Simpleperf is a native profiling tool for Android. It can be used to profile both Android applications and native processes running on Android. It can profile both Java and C++ code on Android. It can be used on Android L and above.
Simpleperf is part of the Android Open Source Project. The source code is here. The latest document is here. Bugs and feature requests can be submitted at http://github.com/android-ndk/ndk/issues.
Simpleperf works similar to linux-tools-perf, but it has some specific features for Android profiling:
Aware of Android environment
a. It can profile embedded shared libraries in apk.
b. It reads symbols and debug information from .gnu_debugdata section.
c. It gives suggestions when errors occur.
d. When recording with -g option, unwind the stack before writting to file to save storage space.
e. It supports adding additional information (like symbols) in perf.data, to support recording on device and reporting on host.
Using python scripts for profiling tasks
Easy to release
a. Simpleperf executables on device are built as static binaries. They can be pushed on any Android device and run.
b. Simpleperf executables on host are built as static binaries, and support different hosts: mac, linux and windows.
Simpleperf is periodically released with Android ndk, located at simpleperf/. The latest release can be found here. Simpleperf tools contain executables, shared libraries and python scripts.
Simpleperf executables running on Android device Simpleperf executables running on Android device are located at bin/android/. Each architecture has one executable, like bin/android/arm64/simpleperf. It can record and report profiling data. It provides a command-line interface broadly the same as the linux-tools perf, and also supports some additional features for Android-specific profiling.
Simpleperf executables running on hosts Simpleperf executables running on hosts are located at bin/darwin, bin/linux and bin/windows. Each host and architecture has one executable, like bin/linux/x86_64/simpleperf. It provides a command-line interface for reporting profiling data on hosts.
Simpleperf report shared libraries used on host Simpleperf report shared libraries used on host are located at bin/darwin, bin/linux and bin/windows. Each host and architecture has one library, like bin/linux/x86_64/libsimpleperf_report.so. It is a library for parsing profiling data.
Python scripts Python scripts are written to help different profiling tasks.
annotate.py is used to annotate source files based on profiling data.
app_profiler.py is used to profile Android applications.
binary_cache_builder.py is used to pull libraries from Android devices.
pprof_proto_generator.py is used to convert profiling data to format used by pprof.
report.py is used to provide a GUI interface to report profiling result.
report_sample.py is used to generate flamegraph.
simpleperf_report_lib.py provides a python interface for parsing profiling data.
Modern CPUs have a hardware component called the performance monitoring unit (PMU). The PMU has several hardware counters, counting events like how many cpu cycles have happened, how many instructions have executed, or how many cache misses have happened.
The Linux kernel wraps these hardware counters into hardware perf events. In addition, the Linux kernel also provides hardware independent software events and tracepoint events. The Linux kernel exposes all this to userspace via the perf_event_open system call, which simpleperf uses.
Simpleperf has three main functions: stat, record and report.
The stat command gives a summary of how many events have happened in the profiled processes in a time period. Here’s how it works:
The record command records samples of the profiled process in a time period. Here’s how it works:
The report command reads a “perf.data” file and any shared libraries used by the profiled processes, and outputs a report showing where the time was spent.
Simpleperf supports several subcommands, including list, stat, record and report. Each subcommand supports different options. This section only covers the most important subcommands and options. To see all subcommands and options, use --help.
# List all subcommands. $ simpleperf --help # Print help message for record subcommand. $ simpleperf record --help
simpleperf list is used to list all events available on the device. Different devices may support different events because of differences in hardware and kernel.
$ simpleperf list List of hw-cache events: branch-loads ... List of hardware events: cpu-cycles instructions ... List of software events: cpu-clock task-clock ...
simpleperf stat is used to get a raw event counter information of the profiled program or system-wide. By passing options, we can select which events to use, which processes/threads to monitor, how long to monitor and the print interval. Below is an example.
# Stat using default events (cpu-cycles,instructions,...), and monitor
# process 7394 for 10 seconds.
$ simpleperf stat -p 7394 --duration 10
Performance counter statistics:
1,320,496,145 cpu-cycles # 0.131736 GHz (100%)
510,426,028 instructions # 2.587047 cycles per instruction (100%)
4,692,338 branch-misses # 468.118 K/sec (100%)
886.008130(ms) task-clock # 0.088390 cpus used (100%)
753 context-switches # 75.121 /sec (100%)
870 page-faults # 86.793 /sec (100%)
Total test time: 10.023829 seconds.
