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/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

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Flow The Psychology Of Optimal Experience

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Creativity: Flow and the Psychology of Discovery and Invention

The problem of portability and reproducibility of the software used to conduct computational experiments has recently come to the fore. Container virtualisation has proved to be a powerful tool to achieve portability of a code and it's execution environment, through runtimes such as Docker, LXC, Singularity and others - without the performance cost of traditional Virtual Machines (Chamberlain, Invenshure, and Schommer 2014; Felter et al. 2014).

However, scientific software often depends on a system foundation that provides middleware, libraries, and other supporting software in order for the code to execute as intended. Typically, container virtualisation addresses only the portability of the code itself, which does not make it inherently reproducible. For example, a containerized MPI application may offer binary compatibility between different systems, but for execution as intended, it must be run on an existing cluster that provides the correct interfaces for parallel MPI execution.

As a greater demand to accomodate a diverse range of disciplines is placed on high performance and cluster resources, the ability to quickly create and teardown reproducible, transitory virtual environments that are tailored for an individual task or experiment will be essential.

The Virtual Container Cluster (VCC) is a framework for building containers that achieve this goal, by encapsulating a parallel application along with an execution model, through a set of dependency linked services and built-in process orchestration. This promotes a high degree of portability, and offers easier reproducibility by shipping the application along with the foundation required to execute it - whether that be an MPI cluster, big data processing framework, bioinformatics pipeline, or any other execution model (Higgins, Holmes, and Venters 2017).

/pdf/vcc-a-framework-for-building-containerized-reproducible-1tgwex4lm4.pdf

VCC: A framework for building containerized reproducible cluster software environments

The rate of scientific discovery depends on the speed at which accurate results and analysis can be obtained. The use of parallel co-processors such as Graphical Processing Units (GPUs) is becoming more and more important in meeting this demand as improvements in serial data processing speed become increasingly difficult to sustain. However, parallel data processing requires more complex programming compared to serial processing. Here we present our methods for parallelising two pieces of scientific software, leveraging multiple GPUs to achieve up to thirty times speed up.

Delivering Faster Results Through Parallelisation and GPU Acceleration

Cyber security is becoming a vital part of many information technologies and computing systems. Increasingly, High-Performance Computing systems are used in scientific research, academia and industry. High-Performance Computing applications are specifically designed to take advantage of the parallel nature of High-Performance Computing systems. Current research into High-Performance Computing systems focuses on the improvements in software development, parallel algorithms and computer systems architecture. However, there are no significant efforts in developing common High-Performance Computing security standards. Security of the High-Performance Computing resources is often an add-on to existing varied institutional policies that do not take into account additional requirements for High-Performance Computing security. Also, the users' terminals or portals used to access the High-Performance Computing resources are frequently insecure or they are being used in unprotected networks. In this paper we present Bearicade - a Data-driven Security Orchestration Automation and Response system. Bearicade collects data from the HPC systems and its users, enabling the use of Machine Learning based solutions to address current security issues in the High-Performance Computing systems. The system security is achieved through monitoring, analysis and interpretation of data such as users' activity, server requests, devices used and geographic locations. Any anomaly in users' behaviour is detected using machine learning algorithms, and would be visible to system administrators to help mediate the threats. The system was tested on a university campus grid system by administrators and users. Two case studies, Anomaly detection of user behaviour and Classification of Malicious Linux Terminal Command, have demonstrated machine learning approaches in identifying potential security threats. Bearicade's data was used in the experiments. The results demonstrated that detailed information is provided to the HPC administrators to detect possible security attacks and to act promptly.

Bearicade: secure access gateway to High Performance Computing systems

The campus grid architectures currently available are considered to be overly complex. We have focused on High Throughput Condor HTCondor as one of the most popular middlewares among UK universities, and are proposing a new system for unifying campus grid resources. This new system PBStoCondor is capable of interfacing with Linux based system within the campus grids, and automatically determining the best resource for a given job. The system does not require additional efforts from users and administrators of the campus grid resources. We have compared the real usage data and PBStoCondor system simulation data. The results show a close match. The proposed system will enable better utilization of campus grid resources, and will not require modification in users' workflows.

/pdf/pbstohtcondor-system-for-campus-grids-14sax11pi9.pdf

PBStoHTCondor system for campus grids

The needs of the research communities in research institutes and Higher Education (HE) establishments are demanding evermore powerful computing resources for supporting complex scientific and industrial simulation and modeling, manipulating and storage of large quantities of data [6, 9]. In this paper we present our experience at the University of Huddersfield (UoH), UK in developing the HPC systems infrastructure, removing a technical burden from researchers and enabling quicker and more insightful research outcomes. We have designed and implemented the University of Huddersfield, Queens gate Grid (QGG) campus grid [7]. In the process of building QGG systems and optimising its performance, we have designed and implemented a reliable network system infrastructure. The network topology was re-designed in various stages of system deployment resulting in a reduction of the number of switches, routers and network interconnects. This has led to an improvement in data transmission, a reduction in the possibility of bottlenecks and much reduced data loss [2, 9]. The rapid expansion of our campus grid has led us to question the energy efficiency of our HPC systems. Our initial investigation has targeted the transfer of data and power usage with a view to extending this work to incorporate other metrics, which is the subject of further work.

/pdf/campus-hpc-network-design-and-monitoring-10er3fl64h.pdf

Violeta Holmes

Papers

VCC: A framework for building containerized reproducible cluster software environments

Delivering Faster Results Through Parallelisation and GPU Acceleration

Bearicade: secure access gateway to High Performance Computing systems

PBStoHTCondor system for campus grids

Campus HPC Network Design and Monitoring