CPU/GPU computing for a multi-block structured grid based high-order flow solver on a large heterogeneous system

Cao, Wei; Xu, Chuanfu; Wang, Zhenghua; Lu, Yao; Hua-yong, Liu

doi:10.1007/s10586-013-0332-1

Cited by 16 publications

(14 citation statements)

References 16 publications

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“…Considering the fact that memory on a single node becomes inadequate when the simulation size grows, we use MPI to increase the simulation size on multi-nodes 18 . Therefore, a tri-level hybrid programming pattern MPI-OpenMP-CUDA that merges fine-grain parallelism using OpenMP and CUDA threads with coarse-grain parallelism using MPI for inter-node communication is presented.…”

Section: Mpi-openmp-cuda Implementationmentioning

confidence: 99%

See 1 more Smart Citation

Cpu/Gpu Computing for an Implicit Multi-Block Compressible Navier-Stokes Solver on Heterogeneous Platform

Deng

Bai

Wang

et al. 2016

Int. J. Mod. Phys. Conf. Ser.

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CPU/GPU computing allows scientists to tremendously accelerate their numerical codes. In this paper, we port and optimize a double precision alternating direction implicit (ADI) solver for three-dimensional compressible Navier-Stokes equations from our in-house Computational Fluid Dynamics (CFD) software on heterogeneous platform. First, we implement a full GPU version of the ADI solver to remove a lot of redundant data transfers between CPU and GPU, and then design two fine-grain schemes, namely "one-thread-one-point" and "one-thread-one-line", to maximize the performance. Second, we present a dual-level parallelization scheme using the CPU/GPU collaborative model to exploit the computational resources of both multi-core CPUs and many-core GPUs within the heterogeneous platform. Finally, considering the fact that memory on a single node becomes inadequate when the simulation size grows, we present a trilevel hybrid programming pattern MPI-OpenMP-CUDA that merges fine-grain parallelism using OpenMP and CUDA threads with coarse-grain parallelism using MPI for inter-node communication. We also propose a strategy to overlap the computation with communication using the advanced features of CUDA and MPI programming. We obtain speedups of 6.0 for the ADI solver on one Tesla M2050 GPU in contrast to two Xeon X5670 CPUs. Scalability tests show that our implementation can offer significant performance improvement on heterogeneous platform.

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Section: Mpi-openmp-cuda Implementationmentioning

confidence: 99%

“…7 illustrates the MPIOpenMP-CUDA programming pattern. However, there are no CUDA commands to operate data transfers between GPUs in different nodes 18 . The data transfers needs CPU-side data buffering, and then exchange by using MPI.…”

Section: Mpi-openmp-cuda Implementationmentioning

confidence: 99%

Cpu/Gpu Computing for an Implicit Multi-Block Compressible Navier-Stokes Solver on Heterogeneous Platform

Deng

Bai

Wang

et al. 2016

Int. J. Mod. Phys. Conf. Ser.

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“…For example, Yang et al [2012] proposed a CPU-assisted pre-execution algorithm to accelerate the execution of GPUs. Although some other researchers [Lee et al 2009;Becker et al 2009;Brown et al 2012;Spafford et al 2012;Power et al 2013;Cao et al 2014] studied the heterogeneous computing system, they mainly focus on the applications, simulation, or the mapping algorithm.…”

Section: Related Workmentioning

confidence: 99%

iConn

Goswami

2015

J. Emerg. Technol. Comput. Syst.

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Recently, the graphics processing unit (GPU) has made significant progress as a general-purpose parallel processor. The CPU and GPU cooperate together to solve data-parallel and control-intensive real-world applications in an optimized fashion. For example, emerging heterogeneous computing architectures such as Intel Sandy Bridge and AMD Fusion integrate the functionality of the CPU and GPU in a single die. However, the single-die CPU-GPU heterogeneous computing architecture faces the challenge of tight budget of die area. The conventional homogenous interconnect fails to provide satisfactory performance by fully exploiting the given area budget in the heterogeneous processing era.In this article, we aim to implement an interconnect network within an area budget for a CPU-GPU heterogeneous computing architecture. We propose iConn, a 2D mesh-style on-chip heterogeneous communication infrastructure. In iConn, a set of GPU logical units such as the stream processors, the texture units, and the rendering output units form a computing unit (CU). Differing from conventional homogenous router design, iConn adopts nonuniform on-chip routers in order to meet the unique communication demands from each single CPU and CU. The routers can also dynamically allocate their buffers across all virtual channels (VCs) to meet the latency requirements of CPUs and CUs. Moreover, the memory controller scheduling algorithm is modified from traditional load-over-store scheduling in order to prioritize the traffic. Our simulation results show that iConn improves the performance of CPUs by 23.0% and CUs by 9.4%. ACM Reference Format:Zhongqi Li, Nilanjan Goswami, and Tao Li. 2015. iConn: A communication infrastructure for heterogeneous computing architectures. ACM

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“…Although, for many existing models, these methods are expensive to adapt for model improvement. At the same time, to overcome speed problems of FEM and FVM models, many model scholars tried parallel computing, block-grid computing, and graphics processing unit (GPU) computing for improving the efficiency of numerical simulation [17][18][19]. Especially for large-scale water areas, high-performance computational methods are indispensable for the numerical simulation.…”

Section: Introductionmentioning

confidence: 99%

A Mesh Reduced Method for Speeding Up Structured Grid-Based Water Quantity and Quality Models in Large-Scale River Networks

Kang

Wang

et al. 2019

Water

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High-precision and efficiently distributed discrete element models for water environment simulation are urgently needed in large-scale river network areas, but most distributed discrete element models are serially computed and need performance improving. Parallel computing and other common methods for models’ high performance have large labor costs and are complicated. We put forward a new mesh reduced method for promoting computational efficiency with grid re-organization according to the structure and algorithm characteristics of 2D and 3D numerical models. This simple and cheap method was adapted to a classical three-dimensional hydrodynamic and sediment model (ECOMSED) for model improvement and effective evaluation. Six schemes with different grids were made to investigate the performance of this method in the river network area of the Three Gorges Reservoir Basin. It showed good characteristics of simulation performance and model speed-up. We concluded that the method is viable and efficient for optimizing distributed discrete element models.

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CPU/GPU computing for a multi-block structured grid based high-order flow solver on a large heterogeneous system

Cited by 16 publications

References 16 publications

Cpu/Gpu Computing for an Implicit Multi-Block Compressible Navier-Stokes Solver on Heterogeneous Platform

Cpu/Gpu Computing for an Implicit Multi-Block Compressible Navier-Stokes Solver on Heterogeneous Platform

iConn

A Mesh Reduced Method for Speeding Up Structured Grid-Based Water Quantity and Quality Models in Large-Scale River Networks

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