2,948 research outputs found
Developing High Performance Computing Resources for Teaching Cluster and Grid Computing courses
High-Performance Computing (HPC) and the ability to process large amounts of data are of
paramount importance for UK business and economy as outlined by Rt Hon David Willetts
MP at the HPC and Big Data conference in February 2014. However there is a shortage of
skills and available training in HPC to prepare and expand the workforce for the HPC and
Big Data research and development. Currently, HPC skills are acquired mainly by students
and staff taking part in HPC-related research projects, MSc courses, and at the dedicated
training centres such as Edinburgh Universityâs EPCC. There are few UK universities teaching
the HPC, Clusters and Grid Computing courses at the undergraduate level. To address the
issue of skills shortages in the HPC it is essential to provide teaching and training as part of
both postgraduate and undergraduate courses. The design and development of such courses is
challenging since the technologies and software in the fields of large scale distributed systems
such as Cluster, Cloud and Grid computing are undergoing continuous change. The students
completing the HPC courses should be proficient in these evolving technologies and equipped
with practical and theoretical skills for future jobs in this fast developing area.
In this paper we present our experience in developing the HPC, Cluster and Grid modules
including a review of existing HPC courses offered at the UK universities. The topics covered in
the modules are described, as well as the coursework projects based on practical laboratory work.
We conclude with an evaluation based on our experience over the last ten years in developing
and delivering the HPC modules on the undergraduate courses, with suggestions for future work
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FABRIC: A National-Scale Programmable Experimental Network Infrastructure
FABRIC is a unique national research infrastructure to enable cutting-edge and exploratory research at-scale in networking, cybersecurity, distributed computing and storage systems, machine learning, and science applications. It is an everywhere-programmable nationwide instrument comprised of novel extensible network elements equipped with large amounts of compute and storage, interconnected by high speed, dedicated optical links. It will connect a number of specialized testbeds for cloud research (NSF Cloud testbeds CloudLab and Chameleon), for research beyond 5G technologies (Platforms for Advanced Wireless Research or PAWR), as well as production high-performance computing facilities and science instruments to create a rich fabric for a wide variety of experimental activities
Survey and Analysis of Production Distributed Computing Infrastructures
This report has two objectives. First, we describe a set of the production
distributed infrastructures currently available, so that the reader has a basic
understanding of them. This includes explaining why each infrastructure was
created and made available and how it has succeeded and failed. The set is not
complete, but we believe it is representative.
Second, we describe the infrastructures in terms of their use, which is a
combination of how they were designed to be used and how users have found ways
to use them. Applications are often designed and created with specific
infrastructures in mind, with both an appreciation of the existing capabilities
provided by those infrastructures and an anticipation of their future
capabilities. Here, the infrastructures we discuss were often designed and
created with specific applications in mind, or at least specific types of
applications. The reader should understand how the interplay between the
infrastructure providers and the users leads to such usages, which we call
usage modalities. These usage modalities are really abstractions that exist
between the infrastructures and the applications; they influence the
infrastructures by representing the applications, and they influence the ap-
plications by representing the infrastructures
Leveraging OpenStack and Ceph for a Controlled-Access Data Cloud
While traditional HPC has and continues to satisfy most workflows, a new
generation of researchers has emerged looking for sophisticated, scalable,
on-demand, and self-service control of compute infrastructure in a cloud-like
environment. Many also seek safe harbors to operate on or store sensitive
and/or controlled-access data in a high capacity environment.
To cater to these modern users, the Minnesota Supercomputing Institute
designed and deployed Stratus, a locally-hosted cloud environment powered by
the OpenStack platform, and backed by Ceph storage. The subscription-based
service complements existing HPC systems by satisfying the following unmet
needs of our users: a) on-demand availability of compute resources, b)
long-running jobs (i.e., days), c) container-based computing with
Docker, and d) adequate security controls to comply with controlled-access data
requirements.
This document provides an in-depth look at the design of Stratus with respect
to security and compliance with the NIH's controlled-access data policy.
Emphasis is placed on lessons learned while integrating OpenStack and Ceph
features into a so-called "walled garden", and how those technologies
influenced the security design. Many features of Stratus, including tiered
secure storage with the introduction of a controlled-access data "cache",
fault-tolerant live-migrations, and fully integrated two-factor authentication,
depend on recent OpenStack and Ceph features.Comment: 7 pages, 5 figures, PEARC '18: Practice and Experience in Advanced
Research Computing, July 22--26, 2018, Pittsburgh, PA, US
The medical science DMZ: a network design pattern for data-intensive medical science
Abstract:
Objective
We describe a detailed solution for maintaining high-capacity, data-intensive network flows (eg, 10, 40, 100 Gbps+) in a scientific, medical context while still adhering to security and privacy laws and regulations.
