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trans_wasm_func_name.py: Correct function index during translation (#3232)

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Adding the N from "aot_func#N" with the import function count is the correct
wasm function index.
This commit is contained in:
liang.he
2024-03-18 10:32:55 +08:00
committed by GitHub
parent ff296c1a62
commit 8c1269d44d
2 changed files with 22 additions and 14 deletions
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23
doc/perf_tune.md
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@ -98,17 +98,22 @@ You should only use this method for well tested wasm applications and make sure
Linux perf is a powerful tool to analyze the performance of a program, developer can use it to find the hot functions and optimize them. It is one profiler supported by WAMR. In order to use it, you need to add `--perf-profile` while running _iwasm_. By default, it is disabled.
> [!CAUTION]
> For now, only llvm-jit mode supports linux-perf.
> For now, only llvm-jit mode and aot mode supports linux-perf.
Here is a basic example, if there is a Wasm application _foo.wasm_, you'll execute.
```
$ perf record --output=perf.data.raw -- iwasm --perf-profile foo.wasm
$ perf record --output=perf.data.raw -- iwasm --enable-linux-perf foo.wasm
```
This will create a _perf.data_ and a _jit-xxx.dump_ under _~/.debug.jit/_ folder. This extra file is WAMR generated at runtime, and it contains the mapping between the JIT code and the original Wasm function names.
This will create a _perf.data_ and
- a _jit-xxx.dump_ under _~/.debug/jit/_ folder if running llvm-jit mode
- or _/tmp/perf-<pid>.map_ if running AOT mode
The next thing need to do is to merge _jit-xxx.dump_ file into the _perf.data_.
This file is WAMR generated. It contains information which includes jitted(precompiled) code addresses in memory, names of jitted (precompiled) functions which are named as *aot_func#N* and so on.
If running with llvm-jit mode, the next thing is to merge _jit-xxx.dump_ file into the _perf.data_.
```
$ perf inject --jit --input=perf.data.raw --output=perf.data
@ -141,28 +146,28 @@ $ perf report --input=perf.data
[Flamegraph](https://www.brendangregg.com/flamegraphs.html) is a powerful tool to visualize stack traces of profiled software so that the most frequent code-paths can be identified quickly and accurately. In order to use it, you need to [capture graphs](https://github.com/brendangregg/FlameGraph#1-capture-stacks) when running `perf record`
```
$ perf record -k mono --call-graph=fp --output=perf.data.raw -- iwasm --perf-profile foo.wasm
$ perf record -k mono --call-graph=fp --output=perf.data.raw -- iwasm --enable-linux-perf foo.wasm
```
merge the _jit-xxx.dump_ file into the _perf.data.raw_.
If running with llvm-jit mode, merge the _jit-xxx.dump_ file into the _perf.data.raw_.
```
$ perf inject --jit --input=perf.data.raw --output=perf.data
```
generate the stack trace file.
Generate the stack trace file.
```
$ perf script > out.perf
```
[fold stacks](https://github.com/brendangregg/FlameGraph#2-fold-stacks).
[Fold stacks](https://github.com/brendangregg/FlameGraph#2-fold-stacks).
```
$ ./FlameGraph/stackcollapse-perf.pl out.perf > out.folded
```
[render a flamegraph](https://github.com/brendangregg/FlameGraph#3-flamegraphpl)
[Render a flamegraph](https://github.com/brendangregg/FlameGraph#3-flamegraphpl)
```
$ ./FlameGraph/flamegraph.pl out.folded > perf.foo.wasm.svg