scholarly journals Unified, Cross-Platform, Open-Source Library Package for High-Performance Computing

2017 ◽  
Author(s):  
Stephen Kozacik
Keyword(s):  
Open Source ◽  
High Performance Computing ◽  
High Performance ◽  
Cross Platform ◽  
Performance Computing

scholarly journals Inspiring the Next Generation of HPC Engineers with Reconfigurable, Multi-Tenant Resources for Teaching and Research

2021 ◽  
Vol 13 (21) ◽  
pp. 11782
Author(s):  
Taha Al-Jody ◽  
Hamza Aagela ◽  
Violeta Holmes
Keyword(s):  
Open Source ◽  
High Performance Computing ◽  
Systems Engineering ◽  
High Performance ◽  
Bare Metal ◽  
Next Generation ◽  
Teaching And Research ◽  
Exascale Computing ◽  
Secure Access ◽  
Performance Computing

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.

scholarly journals MSA: reproducible mutational signature attribution with confidence based on simulations

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Sergey Senkin
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
De Novo ◽  
Least Squares Method ◽  
Parametric Bootstrap ◽  
Mutational Signatures ◽  
Mutational Signature ◽  
Cross Platform ◽  
Computing Environments ◽  
Performance Computing

Abstract Background Mutational signatures proved to be a useful tool for identifying patterns of mutations in genomes, often providing valuable insights about mutagenic processes or normal DNA damage. De novo extraction of signatures is commonly performed using Non-Negative Matrix Factorisation methods, however, accurate attribution of these signatures to individual samples is a distinct problem requiring uncertainty estimation, particularly in noisy scenarios or when the acting signatures have similar shapes. Whilst many packages for signature attribution exist, a few provide accuracy measures, and most are not easily reproducible nor scalable in high-performance computing environments. Results We present Mutational Signature Attribution (MSA), a reproducible pipeline designed to assign signatures of different mutation types on a single-sample basis, using Non-Negative Least Squares method with optimisation based on configurable simulations. Parametric bootstrap is proposed as a way to measure statistical uncertainties of signature attribution. Supported mutation types include single and doublet base substitutions, indels and structural variants. Results are validated using simulations with reference COSMIC signatures, as well as randomly generated signatures. Conclusions MSA is a tool for optimised mutational signature attribution based on simulations, providing confidence intervals using parametric bootstrap. It comprises a set of Python scripts unified in a single Nextflow pipeline with containerisation for cross-platform reproducibility and scalability in high-performance computing environments. The tool is publicly available from https://gitlab.com/s.senkin/MSA.

scholarly journals Using High Performance Computing and Open Source Technologies for Solving Behaviour Analytics Problems in E-Learning

2015 ◽  
Vol 4 ◽  
pp. 529
Author(s):  
Laimonis Zacs ◽  
Anita Jansone
Keyword(s):  
Open Source ◽  
High Performance Computing ◽  
High Performance ◽  
Management Systems ◽  
Course Management Systems ◽  
Apache Hadoop ◽  
Industry Standard ◽  
E Learning ◽  
Technological Opportunities ◽  
Performance Computing

<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>

scholarly journals Enabling ITK-based processing and 3D Slicer MRML scene management in ParaView

2012 ◽  
Author(s):  
Andinet Enquobahrie ◽  
Michael Bowers ◽  
Luis Ibanez ◽  
Julien Finet ◽  
Michel Audette ◽  
...  
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Research Community ◽  
3D Slicer ◽  
Overall Design ◽  
Scene Management ◽  
Imaging Research ◽  
Imaging Science ◽  
Cross Platform ◽  
Performance Computing

This paper documents on-going work to facilitate ITK-based processing and 3D Slicer scene management in ParaView. We believe this will broaden the use of ParaView for high performance computing and visualization in the medical imaging research community. The effort is focused on developing ParaView plug-ins for managing VTK structures from 3D Slicer MRML scenes and encapsulating ITK filters for deployment in ParaView. In this paper, we present KWScene, an open source cross-platform library that is being developed to support implementation of these types of plugins. We describe the overall design of the library and provide implementation details and conclude by presenting a concrete example that demonstrates the use of the KWScene library in computational anatomy research at Johns Hopkins Center for Imaging Science.

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