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 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.

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 Wrapping FreeSurfer 6 for use in High-performance Computing Environments

GigaScience ◽  
2016 ◽  
Vol 5 (suppl_1) ◽  
Author(s):  
David G Ellis ◽  
Regina E.Y. Kim ◽  
Ipek Oguz ◽  
Hans J. Johnson
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Computing Environments ◽  
Performance Computing

scholarly journals A Framework for HI Spectral Source Finding Using Distributed-Memory Supercomputing

2014 ◽  
Vol 31 ◽  
Author(s):  
Stefan Westerlund ◽  
Christopher Harris
Keyword(s):  
High Performance ◽  
Distributed Memory ◽  
Computing Systems ◽  
Sky Surveys ◽  
Local Statistics ◽  
Wide Range ◽  
Gaussian Source ◽  
High Bandwidth ◽  
Traditional Approaches ◽  
Performance Computing

AbstractThe latest generation of radio astronomy interferometers will conduct all sky surveys with data products consisting of petabytes of spectral line data. Traditional approaches to identifying and parameterising the astrophysical sources within this data will not scale to datasets of this magnitude, since the performance of workstations will not keep up with the real-time generation of data. For this reason, it is necessary to employ high performance computing systems consisting of a large number of processors connected by a high-bandwidth network. In order to make use of such supercomputers substantial modifications must be made to serial source finding code. To ease the transition, this work presents the Scalable Source Finder Framework, a framework providing storage access, networking communication and data composition functionality, which can support a wide range of source finding algorithms provided they can be applied to subsets of the entire image. Additionally, the Parallel Gaussian Source Finder was implemented using SSoFF, utilising Gaussian filters, thresholding, and local statistics. PGSF was able to search on a 256GB simulated dataset in under 24 minutes, significantly less than the 8 to 12 hour observation that would generate such a dataset.

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