Reducing memory requirements for large size LBM simulations on GPUs

The scientific community in its never-ending road of larger and more efficient computational resources is in need of more efficient implementations that can adapt efficiently on the current parallel platforms. Graphics processing units are an appropriate platform that cover some of these demands. This architecture presents a high performance with a reduced cost and an efficient power consumption. However, the memory capacity in these devices is reduced and so expensive memory transfers are necessary to deal with big problems. Today, the lattice-Boltzmann method (LBM) has positioned as an efficient approach for Computational Fluid Dynamics simulations. Despite this method is particularly amenable to be efficiently parallelized, it is in need of a considerable memory capacity, which is the consequence of a dramatic fall in performance when dealing with large simulations. In this work, we propose some initiatives to minimize such demand of memory, which allows us to execute bigger simulations on the same platform without additional memory transfers, keeping a high performance. In particular, we present 2 new implementations, LBM-Ghost and LBM-Swap, which are deeply analyzed, presenting the pros and cons of each of them.This project was funded by the Spanish Ministry of Economy and Competitiveness (MINECO): BCAM Severo Ochoa accreditation SEV-2013-0323, MTM2013-40824, Computación de Altas Prestaciones VII TIN2015-65316-P, by the Basque Excellence Research Center (BERC 2014-2017) pro- gram by the Basque Government, and by the Departament d' Innovació, Universitats i Empresa de la Generalitat de Catalunya, under project MPEXPAR: Models de Programació i Entorns d' Execució Paral·lels (2014-SGR-1051). We also thank the support of the computing facilities of Extremadura Research Centre for Advanced Technologies (CETA-CIEMAT) and NVIDIA GPU Research Center program for the provided resources, as well as the support of NVIDIA through the BSC/UPC NVIDIA GPU Center of Excellence.Peer ReviewedPostprint (author's final draft

Zynq SoC based acceleration of the lattice Boltzmann method

Cerebral aneurysm is a life‐threatening condition. It is a weakness in a blood vessel that may enlarge and bleed into the surrounding area. In order to understand the surrounding environmental conditions during the interventions or surgical procedures, a simulation of blood flow in cerebral arteries is needed. One of the effective simulation approaches is to use the lattice Boltzmann (LB) method. Due to the computational complexity of the algorithm, the simulation is usually performed on high performance computers. In this paper, efficient hardware architectures of the LB method on a Zynq system‐on‐chip (SoC) are designed and implemented. The proposed architectures have first been simulated in Vivado HLS environment and later implemented on a ZedBoard using the software‐defined SoC (SDSoC) development environment. In addition, a set of evaluations of different hardware architectures of the LB implementation is discussed in this paper. The experimental results show that the proposed implementation is able to accelerate the processing speed by a factor of 52 compared to a dual‐core ARM processor‐based software implementation

Rotational behavior of red blood cells in suspension---a mesoscale simulation study

The nature of blood as a suspension of red blood cells makes computational hemodynamics a demanding task. Our coarse-grained blood model, which builds on a lattice Boltzmann method for soft particle suspensions, enables the study of the collective behavior of the order of 10^6 cells in suspension. After demonstrating the viscosity measurement in Kolmogorov flow, we focus on the statistical analysis of the cell orientation and rotation in Couette flow. We quantify the average inclination with respect to the flow and the nematic order as a function of shear rate and hematocrit. We further record the distribution of rotation periods around the vorticity direction and find a pronounced peak in the vicinity of the theoretical value for free model cells even though cell-cell interactions manifest themselves in a substantial width of the distribution.Comment: 8 pages, 5 figure

Steering in computational science: mesoscale modelling and simulation

This paper outlines the benefits of computational steering for high performance computing applications. Lattice-Boltzmann mesoscale fluid simulations of binary and ternary amphiphilic fluids in two and three dimensions are used to illustrate the substantial improvements which computational steering offers in terms of resource efficiency and time to discover new physics. We discuss details of our current steering implementations and describe their future outlook with the advent of computational grids.Comment: 40 pages, 11 figures. Accepted for publication in Contemporary Physic

Coupled Lattice Boltzmann Modeling Framework for Pore-Scale Fluid Flow and Reactive Transport

In this paper, we propose a modeling framework for pore-scale fluid flow and reactive transport based on a coupled lattice Boltzmann model (LBM). We develop a modeling interface to integrate the LBM modeling code parallel lattice Boltzmann solver and the PHREEQC reaction solver using multiple flow and reaction cell mapping schemes. The major advantage of the proposed workflow is the high modeling flexibility obtained by coupling the geochemical model with the LBM fluid flow model. Consequently, the model is capable of executing one or more complex reactions within desired cells while preserving the high data communication efficiency between the two codes. Meanwhile, the developed mapping mechanism enables the flow, diffusion, and reactions in complex pore-scale geometries. We validate the coupled code in a series of benchmark numerical experiments, including 2D single-phase Poiseuille flow and diffusion, 2D reactive transport with calcite dissolution, as well as surface complexation reactions. The simulation results show good agreement with analytical solutions, experimental data, and multiple other simulation codes. In addition, we design an AI-based optimization workflow and implement it on the surface complexation model to enable increased capacity of the coupled modeling framework. Compared to the manual tuning results proposed in the literature, our workflow demonstrates fast and reliable model optimization results without incorporating pre-existing domain knowledge

OpenLB User Guide: Associated with Release 1.6 of the Code

OpenLB is an object-oriented implementation of LBM. It is the first implementation of a generic platform for LBM programming, which is shared with the open source community (GPLv2). Since the first release in 2007, the code has been continuously improved and extended which is documented by thirteen releases as well as the corresponding release notes which are available on the OpenLB website (https://www.openlb.net). The OpenLB code is written in C++ and is used by application programmers as well as developers, with the ability to implement custom models OpenLB supports complex data structures that allow simulations in complex geometries and parallel execution using MPI, OpenMP and CUDA on high-performance computers. The source code uses the concepts of interfaces and templates, so that efficient, direct and intuitive implementations of the LBM become possible. The efficiency and scalability has been checked and proved by code reviews. This user manual and a source code documentation by DoxyGen are available on the OpenLB project website