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CPU/GPU computing for a multi-block structured grid based high-order flow solver on a large heterogeneous system
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DOI：10.1007/s10586-013-0332-1.png)
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Abstract
En 中文
The high-order schemes have attracted more and more attention in computational fluid dynamics (CFD) simulations. As a kind of high-order schemes, weighted compact nonlinear schemes (WCNSs) have been widely applied in large eddy simulations, direct numerical simulations etc. However, due to the computational complexity, WCNSs require high-performance platforms. In recent years, the highly parallel graphics processing unit (GPU) is rapidly gaining maturity as a powerful engine for high performance computer. In this paper, we present a high-order double-precision solver of the three-dimensional, compressible viscous flow using multi-block structured grids on GPU clusters. The solver utilizes the high-order WCNS scheme for space discretization and Jacobi iteration method for time discretization. In order to utilize the computational capability of CPU and GPU for the solver, we present a workload balancing model for distributing workload among CPUs and GPUs. And we design two strategies to overlap computations with communications. The performance analyses show that the single-GPU solver achieves about 8x speed-ups relative to a serial computation on a CPU core. The performance results validate the workload distribution scheme. The strong and weak scaling analyses show that GPU clusters offer a significant advantage in performance.
Keywords:
CPU/GPU computing
Hybrid MPI-OpenMP-CUDA
CFD
WCNS
Journal
C
IF:
4.1
Papers:
5.0K
Citations:
7.5K