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Total Recall: flmake and the Quest for Reproducibility
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scipy_2012
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Anthony Scopatz
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jul Thu 19
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10:20
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`FLASH`_ is a high-performance computing (HPC) multi-physics code which is used to perform astrophysical and high-energy density physics simulations. It runs on the full range of systems from laptops to workstations to 100,000 processor super computers - such as the Blue Gene/P at Argonne National Laboratory. Historically, FLASH was born from a collection of unconnected legacy codes written primarily in Fortran and merged into a single project. Over the past 13 years major sections have been rewritten in other languages. For instance, I/O is now implemented in C. However building, testing, and documentation are all performed in Python. FLASH has a unique architecture which compiles *simulation specific* executables for each new type of run. This is aided by an object-oriented-esque inheritance model that is implemented by inspecting the file system's directory hierarchy. This allows FLASH to compile to faster machine code than a compile-once strategy. However it also places a greater importance on the Python build system. To run a FLASH simulation, the user must go through three basic steps: setup, build, and execution. Canonically, each of these tasks are independently handled by the user. However, with the recent advent of `flmake`_ - a Python workflow management utility for FLASH - such tasks may now be performed in a repeatable way. Previous workflow management tools have been written for FLASH. (For example, the "Milad system" was implemented entirely in Makefiles.) However, none of the priorattempts have placed reproducibility as their primary concern. This is in part becausefully capturing the setup metadata requires alterations to the build system. The development of flmake started by rewriting the existing build systemto allow FLASH to be run outside of the main line subversion repository. It separates outproject and simulation directories independent of the FLASH source directory. Thesedirectories are typically under their own version control. Moreover for each of the important tasks (setup, build, run, etc), a sidecar metadata *description* file is either written or appended to. This is a simple dictionary-of-dictionaries JSON file which stores the environment of the system and the state of the code when each flmake command is run. This metadata includes the version information of both the FLASH main line and project repositories. However, it also may include *all* local modifications since the last commit. A patch is automatically generated using the Python standard library ``difflib`` module and stored directly in the description. Along with universally unique identifiers, logging, and Python run control files, the flmake utility may use the description files to fully reproduce a simulation by re-executing each command in its original environment and state. While ``flmake reproduce`` makes a useful debugging tool, it fundamentally increases the scientific merit of FLASH simulations. The methods described above may be used whenever source code itself is distributed. While this is true for FLASH (uncommon amongst compiledcodes), most Python packages also distribute their source. Therefore the same reproducibility strategy is applicable and highly recommended for Python simulation codes. Thus flmake shows that reproducibility - which is notably absent from most computational science projects - is easily attainable using only version control and standard library modules. .. _FLASH: http://flash.uchicago.edu/site/ .. _flmake: http://flash.uchicago.edu/site/flashcode/user_support/tools4b/usersguide/flmake/index.htm
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