Jonathan R. Madsen
945f541965
* Added documentation markdown source * Replaced AARInternal with AMDResearch in URLs * Renamed cpack artifact names * Fix to testing and lulesh submodule checkout * Docker updates * CMake and CPack - force CMAKE_INSTALL_LIBDIR to lib - CPACK_DEBIAN_PACKAGE_RELEASE uses OMNITRACE_CPACK_SYSTEM_NAME - CPACK_RPM_PACKAGE_RELEASE uses OMNITRACE_CPACK_SYSTEM_NAME - Tweak LIBOMP_LIBRARY find in examples/openmp - Tweak setup-env.sh.in * Partial update of README - status badges - docs link - removed install info (covered by docs) * OMNITRACE_SAMPLING_CPUS setting - enables control over which CPUs are sampled for frequency * omnitrace exe updates - exclude transaction clone, virtual thunk, non-virtual thunk - module_function::start_address - module_function::instructions - verbosity > 0 encodes instructions into JSON * Miscellaneous fixes - relocate setup-env.sh.in - add modulefile.in - Updated README.md and source/docs/about.md - cmake fix for libomp - fix license in miscellaneous places - dl.hpp and dl.cpp * Update timemory and dyninst submodules - timemory signals updates - dyninst Movement-adhoc updates * cmake format
32 lines
1.6 KiB
Markdown
32 lines
1.6 KiB
Markdown
# About
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```eval_rst
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.. toctree::
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:glob:
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:maxdepth: 4
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```
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[Browse Omnitrace source code on Github](https://github.com/AMDResearch/omnitrace)
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> [Omnitrace](https://github.com/AMDResearch/omnitrace) is an AMD research project and should
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> not be treated as an offical part of the ROCm software stack.
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[Omnitrace](https://github.com/AMDResearch/omnitrace) is designed for both high-level and
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comprehensive application tracing and profiling on both the CPU and GPU.
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[Omnitrace](https://github.com/AMDResearch/omnitrace) supports both binary instrumentation
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and sampling as a means of collecting various metrics.
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Visualization of the comprehensive omnitrace results can be viewed in any modern web browser by visiting [ui.perfetto.dev](https://ui.perfetto.dev/)
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and loading the perfetto output (`.proto` files) produced by omnitrace.
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Aggregated high-level results are available in text files for human consumption and JSON files for programmatic analysis.
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The JSON output files are compatible with the python package [hatchet](https://github.com/hatchet/hatchet) which converts
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the performance data into pandas dataframes and facilitate multi-run comparisons, filtering, visualization in Jupyter notebooks, and much more.
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[Omnitrace](https://github.com/AMDResearch/omnitrace) has two distinct configuration steps:
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1. Configuring which functions and modules are instrumented in the target binaries (i.e. executable and/or libraries)
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- [Instrumenting with Omnitrace](instrumenting.md)
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2. Configuring what the instrumentation does happens when the instrumented binaries are executed
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- [Customizing Omnitrace Runtime](runtime.md)
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