Abstract
This article presents the application of performance optimization techniques to improve the computational efficiency of a parallel multithreading algorithm for self-gravity calculation on agglomerates. The studied algorithm applies the Discrete Element Method to simulate an ensemble of interacting particles under several contact and body forces. Based on the time scales of the process involved in the problem, we used a computation algorithm that speed up the self-gravity calculation based on defining a mesh over the simulated space. Specific performance improvements are presented, including the update of the occupied regions of the space, profiling and reimplementation of the most time consuming routines. Results indicate that the proposed implementation scale appropriately (almost-linear behavior) with the number of computational resources and the number of particles. The proposed improvements allow accelerating up to 50\(\times \) the execution times over the previous version of the self-gravity algorithm in the studied scenarios.
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Acknowledgments
The work of Néstor Rocchetti, Sergio Nesmachnow, and Gonzalo Tancredi has been partly supported by CSIC, ANII, and PEDECIBA (Uruguay).
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Rocchetti, N., Frascarelli, D., Nesmachnow, S., Tancredi, G. (2018). Performance Improvements of a Parallel Multithreading Self-gravity Algorithm. In: Mocskos, E., Nesmachnow, S. (eds) High Performance Computing. CARLA 2017. Communications in Computer and Information Science, vol 796. Springer, Cham. https://doi.org/10.1007/978-3-319-73353-1_21
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DOI: https://doi.org/10.1007/978-3-319-73353-1_21
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