Simulation modelling and visualisation: toolkits for building artificial worlds

Simulations users at all levels make heavy use of compute resources to drive computational simulations for greatly varying applications areas of research using different simulation paradigms. Simulations are implemented in many software forms, ranging from highly standardised and general models that run in proprietary software packages to ad hoc hand-crafted simulations codes for very specific applications. Visualisation of the workings or results of a simulation is another highly valuable capability for simulation developers and practitioners. There are many different software libraries and methods available for creating a visualisation layer for simulations, and it is often a difficult and time-consuming process to assemble a toolkit of these libraries and other resources that best suits a particular simulation model. We present here a break-down of the main simulation paradigms, and discuss differing toolkits and approaches that different researchers have taken to tackle coupled simulation and visualisation in each paradigm

The LAB@FUTURE Project - Moving Towards the Future of E-Learning

This paper presents Lab@Future, an advanced e-learning platform that uses novel Information and Communication Technologies to support and expand laboratory teaching practices. For this purpose, Lab@Future uses real and computer-generated objects that are interfaced using mechatronic systems, augmented reality, mobile technologies and 3D multi user environments. The main aim is to develop and demonstrate technological support for practical experiments in the following focused subjects namely: Fluid Dynamics - Science subject in Germany, Geometry - Mathematics subject in Austria, History and Environmental Awareness – Arts and Humanities subjects in Greece and Slovenia. In order to pedagogically enhance the design and functional aspects of this e-learning technology, we are investigating the dialogical operationalisation of learning theories so as to leverage our understanding of teaching and learning practices in the targeted context of deployment

On the Internationalization of CAD Learning Through an English Glossary

Comunicació presentada al XXIX Congreso International INGEGRAF 2019 "La transformación Digital en la Ingeniería Gráfica” (20-21 Junio 2019, Logroño - La Rioja)The internationalization of higher education is an essential factor to improve the quality and efficiency of Spanish universities, providing students with the main skills, and knowledge to interact effectively in an international and multicultural work context as professionals. The internationalization of universities must be a transversal process, not exclusive of its territorial dimension, aimed at advancing towards a society and a knowledge economy that propitiate a solid and stable model of development and growth. To this end, professors in the area of Graphic Expression for Engineering at the Universitat Jaume I (UJI) have developed an online glossary of specific terms in English related to the 3D modelling CAD tools used in Graphic Engineering subjects. This new online tool seeks to train students to increase their technical vocabulary in English and improve their learning and communication skills to face possible collaborations in future European projects. The glossary is introduced weekly to the students during the course. Subsequently, a survey is conducted to the students to verify the effectiveness of the training. This work collects the results and conclusions of this analysis

Coding Landscape: Teaching Computer Programming to Landscape Architects

How to best teach coding to landscape architects? Domain-specific approaches to teaching computer programming are surprisingly rare. Most computer programming curricula teach skills in a generic way, to be broadly relevant to many people. A rapidly increasing number and ways of teaching how to code to a range of skill levels is now available online, usually for free (see Appendix, RICHTEL 2015, GASCA 2014, FRAMPTON 2015, SIMS et al. 2011). Yet in landscape architecture coding is often regarded as too difficult, too resource-intensive, insufficiently relevant to practice, or otherwise peripheral to the core mission of the profession to teach (WESTORT et al. 2013) . As a result, fundamentals of coding logic remain largely un-taught in accredited core curricula in the U.S. This paper has three objectives: 1. Offer a landscape architecture-specific approach to teaching introductory computer programming that combines a) landscape parametrics with b) concepts of computer programming logic and c) basic computer graphics. 2. Present a sequence of exercises intended to impart fundamental skills and peak student interest. 3. Showcase student project results that use the approach. A sequence of short programming exercises asks students to define the geometry of elements from the landscape palette – vegetation, landform, water, weather, lighting – and then to modify them using increasingly more advanced and complex coding principles in a modular fashion. The following criteria for successful landscape design software is offered to students as a guide to structuring their software: Graphically display landscape geometry, such that it is Interactively editable/modifiable/deformable and Analysable with accuracy and some precision Quickly, while being Easy to lear

An Introduction to Programming for Bioscientists: A Python-based Primer

Computing has revolutionized the biological sciences over the past several decades, such that virtually all contemporary research in the biosciences utilizes computer programs. The computational advances have come on many fronts, spurred by fundamental developments in hardware, software, and algorithms. These advances have influenced, and even engendered, a phenomenal array of bioscience fields, including molecular evolution and bioinformatics; genome-, proteome-, transcriptome- and metabolome-wide experimental studies; structural genomics; and atomistic simulations of cellular-scale molecular assemblies as large as ribosomes and intact viruses. In short, much of post-genomic biology is increasingly becoming a form of computational biology. The ability to design and write computer programs is among the most indispensable skills that a modern researcher can cultivate. Python has become a popular programming language in the biosciences, largely because (i) its straightforward semantics and clean syntax make it a readily accessible first language; (ii) it is expressive and well-suited to object-oriented programming, as well as other modern paradigms; and (iii) the many available libraries and third-party toolkits extend the functionality of the core language into virtually every biological domain (sequence and structure analyses, phylogenomics, workflow management systems, etc.). This primer offers a basic introduction to coding, via Python, and it includes concrete examples and exercises to illustrate the language's usage and capabilities; the main text culminates with a final project in structural bioinformatics. A suite of Supplemental Chapters is also provided. Starting with basic concepts, such as that of a 'variable', the Chapters methodically advance the reader to the point of writing a graphical user interface to compute the Hamming distance between two DNA sequences.Comment: 65 pages total, including 45 pages text, 3 figures, 4 tables, numerous exercises, and 19 pages of Supporting Information; currently in press at PLOS Computational Biolog

PyCUDA and PyOpenCL: A Scripting-Based Approach to GPU Run-Time Code Generation

High-performance computing has recently seen a surge of interest in heterogeneous systems, with an emphasis on modern Graphics Processing Units (GPUs). These devices offer tremendous potential for performance and efficiency in important large-scale applications of computational science. However, exploiting this potential can be challenging, as one must adapt to the specialized and rapidly evolving computing environment currently exhibited by GPUs. One way of addressing this challenge is to embrace better techniques and develop tools tailored to their needs. This article presents one simple technique, GPU run-time code generation (RTCG), along with PyCUDA and PyOpenCL, two open-source toolkits that support this technique. In introducing PyCUDA and PyOpenCL, this article proposes the combination of a dynamic, high-level scripting language with the massive performance of a GPU as a compelling two-tiered computing platform, potentially offering significant performance and productivity advantages over conventional single-tier, static systems. The concept of RTCG is simple and easily implemented using existing, robust infrastructure. Nonetheless it is powerful enough to support (and encourage) the creation of custom application-specific tools by its users. The premise of the paper is illustrated by a wide range of examples where the technique has been applied with considerable success.Comment: Submitted to Parallel Computing, Elsevie