scholarly journals A comprehensive framework to capture the arcana of neuroimaging analysis

2018 ◽  
Author(s):  
Thomas G. Close ◽  
Phillip G. D. Ward ◽  
Francesco Sforazzini ◽  
Wojtek Goscinski ◽  
Zhaolin Chen ◽  
...  
Keyword(s):  
Data Storage ◽  
High Performance ◽  
Software Framework ◽  
Mri Contrast ◽  
Analysis Methods ◽  
Wide Range ◽  
Computing Environments ◽  
Comprehensive Framework ◽  
Performance Computing ◽  
Generic Analysis

AbstractMastering the “arcana of neuroimaging analysis”, the obscure knowledge required to apply an appropriate combination of software tools and parameters to analyse a given neuroimaging dataset, is a time consuming process. Therefore, it is not typically feasible to invest the additional effort required generalise workflow implementations to accommodate for the various acquisition parameters, data storage conventions and computing environments in use at different research sites, limiting the reusability of published workflows.We present a novel software framework, Abstraction of Repository-Centric ANAlysis (Arcana), which enables the development of complex, “end-to-end” workflows that are adaptable to new analyses and portable to a wide range of computing infrastructures. Analysis templates for specific image types (e.g. MRI contrast) are implemented as Python classes, which define a range of potential derivatives and analysis methods. Arcana retrieves data from imaging repositories, which can be BIDS datasets, XNAT instances or plain directories, and stores selected derivatives and associated provenance back into a repository for reuse by subsequent analyses. Workflows are constructed using Nipype and can be executed on local workstations or in high performance computing environments. Generic analysis methods can be consolidated within common base classes to facilitate code-reuse and collaborative development, which can be specialised for study-specific requirements via class inheritance. Arcana provides a framework in which to develop unified neuroimaging workflows that can be reused across a wide range of research studies and sites.

scholarly journals Performance evaluation of container-based virtualization for high performance computing environments

2019 ◽  
Vol 18 (4) ◽  
pp. 31-42 ◽  
Author(s):  
Carlos Arango ◽  
Rémy Dernat ◽  
John Sanabria
Keyword(s):  
Operating Systems ◽  
High Performance ◽  
Bare Metal ◽  
Single Node ◽  
Network Bandwidth ◽  
Wide Range ◽  
Computing Environments ◽  
Performance Area ◽  
Performance Computing ◽  
Fair Sharing

Virtualization technologies have evolved along with the development of computational environments. Virtualization offered needed features at that time such as isolation, accountability, resource allocation, resource fair sharing and so on. Novel processor technologies bring to commodity computers the possibility to emulate diverse environments where a wide range of computational scenarios can be run. Along with processors evolution, developers have implemented different virtualization mechanisms exhibiting enhanced performance from previous virtualized environments. Recently, operating system-based virtualization technologies captured the attention of communities abroad because their important improvements on performance area. In this paper, the features of three container-based operating systems virtualization tools (LXC, Docker and Singularity) are presented. LXC, Docker, Singularity and bare metal are put under test through a customized single node HPL-Benchmark and a MPI-based application for the multi node testbed. Also the disk I/O performance, Memory (RAM) performance, Network bandwidth and GPU performance are tested for the COS technologies vs bare metal. Preliminary results and conclusions around them are presented and discussed.

scholarly journals CyVerse for Reproducible Research: RNA-Seq Analysis

2022 ◽  
pp. 57-79
Author(s):  
Jason Williams
Keyword(s):  
Data Storage ◽  
High Performance ◽  
Lessons Learned ◽  
Data Availability ◽  
Reproducible Research ◽  
Rna Seq ◽  
Data Intensive ◽  
Interactive Computing ◽  
Computing Environments ◽  
Performance Computing

