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

Integrating supercomputing clusters into education: a case study in biotechnology

The integration of a Supercomputer in the educational process improves student’s technological skills. The aim of the paper is to study the interaction between sci-ence, technology, engineering, and mathematics (STEM) and non-STEM subjects for developing a course of study related to Supercomputing training. We propose a flowchart of the process to improve the performance of students attending courses related to Supercomputing. As a final result, this study highlights the analysis of the information obtained by the use of HPC infrastructures in courses implemented in higher education through a questionnaire that provides useful information about their attitudes, beliefs and evaluations. The results help us to understand how the collaboration between institutions enhances outcomes in the education context. The conclusion provides a description of the resources needed for the improvement of Supercomputing Education (SE), proposing future research directions. 2018-1-ES01-KA201-05093SIComisión EuropeaMinisterio de Ciencia e InnovaciónMinisterio de Economía y CompetitividadFundación Centro de Supercomputación de Castilla y Leó

Análisis y evaluación del uso de la supercomputación en la mejora del desempeño formativo = Analysis and evaluation of supercomputing for training performance improvement

205 p.Los recursos de supercomputación son en la actualidad el pilar fundamental para el desarrollo de la investigación en diversos campos. Su impacto se basa en la capacidad de cálculo, que permite realizar simulaciones computacionales que permiten mejorar la precisión de los experimentos. La presente Tesis Doctoral pretende, en primer lugar, realizar un estudio de la evolución de la supercomputación y su aplicación a diversos campos para, posteriormente, estudiar los factores determinantes que permitan analizar los aspectos más relevantes a la hora de estudiar la relación existente entre los estudios de supercomputación con los aspectos pedagógicos, de conocimiento y de contenido, basándose en el modelo TPACK. El estudio se realizó con información procedente de la base de datos de estudiantes del Centro de Supercomputación de Castilla y León (SCAYLE), de la que se obtuvieron 97 participantes. En el estudio se realizó un análisis factorial para comprobar que la estructura de datos obtenida era coherente con el modelo TPACK usado como referencia. Los resultados obtenidos del análisis relacionan las dimensiones tecnológicas con las de conocimiento, pedagógicas y de contenido