Bridging the HPC Tallent Gap with Computational Science Research Methods (BRIDGE) Session 1
Time and Date: 10:35 - 12:15 on 1st June 2015
Chair: Nia Alexandrov
|589|| Computational Science Research Methods for Science Education at PG level [abstract]
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.
|717|| A New Canadian Interdisciplinary PhD in Computational Sciences [abstract]
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.
|William Gardner, Gary Grewal, Deborah Stacey, David Calvert, Stefan Kremer and Fangju Wang|
|730|| I have a DRIHM: A case study in lifting computational science services up to the scientific mainstream [abstract]
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.
|Michael Schiffers, Nils Gentschen Felde, Dieter Kranzlmüller|
|335|| Mathematical Modelling Based Learning Strategy [abstract]
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.
|Raul Ramirez, Nia Alexandrov, José Raúl Pérez Cázares, Carlos Barba-Jimenez|