A real-time and reliable dynamic migration model for concurrent taskflow in a GPU cluster

Fang, Yuxiao; Chen, Qingkui

doi:10.1007/s10586-018-2866-8

Cited by 4 publications

(1 citation statement)

References 29 publications

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“…Combined with the above literature conclusions, it is a reasonable choice to design an efficient, lightweight and cross platform cluster communication architecture combining CUDA, Boost ThreadPool and Asio. The computing solution of multi GPU workstation cluster is generally divided into three levels: inter node parallel, intra node parallel and GPU parallel [43], as shown in Figure 2. It can be seen from the topology structure that the intra-node communication includes two parts, concurrent execution of CPU in the node and the parallel execution of GPU.…”

Section: Design and Implementation Of Multi-gpu Worktation Clusmentioning

confidence: 99%

Strength Check of Aircraft Parts Based on Multi-GPU Clusters for Fast Calculation of Sparse Linear Equations

Zhang

Hu²

2020

IEEE Access

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In order to improve the cost-effectiveness ratio, the next-generation vehicle needs to meet the requirements of reuse, while adopting a lighter structural weight, so it is necessary to realize the strength calculation and condition monitoring of key components in the digital twin. Most of the current monitoring methods are based on the characteristics of various data acquisition systems, but they need the support of a large number of flight data. The disadvantages of the above strategy can be avoided by reducing the structure of aircraft components to a finite element model and quickly checking the key components in the health management system. In order to solve the problem of fast calculation of the finite element model of the key components of the aircraft, a parallel algorithm and framework of large-scale sparse matrix preprocessing conjugate gradient method based on CUDA(Compute Unified Device Architecture) technology is proposed in the multi GPU(Graphics Processing Unit) workstation cluster environment. Once the sparse matrix is too large to be processed in a single workstation, this paper discusses how to realize the optimized data segmentation in the distributed multi-GPU computing environment. For the problem of iterative solution of matrix preprocessing, two preprocessing strategies of matrix bandwidth reduction parallelization and incomplete Cholesky decomposition are proposed, and asynchronous task concurrency and load balancing strategies are designed on the architecture. The calculation of some examples in the standard sparse matrix database shows that the algorithm and architecture proposed in this paper have the ability to solve large-scale sparse matrix quickly and efficiently, and can complete the fast strength verification of vehicle components.

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Section: Design and Implementation Of Multi-gpu Worktation Clusmentioning

confidence: 99%

Strength Check of Aircraft Parts Based on Multi-GPU Clusters for Fast Calculation of Sparse Linear Equations

Zhang

Hu²

2020

IEEE Access

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The Method of Improving the Performance of Network Analysis Application for the Whole Power Grid

et al. 2020

IOP Conf. Ser.: Earth Environ. Sci.

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With the rapid development of UHV AC / DC hybrid power grid, it is required that the Network Analysis Application have the ability of unified analysis and high-performance computing. In this paper, the time-consuming analysis of each sub-function of Network Analysis Application is carried out to realize the performance bottleneck analysis of the whole process of network analysis. It is proposed that the performance of network analysis should be improved in three aspects: data input and output, topology analysis and core computing of Network Analysis Application. In the aspect of data input, the grid model data service is constructed to realize the combination of measurement reading and model reading, and the parallel model verification and boundary equivalence are completed; In the aspect of data output, it can be parallelized by table, block and equipment; In the aspect of topology analysis, the shared memory programming model OpenMP is adopted, and based on the fork/join parallel mode, its parallelization is realized; In the aspect of core computing, the existing parallel computing methods are summarized, and through the actual power grid simulation analysis, it puts forward the parallel computing mode applicable to different scale power grids and different applications. Finally, the effectiveness of this method is verified by comparing the optimized performance of state estimation.

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Multi-User Concurrent Job Scheduling Method of Network Analysis Application Based on CPU/GPU Cluster

Luo

Lu³

et al. 2019

IOP Conf. Ser.: Mater. Sci. Eng.

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Multi-channel multi-core CPU parallel and CPU+GPU heterogeneous parallel are effective means to enhance Network Analysis Application computing. To solve the job scheduling problem of Network Analysis Application with multi-level dispatching, multi-user, multi-task in CPU/GPU heterogeneous cluster environment, a multi-user concurrent job scheduling method for Network Analysis Application in CPU/GPU cluster is proposed. According to the characteristics of Network Analysis Application, it is suggested that state estimation and dispatcher power flow should be accelerated in parallel with CPU, while static security analysis, perturbation power flow calculation and interruption capacity scanning should be accelerated in parallel with CPU+GPU. The job scheduling method proposed in this paper can satisfy the high concurrent requests of multi-level Dispatching & Control Center in the isomorphic and heterogeneous computing environments.

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A real-time and reliable dynamic migration model for concurrent taskflow in a GPU cluster

Cited by 4 publications

References 29 publications

Strength Check of Aircraft Parts Based on Multi-GPU Clusters for Fast Calculation of Sparse Linear Equations

Strength Check of Aircraft Parts Based on Multi-GPU Clusters for Fast Calculation of Sparse Linear Equations

The Method of Improving the Performance of Network Analysis Application for the Whole Power Grid

Multi-User Concurrent Job Scheduling Method of Network Analysis Application Based on CPU/GPU Cluster

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