Select events We can select which events to use via -e option. Below are examples:
# Stat event cpu-cycles. $ simpleperf stat -e cpu-cycles -p 11904 --duration 10 # Stat event cache-references and cache-misses. $ simpleperf stat -e cache-references,cache-misses -p 11904 --duration 10
When running the stat command, if the number of hardware events is larger than the number of hardware counters available in the PMU, the kernel shares hardware counters between events, so each event is only monitored for part of the total time. In the example below, there is a percentage at the end of each row, showing the percentage of the total time that each event was actually monitored.
# Stat using event cache-references, cache-references:u,.... $ simpleperf stat -p 7394 -e cache-references,cache-references:u,cache-references:k,cache-misses,cache-misses:u,cache-misses:k,instructions --duration 1 Performance counter statistics: 4,331,018 cache-references # 4.861 M/sec (87%) 3,064,089 cache-references:u # 3.439 M/sec (87%) 1,364,959 cache-references:k # 1.532 M/sec (87%) 91,721 cache-misses # 102.918 K/sec (87%) 45,735 cache-misses:u # 51.327 K/sec (87%) 38,447 cache-misses:k # 43.131 K/sec (87%) 9,688,515 instructions # 10.561 M/sec (89%) Total test time: 1.026802 seconds.
In the example above, each event is monitored about 87% of the total time. But there is no guarantee that any pair of events are always monitored at the same time. If we want to have some events monitored at the same time, we can use --group option. Below is an example.
# Stat using event cache-references, cache-references:u,.... $ simpleperf stat -p 7394 --group cache-references,cache-misses --group cache-references:u,cache-misses:u --group cache-references:k,cache-misses:k -e instructions --duration 1 Performance counter statistics: 3,638,900 cache-references # 4.786 M/sec (74%) 65,171 cache-misses # 1.790953% miss rate (74%) 2,390,433 cache-references:u # 3.153 M/sec (74%) 32,280 cache-misses:u # 1.350383% miss rate (74%) 879,035 cache-references:k # 1.251 M/sec (68%) 30,303 cache-misses:k # 3.447303% miss rate (68%) 8,921,161 instructions # 10.070 M/sec (86%) Total test time: 1.029843 seconds.
Select target to monitor We can select which processes or threads to monitor via -p option or -t option. Monitoring a process is the same as monitoring all threads in the process. Simpleperf can also fork a child process to run the new command and then monitor the child process. Below are examples.
# Stat process 11904 and 11905. $ simpleperf stat -p 11904,11905 --duration 10 # Stat thread 11904 and 11905. $ simpleperf stat -t 11904,11905 --duration 10 # Start a child process running `ls`, and stat it. $ simpleperf stat ls
Decide how long to monitor When monitoring existing threads, we can use --duration option to decide how long to monitor. When monitoring a child process running a new command, simpleperf monitors until the child process ends. In this case, we can use Ctrl-C to stop monitoring at any time. Below are examples.
# Stat process 11904 for 10 seconds. $ simpleperf stat -p 11904 --duration 10 # Stat until the child process running `ls` finishes. $ simpleperf stat ls # Stop monitoring using Ctrl-C. $ simpleperf stat -p 11904 --duration 10 ^C
Decide the print interval When monitoring perf counters, we can also use --interval option to decide the print interval. Below are examples.
# Print stat for process 11904 every 300ms. $ simpleperf stat -p 11904 --duration 10 --interval 300 # Print system wide stat at interval of 300ms for 10 seconds (rooted device only). # system wide profiling needs root privilege $ su 0 simpleperf stat -a --duration 10 --interval 300
Display counters in systrace simpleperf can also work with systrace to dump counters in the collected trace. Below is an example to do a system wide stat
# capture instructions (kernel only) and cache misses with interval of 300 milliseconds for 15 seconds $ su 0 simpleperf stat -e instructions:k,cache-misses -a --interval 300 --duration 15 # on host launch systrace to collect trace for 10 seconds (HOST)$ external/chromium-trace/systrace.py --time=10 -o new.html sched gfx view # open the collected new.html in browser and perf counters will be shown up
simpleperf record is used to dump records of the profiled program. By passing options, we can select which events to use, which processes/threads to monitor, what frequency to dump records, how long to monitor, and where to store records.
