2016-07-28 09:39:21 +00:00
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Turbolizer
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==========
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Turbolizer is a HTML-based tool that visualizes optimized code along the various
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phases of Turbofan's optimization pipeline, allowing easy navigation between
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source code, Turbofan IR graphs, scheduled IR nodes and generated assembly code.
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Turbolizer consumes .json files that are generated per-function by d8 by passing
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the '--trace-turbo' command-line flag.
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Host the turbolizer locally by starting a web server that serves the contents of
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the turbolizer directory, e.g.:
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cd src/tools/turbolizer
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python -m SimpleHTTPServer 8000
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Optionally, profiling data generated by the perf tools in linux can be merged
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with the .json files using the turbolizer-perf.py file included. The following
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command is an example of using the perf script:
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2016-07-28 12:37:15 +00:00
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perf script -i perf.data.jitted -s turbolizer-perf.py turbo-main.json
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2016-07-28 09:39:21 +00:00
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The output of the above command is a json object that can be piped to a file
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which, when uploaded to turbolizer, will display the event counts from perf next
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to each instruction in the disassembly. Further detail can be found in the
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bottom of this document under "Using Perf with Turbo."
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Using the python interface in perf script requires python-dev to be installed
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and perf be recompiled with python support enabled. Once recompiled, the
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variable PERF_EXEC_PATH must be set to the location of the recompiled perf
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binaries.
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Graph visualization and manipulation based on Mike Bostock's sample code for an
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interactive tool for creating directed graphs. Original source is at
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https://github.com/metacademy/directed-graph-creator and released under the
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MIT/X license.
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Icons derived from the "White Olive Collection" created by Breezi released under
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the Creative Commons BY license.
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Using Perf with Turbo
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---------------------
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In order to generate perf data that matches exactly with the turbofan trace, you
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must use either a debug build of v8 or a release build with the flag
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'disassembler=on'. This flag ensures that the '--trace-turbo' will output the
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necessary disassembly for linking with the perf profile.
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The basic example of generating the required data is as follows:
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perf record -k mono /path/to/d8 --turbo --trace-turbo --perf-prof main.js
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perf inject -j -i perf.data -o perf.data.jitted
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perf script -i perf.data.jitted -s turbolizer-perf.py turbo-main.json
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These commands combined will run and profile d8, merge the output into a single
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'perf.data.jitted' file, then take the event data from that and link them to the
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disassembly in the 'turbo-main.json'. Note that, as above, the output of the
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script command must be piped to a file for uploading to turbolizer.
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There are many options that can be added to the first command, for example '-e'
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can be used to specify the counting of specific events (default: cycles), as
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well as '--cpu' to specify which CPU to sample.
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