Materials and Methods
High-end networking, packet-filter firewalls, network intrusion-detection systems.
Results
We describe a âMedical Science DMZâ concept as an option for secure, high-volume transport of large, sensitive datasets between research institutions over national research networks, and give 3 detailed descriptions of implemented Medical Science DMZs.
Discussion
The exponentially increasing amounts of âomicsâ data, high-quality imaging, and other rapidly growing clinical datasets have resulted in the rise of biomedical research âBig Data.â The storage, analysis, and network resources required to process these data and integrate them into patient diagnoses and treatments have grown to scales that strain the capabilities of academic health centers. Some data are not generated locally and cannot be sustained locally, and shared data repositories such as those provided by the National Library of Medicine, the National Cancer Institute, and international partners such as the European Bioinformatics Institute are rapidly growing. The ability to store and compute using these data must therefore be addressed by a combination of local, national, and industry resources that exchange large datasets. Maintaining data-intensive flows that comply with the Health Insurance Portability and Accountability Act (HIPAA) and other regulations presents a new challenge for biomedical research. We describe a strategy that marries performance and security by borrowing from and redefining the concept of a Science DMZ, a framework that is used in physical sciences and engineering research to manage high-capacity data flows.
Conclusion
By implementing a Medical Science DMZ architecture, biomedical researchers can leverage the scale provided by high-performance computer and cloud storage facilities and national high-speed research networks while preserving privacy and meeting regulatory requirements
The Strategy of the Commons: Modelling the Annual Cost of Successful ICT Services for European Research
The provision of ICT services for research is increasingly using Cloud services to complement the traditional federation of computing centres. Due to the complex funding structure and differences in the basic business model, comparing the cost-effectiveness of these options requires a new approach to cost assessment. This paper presents a cost assessment method addressing the limitations of the standard methods and some of the initial results of the study. This acts as an illustration of the kind of cost assessment issues high-utilisation rate ICT services should consider when choosing between different infrastructure options. The research is co-funded by the European Commission Seventh Framework Programme through the e-FISCAL project (contract number RI-283449)
From Bare Metal to Virtual: Lessons Learned when a Supercomputing Institute Deploys its First Cloud
As primary provider for research computing services at the University of
Minnesota, the Minnesota Supercomputing Institute (MSI) has long been
responsible for serving the needs of a user-base numbering in the thousands.
In recent years, MSI---like many other HPC centers---has observed a growing
need for self-service, on-demand, data-intensive research, as well as the
emergence of many new controlled-access datasets for research purposes. In
light of this, MSI constructed a new on-premise cloud service, named Stratus,
which is architected from the ground up to easily satisfy data-use agreements
and fill four gaps left by traditional HPC. The resulting OpenStack cloud,
constructed from HPC-specific compute nodes and backed by Ceph storage, is
designed to fully comply with controls set forth by the NIH Genomic Data
Sharing Policy.
Herein, we present twelve lessons learned during the ambitious sprint to take
Stratus from inception and into production in less than 18 months. Important,
and often overlooked, components of this timeline included the development of
new leadership roles, staff and user training, and user support documentation.
Along the way, the lessons learned extended well beyond the technical
challenges often associated with acquiring, configuring, and maintaining
large-scale systems.Comment: 8 pages, 5 figures, PEARC '18: Practice and Experience in Advanced
Research Computing, July 22--26, 2018, Pittsburgh, PA, US
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GSCC targeted inspections of Approved Mental Health Professionals (AMHP) courses in England (2011-12)
The General Social Care Council (GSCC) has duties under Section 19 of the Mental Health Act 2007 to approve Approved Mental Health Profession (AMHP) training. This function will transfer to the Health Professions Council on 31 July 2012. The GSCC carried out a targeted inspection between March 2011 and February 2012 of all 22 AMHP programmes across England. This was to ensure at the point of transfer all courses were sufficiently meeting standards. There had been a number of issues and concerns raised about inconsistencies in the quality of AMHP programmes. The inspection process involved consulting all stakeholders concerned in delivering and using the programmes, including AMHP candidates, service users and carers. Each programme has their inspection report on the GSCC website. The key findings of these inspections are that the majority of the 22 programmes are meeting the standards expected and where they are not, action has been taken to ensure that prior to transfer of the GSCCâs AMHP inspection function, all programmes will meet the standards and not require regulatory intervention. There is inconsistency in the length and attached credit of each AMHP programme, although this did not seem to impair the threshold standard required for being competent in the AMHP role. This composite report features the overall outputs from the inspections
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