# Record on process 7394 for 10 seconds, using default event (cpu-cycles), # using default sample frequency (4000 samples per second), writing records # to perf.data. $ simpleperf record -p 7394 --duration 10 simpleperf I 07-11 21:44:11 17522 17522 cmd_record.cpp:316] Samples recorded: 21430. Samples lost: 0.
Select events In most cases, the cpu-cycles event is used to evaluate consumed cpu time. As a hardware event, it is both accurate and efficient. We can also use other events via -e option. Below is an example.
# Record using event instructions. $ simpleperf record -e instructions -p 11904 --duration 10
Select target to monitor The way to select target in record command is similar to that in stat command. Below are examples.
# Record process 11904 and 11905. $ simpleperf record -p 11904,11905 --duration 10 # Record thread 11904 and 11905. $ simpleperf record -t 11904,11905 --duration 10 # Record a child process running `ls`. $ simpleperf record ls
Set the frequency to record We can set the frequency to dump records via the -f or -c options. For example, -f 4000 means dumping approximately 4000 records every second when the monitored thread runs. If a monitored thread runs 0.2s in one second (it can be preempted or blocked in other times), simpleperf dumps about 4000 * 0.2 / 1.0 = 800 records every second. Another way is using -c option. For example, -c 10000 means dumping one record whenever 10000 events happen. Below are examples.
# Record with sample frequency 1000: sample 1000 times every second running. $ simpleperf record -f 1000 -p 11904,11905 --duration 10 # Record with sample period 100000: sample 1 time every 100000 events. $ simpleperf record -c 100000 -t 11904,11905 --duration 10
Decide how long to monitor The way to decide how long to monitor in record command is similar to that in stat command. Below are examples.
# Record process 11904 for 10 seconds. $ simpleperf record -p 11904 --duration 10 # Record until the child process running `ls` finishes. $ simpleperf record ls # Stop monitoring using Ctrl-C. $ simpleperf record -p 11904 --duration 10 ^C
Set the path to store records By default, simpleperf stores records in perf.data in current directory. We can use -o option to set the path to store records. Below is an example.
# Write records to data/perf2.data. $ simpleperf record -p 11904 -o data/perf2.data --duration 10
simpleperf report is used to report based on perf.data generated by simpleperf record command. Report command groups records into different sample entries, sorts sample entries based on how many events each sample entry contains, and prints out each sample entry. By passing options, we can select where to find perf.data and executable binaries used by the monitored program, filter out uninteresting records, and decide how to group records.
Below is an example. Records are grouped into 4 sample entries, each entry is a row. There are several columns, each column shows piece of information belonging to a sample entry. The first column is Overhead, which shows the percentage of events inside current sample entry in total events. As the perf event is cpu-cycles, the overhead can be seen as the percentage of cpu time used in each function.
# Reports perf.data, using only records sampled in libsudo-game-jni.so, # grouping records using thread name(comm), process id(pid), thread id(tid), # function name(symbol), and showing sample count for each row. $ simpleperf report --dsos /data/app/com.example.sudogame-2/lib/arm64/libsudo-game-jni.so --sort comm,pid,tid,symbol -n Cmdline: /data/data/com.example.sudogame/simpleperf record -p 7394 --duration 10 Arch: arm64 Event: cpu-cycles (type 0, config 0) Samples: 28235 Event count: 546356211 Overhead Sample Command Pid Tid Symbol 59.25% 16680 sudogame 7394 7394 checkValid(Board const&, int, int) 20.42% 5620 sudogame 7394 7394 canFindSolution_r(Board&, int, int) 13.82% 4088 sudogame 7394 7394 randomBlock_r(Board&, int, int, int, int, int) 6.24% 1756 sudogame 7394 7394 @plt
Set the path to read records By default, simpleperf reads perf.data in current directory. We can use -i option to select another file to read records.
$ simpleperf report -i data/perf2.data
Set the path to find executable binaries If reporting function symbols, simpleperf needs to read executable binaries used by the monitored processes to get symbol table and debug information. By default, the paths are the executable binaries used by monitored processes while recording. However, these binaries may not exist when reporting or not contain symbol table and debug information. So we can use --symfs to redirect the paths. Below is an example.
