Lattice Boltzmann Method Implementation on Multiple Devices using OpenCL

Scientific computing community has been in close connection with high performance computing (HPC), which has been privilege of a limited group of scientists. Recently, with rapid development of Graphics Processing Units (GPUs), the parallel processing power of high performance computers has been brought up to every commodity desktop computer, reducing cost of scientific computations. In this paper, we develop a general purpose Lattice Boltzmann code that runs on commodity computer with multiple heterogeneous devices that support OpenCL specification. Different approaches to Lattice Boltzmann code implementations on commodity computer with multiple devices were explored. Simulation results for different code implementations on multiple devices have been compared to each other, to results obtained for single device implementation and with results from the literature. Simulation results for the commodity computer hardware platforms with multiple devices implementation have showed significant speed improvement compared to simulation implemented on single device

Implementation of the Lattice Boltzmann Method on Heterogeneous Hardware and Platforms using OpenCL

The Lattice Boltzmann method (LBM) has become an alternative method for computational fluid dynamics with a wide range of applications. Besides its numerical stability and accuracy, one of the major advantages of LBM is its relatively easy parallelization and, hence, it is especially well fitted to many-core hardware as graphics processing units (GPU). The majority of work concerning LBM implementation on GPU's has used the CUDA programming model, supported exclusively by NVIDIA. Recently, the open standard for parallel programming of heterogeneous systems (OpenCL) has been introduced. OpenCL standard matures and is supported on processors from most vendors. In this paper, we make use of the OpenCL framework for the lattice Boltzmann method simulation, using hardware accelerators - AMD ATI Radeon GPU, AMD Dual-Core CPU and NVIDIA GeForce GPU's. Application has been developed using a combination of Java and OpenCL programming languages. Java bindings for OpenCL have been utilized. This approach offers the benefits of hardware and operating system independence, as well as speeding up of lattice Boltzmann algorithm. It has been showed that the developed lattice Boltzmann source code can be executed without modification on all of the used hardware accelerators. Performance results have been presented and compared for the hardware accelerators that have been utilized