Method for accelerating lattice-Boltzmann by utilizing graphic processing units (GPUs)

Abstract

The invention provides a method for accelerating a lattice-Boltzmann by utilizing graphic processing units (GPUs), and relates to a GPU at a host port and a GPU at an equipment port. The method comprises the steps that the host port gives parameters such as computational domains, the reference length, the freestream velocity, the density, reynolds numbers and the like according to physical problems, and divides thread numbers of a design kernel according to a grid; and the equipment host calculates equilibrium state distribution functions of all lattice points in each direction through the macroscopic parameters (the density, the speed, the reynolds numbers, the viscosity coefficient, and the like), uses the equilibrium state distribution functions as initial fields of the calculation, parallelly solves a discrete equation and processes boundaries, and returns a result finally obtained through iteration to the host port. According to the method, the migration and the collision in the lattice-Boltzmann method are calculated by utilizing the rapid calculation characteristic of the GPU at the equipment port, and the iteration process of the lattice-Boltzmann method is accelerated through coordination operation of the GPU at the host port and the GPU at the equipment port.

Description

technical field [0001] The invention relates to the field of computer high-performance computing and computational fluid dynamics, in particular to a method for accelerating Lattice-Boltzmann using NVIDIA GPUs. Background technique [0002] Lattice Boltzmann Method (Lattice Boltzmann Method, LBM) has been developed into an effective numerical simulation method in the past 20 years, which is an intermediate between microscopic molecular dynamics methods and macroscopic methods based on the continuum assumption view method. This method is different from the traditional fluid simulation method. It is based on the molecular kinetic theory, and obtains the macroscopically averaged properties by tracking the transport of the particle distribution function and then calculating the moment of the distribution function. The dynamic theory of the lattice Boltzmann method makes it more effective in simulating many complex flows, such as porous media flow, suspension flow, multiphase fl...

AI Technical Summary

Problems solved by technology

This approach consumes a lot of time, power and maintenance costs due to the low peak floating-point computing power of the CPU and the huge network transmission overhead.

Moreover, as people's requirements for fluid simulation cycles are getting shorter and higher, and the accuracy requirements are getting higher and higher, the scale of PC server clusters is getting bigger and bigger. Manageability, ease of programming, scalability, management and maintenance costs, etc. are facing huge challenges

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