Jonathan R. Madsen
127e30a4d7
* 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
[ROCm/rocprofiler-systems commit: 945f541965]
32 wiersze
1.6 KiB
Markdown
32 wiersze
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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