AbstractPosing complex research questions poses complex reproducibility challenges. Datasets may need to be managed over long periods of time. Reliable and secure repositories are needed for data storage. Sharing big data requires advance planning and becomes complex when collaborators are spread across institutions and countries. Many complex analyses require the larger compute resources only provided by cloud and high-performance computing infrastructure. Finally at publication, funder and publisher requirements must be met for data availability and accessibility and computational reproducibility. For all of these reasons, cloud-based cyberinfrastructures are an important component for satisfying the needs of data-intensive research. Learning how to incorporate these technologies into your research skill set will allow you to work with data analysis challenges that are often beyond the resources of individual research institutions. One of the advantages of CyVerse is that there are many solutions for high-powered analyses that do not require knowledge of command line (i.e., Linux) computing. In this chapter we will highlight CyVerse capabilities by analyzing RNA-Seq data. The lessons learned will translate to doing RNA-Seq in other computing environments and will focus on how CyVerse infrastructure supports reproducibility goals (e.g., metadata management, containers), team science (e.g., data sharing features), and flexible computing environments (e.g., interactive computing, scaling).

High Performance Computing on Mobile Devices

2016 ◽  
pp. 334-348
Author(s):  
Atta ur Rehman Khan ◽  
Abdul Nasir Khan
Keyword(s):  
Cloud Computing ◽  
High Performance Computing ◽  
Mobile Devices ◽  
High Performance ◽  
Mobile Cloud Computing ◽  
Computation Offloading ◽  
Mobile Cloud ◽  
Computing Technology ◽  
Wide Range ◽  
Performance Computing

Mobile devices are gaining high popularity due to support for a wide range of applications. However, the mobile devices are resource constrained and many applications require high resources. To cater to this issue, the researchers envision usage of mobile cloud computing technology which offers high performance computing, execution of resource intensive applications, and energy efficiency. This chapter highlights importance of mobile devices, high performance applications, and the computing challenges of mobile devices. It also provides a brief introduction to mobile cloud computing technology, its architecture, types of mobile applications, computation offloading process, effective offloading challenges, and high performance computing application on mobile devises that are enabled by mobile cloud computing technology.

scholarly journals Knowledge Base Commons (KBCommons) v1.1: a universal framework for multi-omics data integration and biological discoveries

BMC Genomics ◽  
2019 ◽  
Vol 20 (S11) ◽  
Author(s):  
Shuai Zeng ◽  
Zhen Lyu ◽  
Siva Ratna Kumari Narisetti ◽  
Dong Xu ◽  
Trupti Joshi
Keyword(s):  
Knowledge Base ◽  
Data Storage ◽  
High Performance ◽  
Data Retrieval ◽  
Omics Data ◽  
Web Interface ◽  
Web Resource ◽  
Privilege Management ◽  
Multi Level ◽  
Comprehensive Framework

Abstract Background Knowledge Base Commons (KBCommons) v1.1 is a universal and all-inclusive web-based framework providing generic functionalities for storing, sharing, analyzing, exploring, integrating and visualizing multiple organisms’ genomics and integrative omics data. KBCommons is designed and developed to integrate diverse multi-level omics data and to support biological discoveries for all species via a common platform. Methods KBCommons has four modules including data storage, data processing, data accessing, and web interface for data management and retrieval. It provides a comprehensive framework for new plant-specific, animal-specific, virus-specific, bacteria-specific or human disease-specific knowledge base (KB) creation, for adding new genome versions and additional multi-omics data to existing KBs, and for exploring existing datasets within current KBs. Results KBCommons has an array of tools for data visualization and data analytics such as multiple gene/metabolite search, gene family/Pfam/Panther function annotation search, miRNA/metabolite/trait/SNP search, differential gene expression analysis, and bulk data download capacity. It contains a highly reliable data privilege management system to make users’ data publicly available easily and to share private or pre-publication data with members in their collaborative groups safely and securely. It allows users to conduct data analysis using our in-house developed workflow functionalities that are linked to XSEDE high performance computing resources. Using KBCommons’ intuitive web interface, users can easily retrieve genomic data, multi-omics data and analysis results from workflow according to their requirements and interests. Conclusions KBCommons addresses the needs of many diverse research communities to have a comprehensive multi-level OMICS web resource for data retrieval, sharing, analysis and visualization. KBCommons can be publicly accessed through a dedicated link for all organisms at http://kbcommons.org/.

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