$ simpleperf report # In this case, when simpleperf wants to read executable binary /A/b, # it reads file in /A/b. $ simpleperf report --symfs /debug_dir # In this case, when simpleperf wants to read executable binary /A/b, # it prefers file in /debug_dir/A/b to file in /A/b.
Filter records When reporting, it happens that not all records are of interest. Simpleperf supports five filters to select records of interest. Below are examples.
# Report records in threads having name sudogame. $ simpleperf report --comms sudogame # Report records in process 7394 or 7395 $ simpleperf report --pids 7394,7395 # Report records in thread 7394 or 7395. $ simpleperf report --tids 7394,7395 # Report records in libsudo-game-jni.so. $ simpleperf report --dsos /data/app/com.example.sudogame-2/lib/arm64/libsudo-game-jni.so # Report records in function checkValid or canFindSolution_r. $ simpleperf report --symbols "checkValid(Board const&, int, int);canFindSolution_r(Board&, int, int)"
Decide how to group records into sample entries Simpleperf uses --sort option to decide how to group sample entries. Below are examples.
# Group records based on their process id: records having the same process # id are in the same sample entry. $ simpleperf report --sort pid # Group records based on their thread id and thread comm: records having # the same thread id and thread name are in the same sample entry. $ simpleperf report --sort tid,comm # Group records based on their binary and function: records in the same # binary and function are in the same sample entry. $ simpleperf report --sort dso,symbol # Default option: --sort comm,pid,tid,dso,symbol. Group records in the same # thread, and belong to the same function in the same binary. $ simpleperf report
This section shows how to profile an Android application. Here are examples. And we use SimpleperfExamplePureJava project to show the profiling results.
Simpleperf only supports profiling native instructions in binaries in ELF format. If the Java code is executed by interpreter, or with jit cache, it can’t be profiled by simpleperf. As Android supports Ahead-of-time compilation, it can compile Java bytecode into native instructions with debug information. On devices with Android version <= M, we need root privilege to compile Java bytecode with debug information. However, on devices with Android version >= N, we don't need root privilege to do so.
Profiling an Android application involves three steps:
To profile, we can use either command lines or python scripts. Below shows both.
Before profiling, we need to install the application to be profiled on an Android device. To get valid profiling results, please check following points:
1. The application should be debuggable. It means android:debuggable should be true. So we need to use debug build type instead of release build type. It is understandable because we can‘t profile others’ apps. However, on a rooted Android device, the application doesn't need to be debuggable.
2. Run on an Android >= N device. We suggest profiling on an Android >= N device. The reason is here.
3. On Android O, add wrap.sh in the apk. To profile Java code, we need ART running in oat mode. But on Android O, debuggable applications are forced to run in jit mode. To work around this, we need to add a wrap.sh in the apk. So if you are running on Android O device, Check here for how to add wrap.sh in the apk.
4. Make sure C++ code is compiled with optimizing flags. If the application contains C++ code, it can be compiled with -O0 flag in debug build type. This makes C++ code slow. Check here for how to avoid that.
5. Use native libraries with debug info in the apk when possible. If the application contains C++ code or pre-compiled native libraries, try to use unstripped libraries in the apk. This helps simpleperf generating better profiling results. Check here for how to use unstripped libraries.
Here we use SimpleperfExamplePureJava as an example. It builds an app-profiling.apk for profiling.
$ git clone https://android.googlesource.com/platform/system/extras $ cd extras/simpleperf/demo # Open SimpleperfExamplesPureJava project with Android studio, # and build this project sucessfully, otherwise the `./gradlew` command below will fail. $ cd SimpleperfExamplePureJava # On windows, use "gradlew" instead. $ ./gradlew clean assemble $ adb install -r app/build/outputs/apk/app-profiling.apk
We recommend using python scripts for profiling because they are more convenient. But using command-line will give us a better understanding of the profile process step by step. So we first show how to use command lines.
1. Enable profiling
$ adb shell setprop security.perf_harden 0
2. Fully compile the app
We need to compile Java bytecode into native instructions to profile Java code in the application. This needs different commands on different Android versions.
