During the past two decades, the lattice Boltzmann method (LBM) has been increasingly acknowledged as a valuable alternative to classical numerical techniques (e.g. finite elements, finite volumes, etc.) in fluid dynamics. A distinguishing feature of LBM is undoubtedly its highly parallelizable data structure. In this work we present a general parallel LBM framework for graphic processing units (GPUs) characterized by a high degree of modularity and memory efficiency, still preserving very good computational performance. After recalling the essential programming principles of the CUDA C language for GPUs, the details of the implementation will be provided. The data structure here presented takes into account the intrinsic properties of the Gauss-Hermite quadrature rules (on which the LBM is based) to guarantee a unique and flexible framework for two- and three-dimensional problems. In addition, a careful implementation of a memory efficient formulation of the LBM algorithm has allowed to limit the high memory consumption that typically affects this computational method. Numerical examples in two and three dimensions illustrate the reliability and the performance of the code.
A modular lattice boltzmann solver for GPU computing processors
QUARTERONI, ALFIO MARIA
2012-01-01
Abstract
During the past two decades, the lattice Boltzmann method (LBM) has been increasingly acknowledged as a valuable alternative to classical numerical techniques (e.g. finite elements, finite volumes, etc.) in fluid dynamics. A distinguishing feature of LBM is undoubtedly its highly parallelizable data structure. In this work we present a general parallel LBM framework for graphic processing units (GPUs) characterized by a high degree of modularity and memory efficiency, still preserving very good computational performance. After recalling the essential programming principles of the CUDA C language for GPUs, the details of the implementation will be provided. The data structure here presented takes into account the intrinsic properties of the Gauss-Hermite quadrature rules (on which the LBM is based) to guarantee a unique and flexible framework for two- and three-dimensional problems. In addition, a careful implementation of a memory efficient formulation of the LBM algorithm has allowed to limit the high memory consumption that typically affects this computational method. Numerical examples in two and three dimensions illustrate the reliability and the performance of the code.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
