scholarly journals Modernization and optimization of a legacy open-source CFD code for high-performance computing architectures

2017 ◽  
Vol 31 (2) ◽  
pp. 122-133 ◽  
Author(s):  
Aytekin Gel ◽  
Jonathan Hu ◽  
ElMoustapha Ould-Ahmed-Vall ◽  
Alexander A. Kalinkin
2021 ◽  
Vol 13 (21) ◽  
pp. 11782
Author(s):  
Taha Al-Jody ◽  
Hamza Aagela ◽  
Violeta Holmes

There is a tradition at our university for teaching and research in High Performance Computing (HPC) systems engineering. With exascale computing on the horizon and a shortage of HPC talent, there is a need for new specialists to secure the future of research computing. Whilst many institutions provide research computing training for users within their particular domain, few offer HPC engineering and infrastructure-related courses, making it difficult for students to acquire these skills. This paper outlines how and why we are training students in HPC systems engineering, including the technologies used in delivering this goal. We demonstrate the potential for a multi-tenant HPC system for education and research, using novel container and cloud-based architecture. This work is supported by our previously published work that uses the latest open-source technologies to create sustainable, fast and flexible turn-key HPC environments with secure access via an HPC portal. The proposed multi-tenant HPC resources can be deployed on a “bare metal” infrastructure or in the cloud. An evaluation of our activities over the last five years is given in terms of recruitment metrics, skills audit feedback from students, and research outputs enabled by the multi-tenant usage of the resource.


Author(s):  
Laimonis Zacs ◽  
Anita Jansone

<p><em>In this paper the authors describe solution for solving various analytical problems in <em>E-learning, Course Management Systems like Moodle by using HPC</em></em> <em>(High Performance Computing) and Apache Hadoop open source technologies in Liepaja University. The problem is that nowadays there are collecting huge amounts of analytics data from several gigabytes to petabytes, which is hard to store, process, analyse and visualize. This article reflects one of the solutions concerning distributed parallel processing of huge amounts of data across inexpensive, industry-standard servers that can store and process the data, can scale without limits and provides technological opportunities of reliable, scalable and distributed computing.</em><em> </em></p><p> </p>


2019 ◽  
Author(s):  
Jonathan Ozik ◽  
Nicholson Collier ◽  
Randy Heiland ◽  
Gary An ◽  
Paul Macklin

We present an integrated framework for enabling dynamic exploration of design spaces for cancer immunotherapies with detailed dynamical simulation models on high-performance computing resources. Our framework combines PhysiCell, an open source agent-based simulation platform for cancer and other multicellular systems, and EMEWS, an open source platform for extreme-scale model exploration. We build an agent-based model of immunosurveillance against heterogeneous tumours, which includes spatial dynamics of stochastic tumour-immune contact interactions. We implement active learning and genetic algorithms using high-performance computing workflows to adaptively sample the model parameter space and iteratively discover optimal cancer regression regions within biological and clinical constraints.


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