xref: /system/extras/simpleperf/doc/README.md

# Simpleperf

Simpleperf is a native CPU 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. The simpleperf executable can run on Android >=L,
and Python scripts can be used on Android >= N.

Simpleperf is part of the Android Open Source Project.
The source code is [here](https://android.googlesource.com/platform/system/extras/+/master/simpleperf/).
The latest document is [here](https://android.googlesource.com/platform/system/extras/+/master/simpleperf/doc/README.md).

[TOC]

## Introduction

An introduction slide deck is [here](./introduction.pdf).

Simpleperf contains two parts: the simpleperf executable and Python scripts.

The simpleperf executable works similar to linux-tools-perf, but has some specific features for
the Android profiling environment:

1. It collects more info in profiling data. Since the common workflow is "record on the device, and
   report on the host", simpleperf not only collects samples in profiling data, but also collects
   needed symbols, device info and recording time.

2. It delivers new features for recording.
   1) When recording dwarf based call graph, simpleperf unwinds the stack before writing a sample
      to file. This is to save storage space on the device.
   2) Support tracing both on CPU time and off CPU time with --trace-offcpu option.
   3) Support recording callgraphs of JITed and interpreted Java code on Android >= P.

3. It relates closely to the Android platform.
   1) Is aware of Android environment, like using system properties to enable profiling, using
      run-as to profile in application's context.
   2) Supports reading symbols and debug information from the .gnu_debugdata section, because
      system libraries are built with .gnu_debugdata section starting from Android O.
   3) Supports profiling shared libraries embedded in apk files.
   4) It uses the standard Android stack unwinder, so its results are consistent with all other
      Android tools.

4. It builds executables and shared libraries for different usages.
   1) Builds static executables on the device. Since static executables don't rely on any library,
      simpleperf executables can be pushed on any Android device and used to record profiling data.
   2) Builds executables on different hosts: Linux, Mac and Windows. These executables can be used
      to report on hosts.
   3) Builds report shared libraries on different hosts. The report library is used by different
      Python scripts to parse profiling data.

Detailed documentation for the simpleperf executable is [here](#executable-commands-reference).

Python scripts are split into three parts according to their functions:

1. Scripts used for recording, like app_profiler.py, run_simpleperf_without_usb_connection.py.

2. Scripts used for reporting, like report.py, report_html.py, inferno.

3. Scripts used for parsing profiling data, like simpleperf_report_lib.py.

Detailed documentation for the Python scripts is [here](#scripts-reference).


## Tools in simpleperf

The simpleperf executables and Python scripts are located in simpleperf/ in ndk releases, and in
system/extras/simpleperf/scripts/ in AOSP. Their functions are listed below.

bin/: contains executables and shared libraries.

bin/android/${arch}/simpleperf: static simpleperf executables used on the device.

bin/${host}/${arch}/simpleperf: simpleperf executables used on the host, only supports reporting.

bin/${host}/${arch}/libsimpleperf_report.${so/dylib/dll}: report shared libraries used on the host.

*.py, inferno, purgatorio: Python scripts used for recording and reporting. Details are in [scripts_reference.md](scripts_reference.md).


## Android application profiling

See [android_application_profiling.md](./android_application_profiling.md).


## Android platform profiling

See [android_platform_profiling.md](./android_platform_profiling.md).


## Executable commands reference

See [executable_commands_reference.md](./executable_commands_reference.md).


## Scripts reference

See [scripts_reference.md](./scripts_reference.md).

## View the profile

See [view_the_profile.md](./view_the_profile.md).

## Answers to common issues

### Why we suggest profiling on Android >= N devices?

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 recording dwarf based call graphs.
4. Old Android versions are likely to be shipped with Arm32 chips. In Arm32
   mode, recording stack frame based call graphs doesn't work well.

### Suggestions about recording call graphs

Below is our experiences of dwarf based call graphs and stack frame based call graphs.

dwarf based call graphs:
1. Need support of debug information in binaries.
2. Behave normally well on both ARM and ARM64, for both fully compiled Java code and C++ code.
3. Can only unwind 64K stack for each sample. So usually can't show complete flamegraph. But
   probably is enough for users to identify hot places.
4. Take more CPU time than stack frame based call graphs. So the sample frequency is suggested
   to be 1000 Hz. Thus at most 1000 samples per second.

stack frame based call graphs:
1. Need support of stack frame registers.
2. Don't work well on ARM. Because ARM is short of registers, and ARM and THUMB code have different
   stack frame registers. So the kernel can't unwind user stack containing both ARM/THUMB code.
3. Also don't work well on fully compiled Java code on ARM64. Because the ART compiler doesn't
   reserve stack frame registers.
4. Work well when profiling native programs on ARM64. One example is profiling surfacelinger. And
   usually shows complete flamegraph when it works well.
5. Take less CPU time than dwarf based call graphs. So the sample frequency can be 4000 Hz or
   higher.

