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Thread Safety
Description This verification check examines whether a model called in parallel by multiple threads gives the same results as when called sequentially. A number `num_configs` (preset in the code) of configurations is generated each containing a different number of atoms based on a randomly distorted, periodic, face-centered cubic (fcc) structure containing a random distribution of all atoms supported by the model. Configurations used for testing are provided as auxiliary files. The energy and forces for each configuration are computed in sequence. Then the calculations are repeated in using Python multithreading with `num_configs` threads, and with the Global Interpreter Lock (GIL) released to ensure true parallelism. Each thread possesses its own copy of memory related to the calculation. The threaded calculations are repeated `num_cycles` times (preset in the code) with the configurations randomly distributed to the threads to create many varying opportunities for collisions. To pass the test, the total energy and the forces on all atoms for a given configuration obtained in all cycles must be be identical to those obtained in the sequential calculation. Failure of this verification check implies that the model is inappropriately storing information in persistent (static) memory during or between calls. Passing this verification check provides some assurance the the model is thread safe, but it is NOT a guarantee due to the inherent randomness of race conditions in unsafe code. To be certain, the model code would need to be studied and analyzed carefully.
A statement of applicability provided by the contributor, informing users of the intended use of this KIM Item.
Contributor Ellad B. Tadmor
Maintainer Ellad B. Tadmor
Published on KIM 2018
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Funding Not available
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Citable Link https://openkim.org/cite/VC_881176209980_000
KIM Item TypeVerification Check
Properties as defined in kimspec.edn.
Simulator Name
The name of the simulator as defined in kimspec.edn.
Programming Language(s)
The programming languages used in the code and the percentage of the code written in each one.
100.00% Python

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