Parallel space–time <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="mml30" display="inline" overflow="scroll" altimg="si2.gif"><mml:mi>h</mml:mi><mml:mi>p</mml:mi></mml:math> adaptive discretization scheme for parabolic problems

Łoś, Marcin; Schaefer, Robert; Paszyński, Maciej

doi:10.1016/j.cam.2017.12.005

Cited by 2 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, modal analysis is the most time-consuming link among all calculation processes of these complex dynamic systems and is also the basis for other calculations. High-precision modal analysis requires the help of large-scale finite element models, but these problems usually cannot get satisfactory solutions on traditional serial computers [6,7]. As a consequence, the research and development of corresponding large-scale modal analysis parallel algorithms can provide a feasible method for solving such problems on the basis of parallel supercomputers.…”

Section: Introductionmentioning

confidence: 99%

A Multilevel Hierarchical Parallel Algorithm for Large-Scale Finite Element Modal Analysis

Yu,

Lou,

Dong

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

The strict and high-standard requirements for the safety and stability of major engineering systems make it a tough challenge for large-scale finite element modal analysis. At the same time, realizing the systematic analysis of the entire large structure of these engineering systems is extremely meaningful in practice. This article proposes a multilevel hierarchical parallel algorithm for large-scale finite element modal analysis to reduce the parallel computational efficiency loss when using heterogeneous multicore distributed storage computers in solving large-scale finite element modal analysis. Based on two-level partitioning and four-transformation strategies, the proposed algorithm not only improves the memory access rate through the sparsely distributed storage of a large amount of data but also reduces the solution time by reducing the scale of the generalized characteristic equation (GCEs). Moreover, a multilevel hierarchical parallelization approach is introduced during the computational procedure to enable the separation of the communication of inter-nodes, intra-nodes, heterogeneous core groups (HCGs), and inside HCGs through mapping computing tasks to various hardware layers. This method can efficiently achieve load balancing at different layers and significantly improve the communication rate through hierarchical communication. Therefore, it can enhance the efficiency of parallel computing of large-scale finite element modal analysis by fully exploiting the architecture characteristics of heterogeneous multicore clusters. Finally, typical numerical experiments were used to validate the correctness and efficiency of the proposed method. Then a parallel modal analysis example of the cross-river tunnel with over ten million degrees of freedom (DOFs) was performed, and ten-thousand core processors were applied to verify the feasibility of the algorithm.

show abstract

Section: Introductionmentioning

confidence: 99%

A Multilevel Hierarchical Parallel Algorithm for Large-Scale Finite Element Modal Analysis

Yu,

Lou,

Dong

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

A New Hybrid Hierarchical Parallel Algorithm to Enhance the Performance of Large-Scale Structural Analysis Based on Heterogeneous Multicore Clusters

Yu¹,

Lou²,

Dong³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

Heterogeneous multicore clusters are becoming more popular for high-performance computing due to their great computing power and cost-to-performance effectiveness nowadays. Nevertheless, parallel efficiency degradation is still a problem in large-scale structural analysis based on heterogeneous multicore clusters. To solve it, a hybrid hierarchical parallel algorithm (HHPA) is proposed on the basis of the conventional domain decomposition algorithm (CDDA) and the parallel sparse solver. In this new algorithm, a three-layer parallelization of the computational procedure is introduced to enable the separation of the communication of inter-nodes, heterogeneous-core-groups (HCGs) and inside-heterogeneous-core-groups through mapping computing tasks to various hardware layers. This approach can not only achieve load balancing at different layers efficiently but can also improve the communication rate significantly through hierarchical communication. Additionally, the proposed hybrid parallel approach in this article can reduce the interface equation size and further reduce the solution time, which can make up for the shortcoming of growing communication overheads with the increase of interface equation size when employing CDDA. Moreover, the distributed sparse storage of a large amount of data is introduced to improve memory access. By solving benchmark instances on the Shenwei-Taihuzhiguang supercomputer, the results show that the proposed method can obtain higher speedup and parallel efficiency compared with CDDA and more superior extensibility of parallel partition compared with the two-level parallel computing algorithm (TPCA).

show abstract

Parallel space–time hp adaptive discretization scheme for parabolic problems

Cited by 2 publications

References 30 publications

A Multilevel Hierarchical Parallel Algorithm for Large-Scale Finite Element Modal Analysis

A Multilevel Hierarchical Parallel Algorithm for Large-Scale Finite Element Modal Analysis

A New Hybrid Hierarchical Parallel Algorithm to Enhance the Performance of Large-Scale Structural Analysis Based on Heterogeneous Multicore Clusters

Contact Info

Product

Resources

About