{
    "info": {
        "author": "Jonathan R. Madsen",
        "author_email": "jrmadsen@lbl.gov",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Environment :: GPU",
            "Environment :: GPU :: NVIDIA CUDA",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: MacOS",
            "Operating System :: Microsoft :: Windows :: Windows 10",
            "Operating System :: POSIX :: BSD",
            "Operating System :: POSIX :: Linux",
            "Operating System :: Unix",
            "Programming Language :: C++",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8",
            "Programming Language :: Python :: 3.9",
            "Programming Language :: Python :: 3.10",
            "Topic :: Software Development :: Bug Tracking",
            "Topic :: Software Development :: Libraries :: Python Modules",
            "Topic :: Software Development :: Quality Assurance",
            "Topic :: Software Development :: Testing",
            "Topic :: System :: Logging",
            "Topic :: System :: Monitoring",
            "Topic :: Utilities"
        ],
        "description": "# timemory\n\n## Timing + Memory + Hardware Counter Utilities for C / C++ / CUDA / Python\n\n[![Build Status](https://travis-ci.org/NERSC/timemory.svg?branch=master)](https://travis-ci.org/NERSC/timemory)\n[![Build status](https://ci.appveyor.com/api/projects/status/8xk72ootwsefi8c1/branch/master?svg=true)](https://ci.appveyor.com/project/jrmadsen/timemory/branch/master)\n[![codecov](https://codecov.io/gh/NERSC/timemory/branch/master/graph/badge.svg)](https://codecov.io/gh/NERSC/timemory)\n\n[timemory on GitHub (Source code)](https://github.com/NERSC/timemory)\n\n[timemory General Documentation (ReadTheDocs)](https://timemory.readthedocs.io)\n\n[timemory Source Code Documentation (Doxygen)](https://timemory.readthedocs.io/en/latest/doxygen-docs/)\n\n[timemory Testing Dashboard (CDash)](https://cdash.nersc.gov/index.php?project=TiMemory)\n\n[timemory Tutorials](https://github.com/NERSC/timemory-tutorials)\n\n[timemory Wiki](https://github.com/NERSC/timemory/wiki)\n\n|        |                                                   |\n| ------ | ------------------------------------------------- |\n| GitHub | `git clone https://github.com/NERSC/timemory.git` |\n| PyPi   | `pip install timemory`                            |\n| Spack  | `spack install timemory`                          |\n\n## Purpose\n\nThe goal of timemory is to create an open-source performance measurement and analyis package\nwith modular and reusable components which can be used to adapt to any existing C/C++\nperformance measurement and analysis API and is arbitrarily extendable by users within their\napplication.\nTimemory is not just another profiling tool, it is a profling _toolkit_ which streamlines building custom\nprofiling tools through modularity and then utilizes the toolkit to provides several pre-built tools.\n\nIn other words, timemory provides many pre-built tools, libraries, and interfaces but, due to it's modularity,\ncodes can re-use only individual pieces -- such as the classes for measuring different timing intervals, memory usage,\nand hardware counters -- without the timemory \"runtime management\".\n\n## Building and Installing\n\nTimemory uses a standard CMake installation.\nSeveral installation examples can be found in the [Wiki](https://github.com/NERSC/timemory/wiki/Installation-Examples). See the [installation documentation](https://timemory.readthedocs.io/en/develop/installation.html) for detailed information on the CMake options.\n\n## Documentation\n\nThe full documentation is available at [timemory.readthedocs.io](https://timemory.readthedocs.io).\nDetailed source documentation is provided in the [doygen](https://timemory.readthedocs.io/en/latest/doxygen-docs/)\nsection of the full documentation.\nTutorials are available in the [github.com/NERSC/timemory-tutorials](https://github.com/NERSC/timemory-tutorials).\n\n## Overview\n\n__*The primary objective of the timemory is the development of a common framework for binding together software\nmonitoring code (i.e. performance analysis, debugging, logging) into a compact and highly-efficient interface.*__\n\nTimemory arose out of the need for a universal adapator kit for the various APIs provided several existing tools\nand a straight-forward and intuitive method for creating new tools. Timemory makes it possible to bundle\ntogether deterministic performance measurements, statistical performance\nmeasurements (i.e. sampling), debug messages, data logging, and data validation into the same interface for\ncustom application-specific software monitoring interfaces, easily building tools like `time`,\n`netstat`, instrumentation profilers, sampling profilers, and writing implementations for MPI-P, MPI-T, OMPT,\nKokkosP, etc.\n\nTimemory provides a front-end [C/C++/Fortran API](https://timemory.readthedocs.io/en/develop/api/library.html)\nand [Python API](https://timemory.readthedocs.io/en/develop/api/python.html) which allows arbitrary selection\nof 50+ different components from timers to hardware counters to interfaces with third-party tools. This is all\nbuilt generically from the toolkit API with type-safe bundles of tools such as:\n`component_tuple<wall_clock, papi_vector, nvtx_marker, user_bundle>`\nwhere `wall_clock` is a wall-clock timer,\n`papi_vector` is a handle for hardware counters,\n`nvxt_marker` creates notations in the NVIDIA CUDA profilers, and\n`user_bundle` is a generic component which downstream users can insert more components into at runtime.