期刊
PARALLEL COMPUTATIONAL TECHNOLOGIES, PCT 2019
卷 1063, 期 -, 页码 139-151出版社
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-030-28163-2_10
关键词
LRnLA; LBM; Temporal blocking; Time skewing; GPU; Vectorization
类别
资金
- Russian Science Foundation [18-71-10004]
- Russian Science Foundation [18-71-10004] Funding Source: Russian Science Foundation
We present an implementation of the Lattice Boltzmann Method (LBM) with Locally Recursive non-Locally Asynchronous (LRnLA) algorithms on GPU and CPU. The algorithm is based on the recursive subdivision of the domain of the dD1T space-time simulation and loosens the memory-bound limit for numerical schemes with local dependencies. We show that LRnLA algorithm allows to overcome the main memory bandwidth limitations in both CPU and GPU implementations. For CPU, we find the data layout that provides alignment for the full use of AVX2/AVX512 vectorization. For GPU, we devise a procedure for pairwise CUDA-block synchronization applied to the implementation of the LRnLA algorithm, which previously worked only on CPU. The performance on GPU is higher, as it is usual in modern implementations. However, the performance gap in our implementation is smaller, thanks to a more efficient CPU version. Through a detailed comparison, we show possible future applications for both the CPU and the GPU implementations of the lattice Boltzmann method in the complex setting.
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