Abstract: The role of Computational Science research methods teaching to science students at PG level is to enhance their research profile developing their abilities to investigate complex problems, analyse the resulting data and use adequately HPC environments and tools for computation and visualisation. The paper analyses the current state and proposes a program that encompass mathematical modelling, data science, advanced algorithms development, parallel programming and visualisation tools. It also gives examples of specific scientific domains with explicitly taught and embedded Computational Science subjects.

Abstract: In response to growing demands of society for experts trained in computational skills applied to various domains, the School of Computer Science at the University of Guelph is creating a new approach to doctoral studies called an Interdisciplinary PhD in Computational Sciences. The program is designed to appeal to candidates with strong backgrounds in either computer science or an application discipline who are not necessarily seeking a traditional academic career. Thesis based, it features minimal course requirements and short duration, with the student’s research directed by co-advisors from computer science and the application discipline. The degree program’s rationale and special characteristics are described. Related programs in Ontario and reception of this innovative proposal at the institutional level are discussed.

Abstract: While we are witnessing a transition from petascale to exascale computing, we experience, when teaching students and scientists to adopt distributed computing infrastructures for computational sciences, what Geoffrey A. Moore once coined the chasm between the visionaries in computational sciences and the early majority of scientific pragmatists. Using the EU-funded DRIHM project (Distributed Research Infrastructure for Hydro-Meteorology) as a case study, we see that innovative research infrastructures have difficulties to be accepted by the scientific pragmatists: The infrastructure services are not yet "mainstream". Excellence in workforces in computational sciences, however, can only be achieved if the tools are not only available but also used. In this paper we show for DRIHM how the chasm exhibits and how it can be crossed.

Abstract: Mathematical modelling is a difficult skill to acquire and transfer. In order to succeed in transferring the ability to model the observable world, the environment in which modelling is taught should resemble as much as possible the real environment in which students will leave and work. We devised a learning strategy based on modelling environmental variables in order to link weather conditions to weather emergencies by pollutants in the atmosphere of Monterrey, Mexico, metropolitan area. We structure course topics around a single comprehensive and integrative project. The objective of the project is to create a model that will predict behavior of existing phenomena using real data. In this case, we used data collected by weather stations. This data consists of weather information such as temperature, pressure, humidity, wind speed and the like. And, it also contains information about pollutants such as O3, CO2, CO, SO2, NOx, particles, etc. Students follow a procedure consisting for 4 stages. In the first stage they analyze the data; try to reduce dimensionality, link weather variables to contaminants and determine characteristic behaviours. In the second stage, students interpolate missing data and project component data to a 2D map of the metro area. In the third stage students create the mathematical model by carrying out curve fitting through least squares technique. In the third stage, students solve the model by finding roots, solving systems of equations, solving differential equations or integrating. The final deliverable is to determine under which weather conditions there can be an environmental contingency that put people’s health in danger. Class topics are taught in the order necessary to carry out the project. Any necessary knowledge required for the project not contemplated by course syllabus is carried out through team presentations with worked-out examples. Analysis of the strategy is presented as well as preliminary results.

Abstract: The paper describes an ontology-based methods and framework for design of learning courses covering the HPC and Big Data areas and how to include these into Computational Science training within the remit of existing courses of Master Programme entitled “Applied Mathematics and Computer Science” (Faculty of Mechanics and Mathematics, Perm State University, Russia). It helped bringing together the university and IT-companies around a real industry projects in the field of Big Data with active participation of master’s students. In this paper, the visual tools and ontology-based methods for computer-supported collaborative learning environment will be also presented.

Abstract: 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 project 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.

Abstract: Recently, the idea of quantum computation is becoming more and more popular and there are many attempts to build quantum computers. Therefore, there is a need to introduce this topic to regular students of computer science and engineering. In this paper we present a concept of a course powered by the Quantum Integrated Development Environment (QuIDE), the new quantum computer simulator that joins features of GUI based simulators with interpreters and simulation library approach. The idea of the course is to put together theoretical aspects with practical assignments realized on the QuIDE simulator. Such an approach enables studying a variety of topics in a way understandable for this category of students. The topics of the course included understanding the concept of quantum gates, registers and a series of algorithms: Deutsch and Bernstein-Vazirani Problems, Grover's Fast Database Search, Shor's Prime Factorization, Quantum Teleportation and Quantum Dense Coding. We describe results of QuIDE assessment during the course; our solution scored more points in System Usability Scale survey then the other tool previously used for that purpose. We also show that the most useful features of such a tool indicated by students are similar to the assumptions made on the simulator functionality.

Abstract: In this paper the use of adaptive scientific visualization tools in education, including in the area of high performance computing education is proposed in order to help students understand in depth the nature of particular scientific problems and to help them to learn parallel computing approaches to solving these problems. The proposed approach may help to bridge the talent gap in natural and computational sciences, since high quality visualization can help to uncover hidden regularities in the data with which the researchers and students work and can lead to new level of understanding how the data can be partitioned and processed in parallel. A multiplatform client-server scientific visualization system is presented that can be easily integrated with third-party solvers from any field of science. This system can be used as a visual aid and a collaboration tool in high performance computing education.