Design tools for electromagnetic-driven multi-physics systems using high performance computing

2019 ◽  
Vol 39 (1) ◽  
pp. 198-205
Author(s):  
David Lowther ◽  
Vahid Ghorbanian ◽  
Mohammad Hossain Mohammadi ◽  
Issah Ibrahim
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Complex Analysis ◽  
Job Scheduling ◽  
Computing System ◽  
Electromagnetic System ◽  
Content Type ◽  
Mechanical Coupling ◽  
The Impact ◽  
Performance Computing

Purpose The design of electromagnetic systems for a variety of applications such as induction heating, electrical machines, actuators and transformers requires the solution of a multi-physics problem often involving thermal, structural and mechanical coupling to the electromagnetic system. This results in a complex analysis system embedded within an optimization process. The appearance of high-performance computing systems over the past few years has made coupled simulations feasible for the design engineer. When coupled with surrogate modelling techniques, it is possible to significantly reduce the wall clock time for generating a complete design while including the impact of the multi-physics performance on the device. Design/methodology/approach An architecture is proposed for linking multiple singe physics analysis tools through the material models and a controller which schedules the execution of the various software tools. The combination of tools is implemented on a series of computational nodes operating in parallel and creating a “super node” cluster within a collection of interconnected processors. Findings The proposed architecture and job scheduling system can allow a parallel exploration of the design space for a device. Originality/value The originality of the work derives from the organization of the parallel computing system into a series of “super nodes” and the creation of a materials database suitable for multi-physics interactions.

A High-Performance Computing System for Probabilistic Weather Forecasts

2019 ◽  
Author(s):  
Weiming Hu ◽  
Guido Cervone ◽  
Vivek Balasubramanian ◽  
Matteo Turilli ◽  
Shantenu Jha
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Computing System ◽  
Weather Forecasts ◽  
High Performance Computing System ◽  
Performance Computing

scholarly journals Taxonomic assignment for large-scale metagenomic data on high-perfomance systems

2017 ◽  
Vol 33 (2) ◽  
pp. 119-130
Author(s):  
Vinh Van Le ◽  
Hoai Van Tran ◽  
Hieu Ngoc Duong ◽  
Giang Xuan Bui ◽  
Lang Van Tran
Keyword(s):  
High Performance Computing ◽  
Assignment Problem ◽  
High Performance ◽  
Large Scale ◽  
Computing System ◽  
Metagenomic Data ◽  
Taxonomic Assignment ◽  
High Performance Computing System ◽  
Powerful Approach ◽  
Performance Computing

Metagenomics is a powerful approach to study environment samples which do not require the isolation and cultivation of individual organisms. One of the essential tasks in a metagenomic project is to identify the origin of reads, referred to as taxonomic assignment. Due to the fact that each metagenomic project has to analyze large-scale datasets, the metatenomic assignment is very much computation intensive. This study proposes a parallel algorithm for the taxonomic assignment problem, called SeMetaPL, which aims to deal with the computational challenge. The proposed algorithm is evaluated with both simulated and real datasets on a high performance computing system. Experimental results demonstrate that the algorithm is able to achieve good performance and utilize resources of the system efficiently. The software implementing the algorithm and all test datasets can be downloaded at http://it.hcmute.edu.vn/bioinfo/metapro/SeMetaPL.html.

scholarly journals An Application Of Pde To Predict Brain Tumor Growth Using High Performance Computing System

1970 ◽  
Vol 6 (1) ◽  
pp. 60-68 ◽  
Author(s):  
Md Rajibul Islam ◽  
Norma Alias ◽  
Siew Young Ping
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
High Performance Computing ◽  
High Performance ◽  
Computing System ◽  
Two Dimensional ◽  
Explicit Method ◽  
Parallel Virtual Machine ◽  
High Performance Computing System ◽  
Performance Computing

This study is to predict two-dimensional brain tumors growth through parallel algorithm using the High Performance Computing System. The numerical finite-difference method is highlighted as a platform for discretization of twodimensional parabolic equations. The consequence of a type of finite difference approximation namely explicit method will be presented in this paper. The numerical solution is applied in the medical field by solving a mathematical model for the diffusion of brain tumors which is a new technique to predict brain tumor growth. A parabolic mathematical model used to describe and predict the evolution of tumor from the avascular stage to the vascular, through the angiogenic process. The parallel algorithm based on High Performance Computing (HPC) System is used to capture the growth of brain tumors cells in two-dimensional visualization. PVM (Parallel Virtual Machine) software is used as communication platform in the HPC System. The performance of the algorithm evaluated in terms of speedup, efficiency, effectiveness and temporal performance. Keywords: Partial Differential Equation (PDE); parabolic equation; explicit method; Red Black Gauss-Seidel; Parallel Virtual Machine (PVM); High Performance Computing (HPC); Brain Tumor. DOI: http://dx.doi.org/10.3329/diujst.v6i1.9335 DIUJST 2011; 6(1): 60-68

High-performance computing for computational modelling in built environment-related studies – a scientometric review

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Emmanuel Imuetinyan Aghimien ◽  
Lerato Millicent Aghimien ◽  
Olutomilayo Olayemi Petinrin ◽  
Douglas Omoregie Aghimien
Keyword(s):  
High Performance Computing ◽  
Graphics Processing Units ◽  
High Performance ◽  
Large Scale ◽  
Computational Models ◽  
Computational Modelling ◽  
Scientometric Analysis ◽  
Content Type ◽  
Performance Computing ◽  
Aec Industry

Purpose This paper aims to present the result of a scientometric analysis conducted using studies on high-performance computing in computational modelling. This was done with a view to showcasing the need for high-performance computers (HPC) within the architecture, engineering and construction (AEC) industry in developing countries, particularly in Africa, where the use of HPC in developing computational models (CMs) for effective problem solving is still low. Design/methodology/approach An interpretivism philosophical stance was adopted for the study which informed a scientometric review of existing studies gathered from the Scopus database. Keywords such as high-performance computing, and computational modelling were used to extract papers from the database. Visualisation of Similarities viewer (VOSviewer) was used to prepare co-occurrence maps based on the bibliographic data gathered. Findings Findings revealed the scarcity of research emanating from Africa in this area of study. Furthermore, past studies had placed focus on high-performance computing in the development of computational modelling and theory, parallel computing and improved visualisation, large-scale application software, computer simulations and computational mathematical modelling. Future studies can also explore areas such as cloud computing, optimisation, high-level programming language, natural science computing, computer graphics equipment and Graphics Processing Units as they relate to the AEC industry. Research limitations/implications The study assessed a single database for the search of related studies. Originality/value The findings of this study serve as an excellent theoretical background for AEC researchers seeking to explore the use of HPC for CMs development in the quest for solving complex problems in the industry.

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