The distribution of computational load over different processing elements represents an important issue in parallel computing [1]. This is particularly relevant in thecase of parallel execution of structured grid computational models, such as CellularAutomata (CA) [2], where the domain space is partitioned in region assigned to theparallel computing nodes. Load balancing techniques are particularly effective whenstructured grid computational models are exploited to simulate topologically connectedphysical phenomena like lava or debris flows (e.g., [3]), in which the evolution develops in a usually small sub-region of the domain. In this work, we present a dynamicload balancing technique that can provide performance improvements in structuredgrid model development on distributed memory architectures by adopting the MPItechnology. First tests have demonstrated the usefulness of the feature in appreciablyreducing execution times in comparison with not-balanced parallel versions.

A Dynamic Load Balancing technique for Parallel Execution of Structured Grid Models

Giordano Andrea;
2019

Abstract

The distribution of computational load over different processing elements represents an important issue in parallel computing [1]. This is particularly relevant in thecase of parallel execution of structured grid computational models, such as CellularAutomata (CA) [2], where the domain space is partitioned in region assigned to theparallel computing nodes. Load balancing techniques are particularly effective whenstructured grid computational models are exploited to simulate topologically connectedphysical phenomena like lava or debris flows (e.g., [3]), in which the evolution develops in a usually small sub-region of the domain. In this work, we present a dynamicload balancing technique that can provide performance improvements in structuredgrid model development on distributed memory architectures by adopting the MPItechnology. First tests have demonstrated the usefulness of the feature in appreciablyreducing execution times in comparison with not-balanced parallel versions.
2019
Istituto di Calcolo e Reti ad Alte Prestazioni - ICAR
Parallel Computing
Parallel Software Tools
Load Balancing
Cellular Automata
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/363609
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