\n\nPerformance measurement components written with timemory are arbitrarily scalable up to any number of threads and\nprocesses and fully support intermixing different measurements at different locations within the program -- this\nuniquely enables timemory to be deployed to collect performance data at scale in HPC because highly detailed collection can\noccur at specific locations within the program where ubiquitous collection would simulatenously degrade performance\nsignificantly and require a prohibitive amount of memory.\n\nTimemory can be used as a backend to bundle instrumentation and sampling tools together, support serialization to JSON/XML,\nand provide statistics among other uses. It can also be utilized as a front-end to invoke\ncustom instrumentation and sampling tools. Timemory uses the abstract term \"component\" for a structure\nwhich encapsulates some performance analysis operation. The structure might encapsulate function\ncalls to another tool, record timestamps for timing, log values provided by the application,\nprovide a operator for replacing a function in the code dynamically, audit the incoming arguments\nand/or outgoing return value from function, or just provide stubs which can be overloaded by the linker.\n\n### Visualization and Analysis\n\nThe native output format of timemory is JSON and text; other output formats such as XML are also supported.\nThe text format is intended to be human readable. The JSON data\nis intended for analysis and comes in two flavors: hierarchical and flat. Basic plotting capabilities are\navailable via `timemory-plotting` but users are highly encouraged to use [hatchet](https://github.com/hatchet/hatchet)\nfor analyzing the heirarchical JSON data in pandas dataframes. [Hatchet](https://github.com/hatchet/hatchet) supports\nfiltering, unions, addition, subtractions, output to `dot` and flamegraph formats, and an interactive Jupyter notebook.\nAt present, timemory supports 45+ metric types for analysis in Hatchet.\n\n### Categories\n\nThere are 4 primary categories in timemory: components, operations, bundlers, and storage. Components provide\nthe specifics of how to perform a particular behavior, operations provide the scaffold for requesting that\na component perform an operation in complex scenarios, bundlers group components into a single generic handle,\nand storage manages data collection over the lifetime of the application. When all four categories are combined,\ntimemory effectively resembles a standard performance analysis tool which passively collects data and provides\nreports and analysis at the termination of the application. Timemory, however, makes it _very easy_ to subtract\nstorage from the equation and, in doing so, transforms timemory into a toolkit for customized data collection.\n\n1. __*Components*__\n   - Individual classes which encapsulate one or more measurement, analysis, logging, or third-party library action(s)\n   - Any data specific to one instance of performing the action is stored within the instance of the class\n   - Any configuration data specific to that type is typically stored within static member functions which return a reference to the configuration data\n   - These classes are designed to support direct usage within other tools, libraries, etc.\n   - Examples include:\n     - `tim::component::wall_clock` : a simple wall-clock timer\n     - `tim::component::vtune_profiler` : a simple component which turns the VTune Profiler on and off (when VTune is actively profiling application)\n     - `tim::component::data_tracker_integer` : associates an integer values with a label as the application executes (e.g. number of loop iterations used somewhere)\n     - `tim::component::papi_vector` : uses the PAPI library to collect hardware-counters values\n     - `tim::component::user_bundle` : encapsulates an array of components which the user can dynamically manipulate during runtime\n2. __*Operations*__\n   - Templated classes whose primary purpose is to provide the implementation for performing some action on a component, e.g. `tim::operation::start<wall_clock>` will attempt to call the `start()` member function on a `wall_clock` component instance\n   - Default implementations generally have one or two public functions: a constructor and/or a function call operator\n     - These generally accept any/all arguments and use SFINAE to determine whether the operation can be performed with or without the given arguments (i.e. does `wall_clock` have a `store(int)` function? `store()`?)\n   - Operations are (generally) not directly utilized by the user and are typically optimized out of the binary\n   - Examples include:\n     - `tim::operation::start` : instruct a component to start collection\n     - `tim::operation::sample` : instruct a component to take individual measurement\n     - `tim::operation::derive` : extra data from other components if it is available\n3. __*Bundlers*__\n   - Provide a generic handle for multiple components\n   - Member functions generally accept any/all arguments and use operations classes to correctly to handle differences between different capabilities of the components it is bundling\n   - Examples include:\n     - `tim::auto_tuple`\n     - `tim::component_tuple`\n     - `tim::component_list`\n     - `tim::lightweight_tuple`\n   - Various flavors provide different implicit behaviors and allocate memory differently\n     - `auto_tuple` starts all components when constructed and stops all components when destructed whereas `component_tuple` requires an explicit start\n     - `component_tuple` allocates all components on the stack and components are \"always on\" whereas `component_list` allocates components on the heap and thus components can be activated/deactivated at runtime\n     - `lightweight_tuple` does not implicitly perform any expensive