Time and Date: 14:10 - 15:50 on 11th June 2014
Chair: Angela Shiflet
|339|| Double-Degree Master's Program in Computational Science: Experiences of ITMO University and
University of Amsterdam
Abstract: We present a new double-degree graduate (Master's) programme developed together by the ITMO University, Russia and University of Amsterdam, The Netherlands. First, we look into the global aspects of integration of different educational systems and list some funding opportunities from European foundations. Then we describe our double-degree program curriculum, suggest the timeline of enrollment and studies, and give some examples of student research topics. Finally, we discuss the peculiarities of joint programs with Russia, reflect on the first lessons learnt, and share our thoughts and experiences that could be of interest to the international community expanding the educational markets to the vast countries like Russia, China or India. The paper is written for education professionals and contains useful information for potential students.
|Alexey Dukhanov, Valeria Krzhizhanovskaya, Anna Bilyatdinova, Alexander Boukhanovsky, Peter Sloot|
|254|| Critical Issues in the Teaching of High Performance Computing to Postgraduate Scientists [abstract]
Abstract: High performance computing is in increasing demand, especially with the need to conduct parallel processing on very large datasets, whether evaluated by volume, velocity and variety. Unfortunately the necessary skills - from familiarity with the command line interface, job submission, scripting, through to parallel programming - is not commonly taught at the level required for most researchers. As a result the uptake of HPC usage remains disproportionately low, with emphasis on system metrics taking priority, leading to a situation described as 'high performance computing considered harmful'. Changing this is not of a problem of computational science but rather a problem for computational science which can only be resolved from an multi-disciplinary approach. The following example addresses the main issues in such teaching and thus makes an appeal to some universality in application which may be useful for other institutions. For the past several years the Victorian Partnership for Advanced Computing (VPAC) has conducted a range of training courses designed to bring the capabilities of postgraduate researchers to a level of competence useful for their research. These courses have developed in this time, in part through providing a significantly wider range of content for varying skillsets, but more importantly by introducing some of the key insights from the discipline of adult and tertiary education in the context of the increasing trend towards lifelong learning. This includes an androgogical orientation, providing integrated structural knowledge, encouraging learner autonomy, self-efficacy, and self-determination, utilising appropriate learning styles for the discipline, utilising modelling and scaffolding for example problems (as a contemporary version of proximal learning), and following up with a connectivist mentoring and outreach program in the context of a culturally diverse audience.
|89|| A High Performance Computing Course Guided by the LU Factorization [abstract]
Abstract: This paper presents an experience of Problem-based learning in a High Performance Computing course. The course is part of the specialization of High Performance Architectures and Supercomputing in a Master on New Technologies in Computer Science. It is supposed the students have a basic knowledge of Parallel Programming, but previous studies and the place where they were taken mean the group is heterogeneous. The Problem-based learning approach therefore has to facilitate the individual development and supervision of the students. The course focuses on HPC, matrix computation, parallel libraries, heterogeneous computing and scientific applications of parallelism. The students work on the different aspects of the course using the LU factorization, developing their own implementations, using different libraries, combining different levels of parallelism and conducting experiments in a small heterogeneous cluster composed of multicores of different characteristics and with GPU of different types.
|Gregorio Bernabé, Javier Cuenca, Luis P. Garcia, Domingo Gimenez, Sergio Rivas-Gomez|
|50|| Teaching High Performance Computing using BeesyCluster and Relevant Usage Statistics [abstract]
Abstract: The paper presents motivations and experiences from using the BeesyCluster middleware for teaching high performance computing at the Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology. Features of BeesyCluster well suited for conducting courses are discussed including: easy-to-use WWW interface for application development and running hiding queuing systems, publishing applications as services and running in a sandbox by novice users, team work and workflow management environments. Additionally, practical experiences are discussed from courses: High Performance Computing Systems and Architectures of Internet Services. For the former, activities such as the number of team work activities, numbers of applications run on clusters and the number of WWW user sessions are shown over the period of one semester. Results of survey from a general course on BeesyCluster for HPC conducted for the university staff and students are also presented.