On Android >= N:
$ adb shell setprop debug.generate-debug-info true $ adb shell cmd package compile -f -m speed com.example.simpleperf.simpleperfexamplepurejava # Restart the app to take effect $ adb shell am force-stop com.example.simpleperf.simpleperfexamplepurejava
On Android M devices, We need root privilege to force Android to fully compile Java code into native instructions in ELF binaries with debug information. We also need root privilege to read compiled native binaries (because installd writes them to a directory whose uid/gid is system:install). So profiling Java code can only be done on rooted devices.
$ adb root $ adb shell setprop dalvik.vm.dex2oat-flags -g # Reinstall the app. $ adb install -r app/build/outputs/apk/app-profiling.apk
On Android L devices, we also need root privilege to compile the app with debug info and access the native binaries.
$ adb root $ adb shell setprop dalvik.vm.dex2oat-flags --include-debug-symbols # Reinstall the app. $ adb install -r app/build/outputs/apk/app-profiling.apk
3. Find the app process
# Start the app if needed $ adb shell am start -n com.example.simpleperf.simpleperfexamplepurejava/.MainActivity $ adb shell pidof com.example.simpleperf.simpleperfexamplepurejava 6885
So the id of the app process is 6885. We will use this number in the command lines below, please replace this number with what you get by running pidof command. On Android <= M, pidof may not exist or work well, and you can try ps | grep com.example.simpleperf.simpleperfexamplepurejava instead.
4. Download simpleperf to the app's data directory
# Find which architecture the app is using. $ adb shell run-as com.example.simpleperf.simpleperfexamplepurejava cat /proc/6885/maps | grep boot.oat 708e6000-70e33000 r--p 00000000 103:09 1214 /system/framework/arm64/boot.oat # The app uses /arm64/boot.oat, so push simpleperf in bin/android/arm64/ to device. $ cd ../../scripts/ $ adb push bin/android/arm64/simpleperf /data/local/tmp $ adb shell chmod a+x /data/local/tmp/simpleperf $ adb shell run-as com.example.simpleperf.simpleperfexamplepurejava cp /data/local/tmp/simpleperf .
5. Record perf.data
$ adb shell run-as com.example.simpleperf.simpleperfexamplepurejava ./simpleperf record -p 6885 --duration 10 simpleperf I 04-27 20:41:11 6940 6940 cmd_record.cpp:357] Samples recorded: 40008. Samples lost: 0. $ adb shell run-as com.example.simpleperf.simpleperfexamplepurejava ls -lh perf.data simpleperf I 04-27 20:31:40 5999 5999 cmd_record.cpp:357] Samples recorded: 39949. Samples lost: 0.
The profiling data is recorded at perf.data.
Normally we need to use the app when profiling, otherwise we may record no samples. But in this case, the MainActivity starts a busy thread. So we don't need to use the app while profiling.
There are many options to record profiling data, check record command for details.
6. Report perf.data
# Pull perf.data on host. $ adb shell "run-as com.example.simpleperf.simpleperfexamplepurejava cat perf.data | tee /data/local/tmp/perf.data >/dev/null" $ adb pull /data/local/tmp/perf.data # Report samples using report.py, report.py is a python wrapper of simpleperf report command. $ python report.py ... Overhead Command Pid Tid Shared Object Symbol 83.54% Thread-2 6885 6900 /data/app/com.example.simpleperf.simpleperfexamplepurejava-2/oat/arm64/base.odex void com.example.simpleperf.simpleperfexamplepurejava.MainActivity$1.run() 16.11% Thread-2 6885 6900 /data/app/com.example.simpleperf.simpleperfexamplepurejava-2/oat/arm64/base.odex int com.example.simpleperf.simpleperfexamplepurejava.MainActivity$1.callFunction(int)
See here for why we use tee rather than just >. There are many ways to show reports, check report command for details.
Besides command lines, We can use app-profiler.py to profile Android applications. It downloads simpleperf on device, records perf.data, and collects profiling results and native binaries on host.
1. Record perf.data by running app-profiler.py
$ python app_profiler.py --app com.example.simpleperf.simpleperfexamplepurejava \
--apk ../SimpleperfExamplePureJava/app/build/outputs/apk/app-profiling.apk \
-r "-e cpu-cycles:u --duration 10"
If running successfully, it will collect profiling data in perf.data in current directory, and related native binaries in binary_cache/.
