Workstation Cellular Framework for Computer Aided Design Interactive Online Courses

Inevitably, the global pandemic that occurred in 2020 had a significant impact on the practices of design education, and apparently, design teachers were not fully prepared and equipped. In this ambivalent and emergent era, they struggled to adapt their current teaching methods to online education. Since then, various teaching methods have been developed and applied to incorporate online delivery. As we see an invitation to advance learner-centered and process-based teaching approaches regarding Computer-Aided Design (CAD) education in the literature; in this article, we address how a constructively aligned CAD course has been adapted to online learning and how it affected design students’ learning ex­per­iences. Hence, we discuss the online CAD learning experience through students’ reflections based on the Activity-Centered Analysis and Design (ACAD) Framework. To get insights into their online learning experience on CAD, we asked students to write their thoughts based on a structured qualitative course evaluation template. Broadly, online learning practices in CAD not only resulted in challenges in design learning but also created opportunities as mentioned in students’ writings. Based on qualitative content analysis, students’ reflections on a CAD class implemented under the context of online learning can include both advantages and disadvantages of the online learning platform, peer learning, peer tutoring, active involvement in the class, communication, etc. As a result, students’ reflections on the CAD course made us think that the dimensions of the ACAD Framework are interwoven and interactive.

We present a novel heterogeneous parallel matrix multiplication algorithm that utilizes both central processing units (CPUs) and graphics processing units (GPUs) for large-scale matrices. Based on Strassen’s method, we represent matrix multiplication work as a set of matrix addition and multiplication tasks among their sub-matrices. Then, we distribute the tasks to CPUs and GPUs while considering the characteristics of the tasks and computing resources to minimize the data communication overhead and fully utilize the available computing power. To handle a large matrix efficiently with limited GPU memory, we also propose a block-based work decomposition method. We then further improve the performance of our method by exploiting the concurrent execution abilities of a heterogeneous parallel computing system. We implemented our method on five different heterogeneous systems and applied it to matrices of various sizes. Our method generally shows higher performance than the prior GPU-based matrix multiplication methods. Moreover, compared with the state-of-the-art GPU matrix multiplication library (i.e., CUBLAS), our method achieved up to 1.97 times higher performance using the same GPUs and CPU cores. In some cases, our method using a low-performance GPU (e.g., GTX 1060, 3 GB) achieved performance comparable to that of CUBLAS using a high-performance GPU (e.g., RTX 2080, 8 GB). Also, our method continually improves performance as we use more computing resources like additional CPU cores and GPUs. We could achieve such high performance because our approach fully utilized the capacities of the given heterogeneous parallel computing systems while employing the Strassen’s method, which has a lower asymptotic complexity. These results demonstrate the efficiency and robustness of our algorithm.

In the last decade, the entertainment and graphic design industries demand for higher resolution and more realistic graphics has motivated graphic card manufacturers to develop high performance graphic processing units (GPUs) at low cost. Nowadays GPUs are highly efficient parallel processing units that can be used for general purposes. Such developments open a new alternative within scientific computation, promising high performance parallel computation at everyones reach. Graphic cards seem to be an attractive option, specially for research groups and institutions with limited computational resources and for teaching purposes. Here, an overview of parallel computation on GPUs is presented, as well as the efforts to manipulate and promote this technology within the UNAM, particularly in Fluid Mechanics applications. KeywordsGraphic Processing UnitGraphic CardSingle PrecisionSingle Program Multiple DataDouble Precision ComputationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

We present a high performance graphics processing unit (GPU) algorithm ISSD (Improved Sorting considering Special Distributions) implemented with the Compute Unified Device Architecture (CUDA). The ISSD focuses on two aspects to improve performance. One is how to decompose the tasks into independent and balanced subtasks which can then be easily distributed to thousands of threads to realize the concept of parallel sorting as well as to efficiently explore the power of GPU. The other one is how to take advantage of special data distributions to further optimize the algorithms and improve their performance. The algorithm is redesigned based on our previous general data distribution version and optimized both on general implementation methods and special input distributions. Experimental results show that for the general data distribution inputs, the ISSD outperforms the existing algorithms by about 10% in performance due to its practical optimization in implementation, and for three special data distribution inputs, the ISSD outperforms the existing algorithms by more than 40% due to its special optimization based on the data distributions. Therefore, the algorithm is viable and efficient when dealing with specific data distributions.

Prior to the COVID-19 pandemic, most of us had little to no experience with online teaching. With the realization that we would need to teach our students remotely during the pandemic, we needed to develop our online teaching skills and apply this knowledge to create effective online classes for our students. Tulane University's Center for Engaged Learning and Teaching (CELT, http://celt.tulane.edu) and its Innovative Learning Center (ILC, http://it.tulane.edu/innovative-learning-center) partnered to teach an Online Teaching Training (OTT) course for all instructors. This five-week session covered many of the topics needed to teach an engaging and interactive online course. The training included not only the theory but also practical applications of many resources available to online course instructors. This training prepared me to teach a fully online course for the first time in Summer, 2020 and again the following summer. Student evaluations compared between the Emergency Remote Learning done in quick response to the COVID-19 pandemic (Spring 2020) and the online course after completion of the OTT (Summer 2020) indicated that student experiences were much more positive when taught by a trained instructor. Many of the resources/techniques for online courses can be incorporated into in-person or hybrid classes, and vice versa. Further, sharing ideas among colleagues, especially as new resources become available, is critical for the success of all instructors. In this article I share my lessons learned, insights, and thoughts for moving forward as we approach a new era of neuroscience instruction.

