sh") wrapFile.withWriter { writer -> writer.write('#!/system/bin/sh\n$@\n') } } } ```
- If you want to profile C/C++ code:
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.
- If you want to profile Java code:
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 appprofiler.py the --compilejava_code option.
On Android N, simpleperf supports profiling Java code that is compiled into native instructions. To compile java code, we can pass appprofiler.py the --compilejava_code option.
On Android <= M, simpleperf doesn't support profiling Java code.
Below I use application SimpleperfExampleWithNative. It builds an app-profiling.apk for profiling.
$ git clone https://android.googlesource.com/platform/system/extras
$ cd extras/simpleperf/demo
# Open SimpleperfExamplesWithNative project with Android studio, and build this project
# successfully, otherwise the `./gradlew` command below will fail.
$ cd SimpleperfExampleWithNative
# On windows, use "gradlew" instead.
$ ./gradlew clean assemble
$ adb install -r app/build/outputs/apk/profiling/app-profiling.apk
Record and report profiling data
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.
$ python app_profiler.py -p com.example.simpleperf.simpleperfexamplewithnative --compile_java_code \
-a .MixActivity -lib path_of_SimpleperfExampleWithNative
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.
$ python report.py
Cmdline: /data/data/com.example.simpleperf.simpleperfexamplewithnative/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.
$ python report_html.py
# Add source code and disassembly. Change the path of source_dirs if it not correct.
$ python report_html.py --add_source_code --source_dirs path_of_SimpleperfExampleWithNative \
--add_disassembly
report_html.py generates report in report.html, and pops up a browser tab to show it.
Record and report call graph
We can record and report call graphs as below.
# Record dwarf based call graphs: add "-g" in the -r option.
$ python app_profiler.py -p com.example.simpleperf.simpleperfexamplewithnative \
-r "-e task-clock:u -f 1000 --duration 10 -g" -lib path_of_SimpleperfExampleWithNative
# Record stack frame based call graphs: add "--call-graph fp" in the -r option.
$ python app_profiler.py -p com.example.simpleperf.simpleperfexamplewithnative \
-r "-e task-clock:u -f 1000 --duration 10 --call-graph fp" \
-lib path_of_SimpleperfExampleWithNative
# Report call graphs in stdio interface.
$ python report.py -g
# Report call graphs in python Tk interface.
$ python report.py -g --gui
# Report call graphs in html interface.
$ python report_html.py
# Report call graphs in flamegraphs.
# On Windows, use inferno.bat instead of ./inferno.sh.
$ ./inferno.sh -sc
Report in html interface
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.
$ python report_html.py
Show flamegraph
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
$ python report_sample.py --symfs binary_cache >out.perf
$ FlameGraph/stackcollapse-perf.pl out.perf >out.folded
$ FlameGraph/flamegraph.pl out.folded >a.svg
Record both on CPU time and off CPU time
We can record both on CPU time and off CPU time.
First check if trace-offcpu feature is supported on the device.
Bash
$ python 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.
Bash
$ python app_profiler.py -p com.example.simpleperf.simpleperfexamplewithnative -a .SleepActivity \
-r "-g -e task-clock:u -f 1000 --duration 10 --trace-offcpu" \
-lib path_of_SimpleperfExampleWithNative
$ python report_html.py --add_disassembly --add_source_code \
--source_dirs path_of_SimpleperfExampleWithNative
Profile from launch
We can profile from launch of an application.
Bash
# Start simpleperf recording, then start the Activity to profile.
$ python app_profiler.py -p com.example.simpleperf.simpleperfexamplewithnative -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.
$ python app_profiler.py -p com.example.simpleperf.simpleperfexamplewithnative
# 3\. Start the app manually on the device.
Parse profiling data manually
We can also write python scripts to parse profiling data manually, by using simpleperfreportlib.py. Examples are reportsample.py, reporthtml.py.