2. Report perf.data
We can use report.py to report perf.data.
$ python report.py
We can add any option accepted by simpleperf report command to report.py.
A call graph is a tree showing function call relations. Below is an example.
main() {
FunctionOne();
FunctionTwo();
}
FunctionOne() {
FunctionTwo();
FunctionThree();
}
callgraph:
main-> FunctionOne
| |
| |-> FunctionTwo
| |-> FunctionThree
|
|-> FunctionTwo
When using command lines, add -g option like below:
$ adb shell run-as com.example.simpleperf.simpleperfexamplepurejava ./simpleperf record -g -p 6685 --duration 10
When using app_profiler.py, add “-g” in record option as below:
$ python app_profiler.py --app com.example.simpleperf.simpleperfexamplepurejava \
--apk ../SimpleperfExamplePureJava/app/build/outputs/apk/app-profiling.apk \
-r "-e cpu-cycles:u --duration 10 -g"
Recording dwarf based call graph needs support of debug information in native binaries. So if using native libraries in the application, it is better to contain non-stripped native libraries in the apk.
When using command lines, add --call-graph fp option like below:
$ adb shell run-as com.example.simpleperf.simpleperfexamplepurejava ./simpleperf record --call-graph fp -p 6685 --duration 10
When using app_profiler.py, add “--call-graph fp” in record option as below:
$ python app_profiler.py --app com.example.simpleperf.simpleperfexamplepurejava \
--apk ../SimpleperfExamplePureJava/app/build/outputs/apk/app-profiling.apk \
-r "-e cpu-cycles:u --duration 10 --call-graph fp"
Recording stack frame based call graphs needs support of stack frame register. Notice that on arm architecture, the stack frame register is not well supported, even if compiled using -O0 -g -fno-omit-frame-pointer options. It is because the kernel can't unwind user stack containing both arm/thumb code. So please consider using dwarf based call graph on arm architecture, or profiling in arm64 environment.
To report call graph using command lines, add -g option.
$ bin/linux/x86_64/simpleperf report -g
...
Children Self Command Pid Tid Shared Object Symbol
99.97% 0.00% Thread-2 10859 10876 /system/framework/arm64/boot.oat java.lang.Thread.run
|
-- java.lang.Thread.run
|
-- void com.example.simpleperf.simpleperfexamplepurejava.MainActivity$1.run()
|--83.66%-- [hit in function]
|
|--16.22%-- int com.example.simpleperf.simpleperfexamplepurejava.MainActivity$1.callFunction(int)
| |--99.97%-- [hit in function]
To report call graph using report.py, add -g option.
# In text mode $ python report.py -g # In GUI mode $ python report.py -g --gui # Double-click an item started with '+' to show its callgraph.
simpleperf_report_lib.py provides an interface reading samples from perf.data. By using it, You can write python scripts to read perf.data or convert perf.data to other formats. Below are two examples.
$ python report_sample.py >out.perf $ stackcollapse-perf.pl out.perf >out.folded $ ./flamegraph.pl out.folded >a.svg
pprof is a tool for visualization and analysis of profiling data. It can be got from https://github.com/google/pprof. pprof_proto_generator.py can generate profiling data in a format acceptable by pprof.
$ python pprof_proto_generator.py $ pprof -pdf pprof.profile
annotate.py reads perf.data, binaries in binary-cache (collected by app-profiler.py) and source code, and generates annoated source code in annotated_files/.
1. Run annotate.py
$ python annotate.py -s ../SimpleperfExamplePureJava
addr2line is need to annotate source code. It can be found in Android ndk release, in paths like toolchains/aarch64-linux-android-4.9/prebuilt/linux-x86_64/bin/aarch64-linux-android-addr2line. Please use --addr2line option to set the path of addr2line if annotate.py can't find it.
2. Read annotated code
The annotated source code is located at annotated_files/. annotated_files/summary shows how each source file is annotated.
One annotated source file is annotated_files/java/com/example/simpleperf/simpleperfexamplepurejava/MainActivity.java. It's content is similar to below:
// [file] shows how much time is spent in current file.
