mirror of
https://github.com/bytecodealliance/wasm-micro-runtime.git
synced 2024-11-26 15:32:05 +00:00
4ef724bbff
- Add new API wasm_runtime_load_ex() in wasm_export.h and wasm_module_new_ex in wasm_c_api.h - Put aot_create_perf_map() into a separated file aot_perf_map.c - In perf.map, function names include user specified module name - Enhance the script to help flamegraph generations
91 lines
3.5 KiB
Markdown
91 lines
3.5 KiB
Markdown
# linux perf sample introduction
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This is a sample to show how to use the Linux perf tool to profile the execution of a WebAssembly application. And how to use the [Flamegraph](https://www.brendangregg.com/flamegraphs.html) tool to visualize the profiling result.
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## Build and run the sample
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There are two Wasm modules and their instance will be created and run in the sample. [The first module](./wasm/fib.c) is a simple Wasm module that calculates the Fibonacci number. [The second module](./wasm/ackermann.c) is a simple Wasm module that execute the Ackermann function. The target is enable to profile the execution of both two modules separately.
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```bash
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$ cmake -S . -B build
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$ cmake --build build
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```
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### Profile the execution
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```bash
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$ cd build
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$ perf record -k mono -g --output=perf.data -- ./linux_perf_sample
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```
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Enable to use `perf report --stdio` to do a quick analysis of the profiling result.
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### Visualize the profiling result
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Need to download Flamegraph tool from [Flamegraph](https://github.com/brendangregg/FlameGraph/releases/tag/v1.0) firstly.
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```bash
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$ perf script > out.perf
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$ ./FlameGraph/stackcollapse-perf.pl out.perf > out.folded
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$ ./FlameGraph/flamegraph.pl out.folded > perf.svg
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```
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In this result, you'll see two modules's profiling result and all wasm functions are named as "aot_func#N" which is a little hard to distinguish.
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### Separate profiling result
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[process_folded_data.py](../../test-tools/flame-graph-helper/process_folded_data.py) is a script can a) translate "aot_func#N" into its original function name in name sections, b) separate the profiling result of different modules.
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In this sample, we want to separate `fib` and `ackermann` profiling data from _out.folded_. In [demo](host/demo.c), we decide to name the module of `fib1.wasm` as `fib2` and the module of `ackermann1.wasm` as `ackermann2`.
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```bash
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$ python process_folded_data.py --wabt_home /opt/wabt --wasm_names fib2=./fib1.wasm,ackermann2=./ackermann1.wasm out.folded
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-> write into out.fib2.translated
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-> write into out.ackermann2.translated
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-> write into out.translated
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```
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More scenarios:
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if only using one wasm during profiling, the script can be used like this:
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```bash
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$ python process_folded_data.py --wabt_home /opt/wabt --wasm <wasm_file> --folded <folded_file>
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```
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if only using one wasm during profiling and specify the module name via APIs, the script can be used like this:
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```bash
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$ python process_folded_data.py --wabt_home /opt/wabt --wasm_names <module name>=<wasm_file> --folded <folded_file>
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```
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if only using one wasm during profiling and specify the module name, which is same with the basename of wasm file, via APIs, the script can be used like this:
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```bash
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$ python process_folded_data.py --wabt_home /opt/wabt --wasm <wasm_file> --folded <folded_file>
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```
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if using multiple wasm during profiling and specify module names, which are same with basename of wasm files, via APIs, the script can be used like this:
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```bash
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$ python process_folded_data.py --wabt_home /opt/wabt --wasm <wasm_file> --wasm <wasm_file> --wasm <wasm_file> --folded <folded_file>
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```
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if using multiple wasm during profiling and specify module names via APIs, the script can be used like this:
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```bash
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$ python process_folded_data.py --wabt_home /opt/wabt --wasm_names <module_name>=<wasm_file>,<module_name>=<wasm_file>,<module_name>=<wasm_file> --folded <folded_file>
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```
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Now we have two flame-graphs for two wasm modules:
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## Reference
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- [perf_tune](../../doc/perf_tune.md)
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