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https://github.com/thlorenz/turbolizer

Turbolizer tool from the v8 repository with added support to preload a profile
https://github.com/thlorenz/turbolizer

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Turbolizer tool from the v8 repository with added support to preload a profile

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README 

        
# Turbolizer



Turbolizer tool derived from the one included with `v8/tools`.



![turbolizer](./assets/turbolizer.gif)



## Installation



```

npm install -g turbolizer

```



## Usage



Run your app with the `--trace-turbo` flag, i.e. `node --trace-turbo app.js` to produce `turbo-*.json` files.



Then just run `turbolizer` in the same directory and select which file (or all) you want to

load and the turbolizer application will open in the browser with it preloaded.



## Alternatives



If you don't want to install anything, as an alternative can then either load them one by one

via the hosted browser version of this repo at [thlorenz.github.io/turbolizer](https://thlorenz.github.io/turbolizer).



* * *



_Original Readme from the [v8 repository](https://github.com/v8/v8)_



Turbolizer

==========



Turbolizer is a HTML-based tool that visualizes optimized code along the various

phases of Turbofan's optimization pipeline, allowing easy navigation between

source code, Turbofan IR graphs, scheduled IR nodes and generated assembly code.



Turbolizer consumes .json files that are generated per-function by d8 by passing

the '--trace-turbo' command-line flag.



Host the turbolizer locally by starting a web server that serves the contents of

the turbolizer directory, e.g.:



    cd src/tools/turbolizer

    python -m SimpleHTTPServer 8000



Optionally, profiling data generated by the perf tools in linux can be merged

with the .json files using the turbolizer-perf.py file included. The following

command is an example of using the perf script:



    perf script -i perf.data.jitted -s turbolizer-perf.py turbo-main.json



The output of the above command is a json object that can be piped to a file

which, when uploaded to turbolizer, will display the event counts from perf next

to each instruction in the disassembly. Further detail can be found in the

bottom of this document under "Using Perf with Turbo."



Using the python interface in perf script requires python-dev to be installed

and perf be recompiled with python support enabled. Once recompiled, the

variable PERF_EXEC_PATH must be set to the location of the recompiled perf

binaries.



Graph visualization and manipulation based on Mike Bostock's sample code for an

interactive tool for creating directed graphs. Original source is at

https://github.com/metacademy/directed-graph-creator and released under the

MIT/X license.



Icons derived from the "White Olive Collection" created by Breezi released under

the Creative Commons BY license.



Using Perf with Turbo

---------------------



In order to generate perf data that matches exactly with the turbofan trace, you

must use either a debug build of v8 or a release build with the flag

'disassembler=on'. This flag ensures that the '--trace-turbo' will output the

necessary disassembly for linking with the perf profile.



The basic example of generating the required data is as follows:



    perf record -k mono /path/to/d8 --trace-turbo --perf-prof main.js

    perf inject -j -i perf.data -o perf.data.jitted

    perf script -i perf.data.jitted -s turbolizer-perf.py turbo-main.json



These commands combined will run and profile d8, merge the output into a single

'perf.data.jitted' file, then take the event data from that and link them to the

disassembly in the 'turbo-main.json'. Note that, as above, the output of the

script command must be piped to a file for uploading to turbolizer.



There are many options that can be added to the first command, for example '-e'

can be used to specify the counting of specific events (default: cycles), as

well as '--cpu' to specify which CPU to sample.