/* [file] acc_p: 99.966552%, p: 99.837438% */package com.example.simpleperf.simpleperfexamplepurejava;
...
// [func] shows how much time is spent in current function.
/* [func] acc_p: 16.213395%, p: 16.209250% */ private int callFunction(int a) {
...
// This shows how much time is spent in current line.
// acc_p field means how much time is spent in current line and functions called by current line.
// p field means how much time is spent just in current line.
/* acc_p: 99.966552%, p: 83.628188% */ i = callFunction(i);
As perf.data is generated in app‘s context, it can’t be pulled directly to host. One way is to adb shell run-as xxx cat perf.data >perf.data. However, it doesn‘t work well on Windows, because the content can be modified when it goes through the pipe. So we first copy it from app’s context to shell's context, then pull it on host. The commands are as below:
$adb shell "run-as xxx cat perf.data | tee /data/local/tmp/perf.data >/dev/null" $adb pull /data/local/tmp/perf.data
1. Running on a device reflects a real running situation, so we suggest profiling on real devices instead of emulators. 2. To profile Java code, we need ART running in oat mode, which is only available >= L for rooted devices, and >= N for non-rooted devices. 3. Old Android versions are likely to be shipped with old kernels (< 3.18), which may not support profiling features like dwarf based call graph. 4. Old Android versions are likely to be shipped with Arm32 chips. In Arm32 mode, stack frame based call graph doesn't work well.
Inferno is a flamegraph generator for native (C/C++) Android apps. It was originally written to profile and improve surfaceflinger performance (Android compositor) but it can be used for any native Android application . You can see a sample report generated with Inferno here. Report are self-contained in HTML so they can be exchanged easily.
Notice there is no concept of time in a flame graph since all callstack are merged together. As a result, the width of a flamegraph represents 100% of the number of samples and the height is related to the number of functions on the stack when sampling occurred.
In the flamegraph featured above you can see the main thread of SurfaceFlinger. It is immediatly apparent that most of the CPU time is spent processing messages android::SurfaceFlinger::onMessageReceived. The most expensive task is to ask the screen to be refreshed as android::DisplayDevice::prepare shows in orange . This graphic division helps to see what part of the program is costly and where a developer's effort to improve performances should go.
A flamegraph give you instant vision on the CPU cycles cost centers but it can also be used to find specific offenders. To find them, look for plateaus. It is easier to see an example:
In the previous flamegraph, two plateaus (due to android::BufferQueueCore::validateConsistencyLocked) are immediately apparent.
Inferno relies on simpleperf to record the callstack of a native application thousands of times per second. Simpleperf takes care of unwinding the stack either using frame pointer (recommended) or dwarf. At the end of the recording simpleperf also symbolize all IPs automatically. The record are aggregated and dumps dumped to a file perf.data. This file is pulled from the Android device and processed on the host by Inferno. The callstacks are merged together to visualize in which part of an app the CPU cycles are spent.
Open a terminal and from simpleperf directory type:
./inferno.sh (on Linux/Mac) ./inferno.bat (on Windows)
Inferno will collect data, process them and automatically open your web browser to display the HTML report.
You can select how long to sample for, the color of the node and many other things. Use -h to get a list of all supported parameters.
./inferno.sh -h
A healthy flame graph features a single call site at its base (see inferno/report.html). If you don't see a unique call site like _start or _start_thread at the base from which all flames originate, something went wrong. : Stack unwinding may fail to reach the root callsite. These incomplete callstack are impossible to merge properly. By default Inferno asks simpleperf to unwind the stack via the kernel and frame pointers. Try to perform unwinding with dwarf -du, you can further tune this setting.
If you see no flames at all or a mess of 1 level flame without a common base, this may be because you compiled without frame pointers. Make sure there is no -fomit-frame-pointer in your build config. Alternatively, ask simpleperf to collect data with dward unwinding -du.
If simpleperf reports a lot of lost sample it is probably because you are unwinding with dwarf. Dwarf unwinding involves copying the stack before it is processed. Try to use frame pointer unwinding which can be done by the kernel and it much faster.
The cost of frame pointer is negligible on arm64 parameter but considerable on arm 32-bit arch (due to register pressure). Use a 64-bit build for better profiling.
If you cannot run as root, make sure the app is debuggable otherwise simpleperf will not be able to profile it.