The OMII Software – Demonstrations and Comparisons between two different deployments for Client-Server Distributed Systems

This paper describes the key elements of the OMII software and the scenarios which OMII software can be deployed to achieve distributed computing in the UK e-Science Community, where two different deployments for Client-Server distributed systems are demonstrated. Scenarios and experiments for each deployment have been described, with its advantages and disadvantages compared and analyzed. We conclude that our first deployment is more relevant for system administrators or developers, and the second deployment is more suitable for users’ perspective which they can send and check job status for hundred job submissions

Leveraging simulation practice in industry through use of desktop grid middleware

This chapter focuses on the collaborative use of computing resources to support decision making in industry. Through the use of middleware for desktop grid computing, the idle CPU cycles available on existing computing resources can be harvested and used for speeding-up the execution of applications that have “non-trivial” processing requirements. This chapter focuses on the desktop grid middleware BOINC and Condor, and discusses the integration of commercial simulation software together with free-to-download grid middleware so as to offer competitive advantage to organizations that opt for this technology. It is expected that the low-intervention integration approach presented in this chapter (meaning no changes to source code required) will appeal to both simulation practitioners (as simulations can be executed faster, which in turn would mean that more replications and optimization is possible in the same amount of time) and the management (as it can potentially increase the return on investment on existing resources)

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 University’s 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

Parallel detrended fluctuation analysis for fast event detection on massive PMU data

("(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.")Phasor measurement units (PMUs) are being rapidly deployed in power grids due to their high sampling rates and synchronized measurements. The devices high data reporting rates present major computational challenges in the requirement to process potentially massive volumes of data, in addition to new issues surrounding data storage. Fast algorithms capable of processing massive volumes of data are now required in the field of power systems. This paper presents a novel parallel detrended fluctuation analysis (PDFA) approach for fast event detection on massive volumes of PMU data, taking advantage of a cluster computing platform. The PDFA algorithm is evaluated using data from installed PMUs on the transmission system of Great Britain from the aspects of speedup, scalability, and accuracy. The speedup of the PDFA in computation is initially analyzed through Amdahl's Law. A revision to the law is then proposed, suggesting enhancements to its capability to analyze the performance gain in computation when parallelizing data intensive applications in a cluster computing environment

Parallel detrended fluctuation analysis for fast event detection on massive PMU data

("(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.")Phasor measurement units (PMUs) are being rapidly deployed in power grids due to their high sampling rates and synchronized measurements. The devices high data reporting rates present major computational challenges in the requirement to process potentially massive volumes of data, in addition to new issues surrounding data storage. Fast algorithms capable of processing massive volumes of data are now required in the field of power systems. This paper presents a novel parallel detrended fluctuation analysis (PDFA) approach for fast event detection on massive volumes of PMU data, taking advantage of a cluster computing platform. The PDFA algorithm is evaluated using data from installed PMUs on the transmission system of Great Britain from the aspects of speedup, scalability, and accuracy. The speedup of the PDFA in computation is initially analyzed through Amdahl's Law. A revision to the law is then proposed, suggesting enhancements to its capability to analyze the performance gain in computation when parallelizing data intensive applications in a cluster computing environment