So if you need to profile code on ARM or profile fully compiled Java code, dwarf based call graphs
may be better. If you need to profile C++ code on ARM64, stack frame based call graphs may be
better. After all, you can always try dwarf based call graph first, because it always produces
reasonable results when given unstripped binaries properly. If it doesn't work well enough, then
try stack frame based call graphs instead.

Simpleperf may need unstripped native binaries on the device to generate good dwarf based call
graphs. It can be supported by downloading unstripped native libraries on device, as [here](#fix-broken-callchain-stopped-at-c-functions).

### Why we can't always get complete DWARF-based call graphs?

DWARF-based call graphs are generated by unwinding thread stacks. When a sample is generated, up to
64KB stack data is dumped by the kernel. By unwinding the stack based on dwarf information, we get
a callchain. But the thread stack can be much longer than 64KB. In that case, we can't unwind to
the thread start point.

To alleviate the problem, simpleperf joins callchains after recording them. If two callchains of
a thread have an entry containing the same ip and sp address, then simpleperf tries to join them to
make the callchains longer. In that case, the longer we run, the more samples we get. This makes it
more likely to get complete callchains, but it's still not guaranteed to get complete call graphs.

### How to solve missing symbols in report?

The simpleperf record command collects symbols on device in perf.data. But if the native libraries
you use on device are stripped, this will result in a lot of unknown symbols in the report. A
solution is to build binary_cache on host.

```sh
# Collect binaries needed by perf.data in binary_cache/.
$ ./binary_cache_builder.py -lib NATIVE_LIB_DIR,...
```

The NATIVE_LIB_DIRs passed in -lib option are the directories containing unstripped native
libraries on host. After running it, the native libraries containing symbol tables are collected
in binary_cache/ for use when reporting.

```sh
$ ./report.py --symfs binary_cache

# report_html.py searches binary_cache/ automatically, so you don't need to
# pass it any argument.
$ ./report_html.py
```

### Fix broken callchain stopped at C functions

When using dwarf based call graphs, simpleperf generates callchains during recording to save space.
The debug information needed to unwind C functions is in .debug_frame section, which is usually
stripped in native libraries in apks. To fix this, we can download unstripped version of native
libraries on device, and ask simpleperf to use them when recording.

To use simpleperf directly:

```sh
# create native_libs dir on device, and push unstripped libs in it (nested dirs are not supported).
$ adb shell mkdir /data/local/tmp/native_libs
$ adb push <unstripped_dir>/*.so /data/local/tmp/native_libs
# run simpleperf record with --symfs option.
$ adb shell simpleperf record xxx --symfs /data/local/tmp/native_libs
```

To use app_profiler.py:

```sh
$ ./app_profiler.py -lib <unstripped_dir>
```

### Show annotated source code and disassembly

To show hot places at source code and instruction level, we need to show source code and
disassembly with event count annotation. Simpleperf supports showing annotated source code and
disassembly for C++ code and fully compiled Java code. Simpleperf supports two ways to do it:

1. Through report_html.py:
   1) Generate perf.data and pull it on host.
   2) Generate binary_cache, containing elf files with debug information. Use -lib option to add
     libs with debug info. Do it with
     `binary_cache_builder.py -i perf.data -lib <dir_of_lib_with_debug_info>`.
   3) Use report_html.py to generate report.html with annotated source code and disassembly,
     as described [here](https://android.googlesource.com/platform/system/extras/+/master/simpleperf/doc/scripts_reference.md#report_html_py).

2. Through pprof.
   1) Generate perf.data and binary_cache as above.
   2) Use pprof_proto_generator.py to generate pprof proto file. `pprof_proto_generator.py`.
   3) Use pprof to report a function with annotated source code, as described [here](https://android.googlesource.com/platform/system/extras/+/master/simpleperf/doc/scripts_reference.md#pprof_proto_generator_py).

## Bugs and contribution

Bugs and feature requests can be submitted at https://github.com/android/ndk/issues.
Patches can be uploaded to android-review.googlesource.com as [here](https://source.android.com/setup/contribute/),
or sent to email addresses listed [here](https://android.googlesource.com/platform/system/extras/+/master/simpleperf/OWNERS).

If you want to compile simpleperf C++ source code, follow below steps:
1. Download AOSP main branch as [here](https://source.android.com/setup/build/requirements).
2. Build simpleperf.
```sh
$ . build/envsetup.sh
$ lunch aosp_arm64-userdebug
$ mmma system/extras/simpleperf -j30
```

If built successfully, out/target/product/generic_arm64/system/bin/simpleperf is for ARM64, and
out/target/product/generic_arm64/system/bin/simpleperf32 is for ARM.