actions, such as call-stack tracking in \"Storage\"\n4. __*Storage*__\n   - Provides persistent storage for multiple instances of components over the lifetime of a thread in the application\n   - Responsible for maintaining the hierarchy and order of component measurements, i.e. call-stack tracking\n   - Responsible for combining component data from multiple threads and/or processes and outputting the results\n\n> NOTE: `tim::lightweight_tuple` is the recommended bundle for those seeking to use timemory as a toolkit for implementing custom tools and interfaces\n\n## Features\n\n- C++ Template API\n    - Modular and fully-customizable\n    - Adheres to C++ standard template library paradigm of \"you don't pay for what you don't use\"\n    - Simplifies and facilitates creation and implementation of performance measurement tools\n        - Create your own instrumentation profiler\n        - Create your own instrumentation library\n        - Create your own sampling profiler\n        - Create your own sampling library\n        - Create your own execution wrappers\n        - Supplement timemory-provided tools with your own custom component(s)\n        - Thread-safe data aggregation\n        - Aggregate collection over multiple processes (MPI and UPC++ support)\n        - Serialization to text, JSON, XML\n    - Components are composable with other components\n    - Variadic component bundlers which maintain complete type-safety\n        - Components can be bundled together into a single handle without abstractions\n    - Components can store data in any valid C++ data type\n    - Components can return data in any valid C++ data type\n- C / C++ / CUDA / Fortran Library API\n    - Straight-forward collection of functions and macros for creating built-in performance analysis to your code\n    - Component collection can be arbitrarily inter-mixed\n        - E.g. collect \"A\" and \"B\" in one region, \"A\" and \"C\" in another region\n    - Component collection can be dynamically manipulated at runtime\n        - E.g. add/remove \"A\" at any point, on any thread, on any process\n- Python API\n    - Decorators and context-managers for functions or regions in code\n    - Python function profiling\n    - Python line-by-line profiling\n    - Every component in `timemory-avail` is provided as a stand-alone Python class\n        - Provide low-overhead measurements for building your own Python profiling tools\n- Python Analysis via [pandas](https://pandas.pydata.org/)\n- Command-line Tools\n    - [timemory-avail](source/tools/timemory-avail/README.md)\n        - Provides available components, settings, and hardware counters\n        - Quick API reference tool\n    - [timem](source/tools/timem/README.md) (UNIX)\n        - Extended version of UNIX `time` command-line tool that includes additional information on memory usage, context switches, and hardware counters\n        - Support collecting hardware counters (Linux-only, requires PAPI)\n    - [timemory-run](source/tools/timemory-run/README.md) (Linux)\n        - Dynamic instrumentation profiling tool\n        - Supports runtime instrumentation and binary re-writing\n    - [timemory-nvml](source/tools/timemory-nvml/README.md)\n        - Data collection similar to `nvidia-smi`\n    - `timemory-python-profiler`\n        - Python function profiler supporting all timemory components\n        - `from timemory.profiler import Profile`\n    - `timemory-python-trace`\n        - Python line-by-line profiler supporting all timemory components\n        - `from timemory.trace import Trace`\n    - `timemory-python-line-profiler`\n        - Python line-by-line profiler based on [line-profiler](https://pypi.org/project/line-profiler/) package\n        - Extended to use components: cpu-clock, memory-usage, context-switches, etc. (all components which collect scalar values)\n        - `from timemory.line_profiler import LineProfiler`\n- Instrumentation Libraries\n    - [timemory-mpip](source/tools/timemory-mpip/README.md): MPI Profiling Library (Linux-only)\n    - [timemory-ncclp](source/tools/timemory-ncclp/README.md): NCCL Profiling Library (Linux-only)\n    - [timemory-ompt](source/tools/timemory-ompt/README.md): OpenMP Profiling Library\n    - [timemory-compiler-instrument](source/tools/timemory-compiler-instrument/README.md): Compiler instrumentation Library\n    - [kokkos-connector](source/tools/kokkos-connector/README.md): Kokkos Profiling Libraries\n\n## Versioning\n\nTimemory originated as a very simple tool for recording timing and memory measurements (hence the name) in C, C++, and Python and only supported\nthree modes prior to the 3.0.0 release: a fixed set of timers, a pair of memory measurements, and the combination of the two.\n__Prior to the 3.0.0 release, timemory was almost completely rewritten from scratch__ with the sole exceptions of some C/C++ macro, e.g.\n`TIMEMORY_AUTO_TIMER`, and some Python decorators and context-manager, e.g. `timemory.util.auto_timer`, whose behavior were\nable to be fully replicated in the new release. Thus, while it may appear that timemory is a mature project at v3.0+, it\nis essentially still in it's first major release.\n\n## Citing timemory\n\nTo reference timemory in a publication, please cite the following paper:\n\n- Madsen, J.R. et al. (2020) Timemory: Modular Performance Analysis for HPC. In: Sadayappan P., Chamberlain B., Juckeland G., Ltaief H. (eds) High Performance Computing. ISC High Performance 2020. Lecture Notes in Computer Science, vol 12151. Springer, Cham\n\n## Additional Information\n\nFor more information, refer to the [documentation](https://timemory.readthedocs.io/en/latest/).",
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