In the last 10 years we have provided an interactive online course on Computer Architecture and Organization, which uses Online Learning and Interactive e-Assessment technology to support easier learning. The technology enabled an interactive environment which, checks student's knowledge and enables continuation to the next learning environment based on decision making criteria to check if sufficient knowledge is acquired by the student for a given learning objective. The system works in such a way, that the decision is made based on three correct answers in a row for questions set in a given area. Our experience shows that establishing interactive environment for online learning helps the students to gain knowledge and skills more easily, and improves the overall understanding and knowledge discovery and construction for the given course.

Bioinspired methods usually need a high amount of computational resources. For this reason, parallelization is an interesting alternative in order to decrease the execution time and to provide accurate results. In this sense, recently there has been a growing interest in developing parallel algorithms using graphic processing units (GPU) also refered as GPU computation. Advances in the video gaming industry have led to the production of low-cost, high-performance graphics processing units (GPUs) that possess more memory bandwidth and computational capability than central processing units (CPUs). As GPUs are available in personal computers, and they are easy to use and manage through several GPU programming languages (CUDA, OpenCL, etc.), graphics engines are being adopted widely in scientific computing applications, particularly in the fields of computational biology and bioinformatics. This paper reviews the use of GPUs to solve scientific problems, giving an overview of current software systems.

At the basis of this article is the goal of increasing the efficiency of the process of building interactive online courses by analyzing and developing specific requirements for the training content provided by the authors so that the subsequent stages of developing effective online courses can be optimized and implemented with as few iterations as possible.

Relevance of research. The development of information technology and the use of computers in biological and medical disciplines have significantly changed the way in which specialists in these fields work and learn. When interacting with information resources, which include software, medical devices, and Internet resources, students and future professionals should be especially attentive to occupational safety issues. Accordingly, studying the basics of occupational safety in these contexts becomes a critical component of the training of future biologists and physicians. The above determines the relevance of considering the main aspects of occupational safety training when working with information tools among students of biological and medical specialties, as well as determining the key challenges and opportunities of this process. The purpose of this article is to reveal the features of training future specialists in the natural and medical fields for safe and effective professional activities that involve the use of information and communication technologies. Research methods: analysis and synthesis of scientific literature, systematization, theoretical generalization of research results. Research results. In our opinion, the key challenges of occupational safety training when working with information tools for future specialists in natural and medical specialties are the following: the growing number of cyber threats and attacks that endanger information systems requires knowledge in the field of cyber security from medical and biological specialists; working with medical data and personal information of patients requires compliance with high security standards to preserve confidentiality; training specialists in quick and effective response to cyber attacks and incidents is becoming an important component of occupational safety. At the same time, it is necessary to emphasize the opportunities that open up during the coverage of occupational safety issues with informational means for future specialists in biology and doctors: the use of simulation programs allows future specialists to learn safety in conditions that reproduce real situations; interactive online courses and webinars provide an opportunity to learn the basic principles of occupational safety using information tools; access to electronic resources, such as online guides, textbooks and video materials, allows students to independently study and deepen their knowledge of occupational safety issues; regular training and practical exercises help future professionals to consolidate the skills of occupational safety and response to extreme situations.

Aim The aim of this project was to identify the biggest fears of IMGs regarding the NHS and address those fears via a locally designed online course, ultimately promoting equality, diversity, and inclusion in the NHS. Method The course was created after consultation with IMGs working in the NHS, and the content was based on the Higher-Level Outcomes (HLOs) of the Foundation Programme. The course was divided into 4 sessions, delivered weekly. Each session had an initial presentation followed by an interactive question answer segment, had 3 faculty personnel, and lasted 2 hours. A good laptop with internet connection was all that was needed for the course. Results In total, 699 participants registered for the course. About 92% of them had never attended similar courses before, and about 73% has never worked in the NHS. Lack of knowledge of the NHS system (32%) and medico-legal issues (25%) were the biggest fears of prospective IMGs regarding working in the NHS. Post course feedback revealed that their confidence about working in the NHS increased by 30% after this course. Their confidence in working on-calls and building portfolio has also increased after the course by almost 26%. 78% participants said their biggest fear was addressed by this course. The question answer segment was rated helpful by 92% of participants. Conclusions This study illustrated that we could promote equality, diversity, and inclusion in the NHS by addressing the biggest fear of IMGs of not knowing the NHS system, using low resource, interactive online courses.

In this paper a new hardware accelerated method is presented to evaluate the machinability of free-form surfaces. This method works on tessellated models that are commonly used by computer aided design (CAD) systems to render three-dimensional shaded images of solid models. Modern graphics processing units (GPUs) can be programed in hardware to accelerate specialized rendering techniques. In this research, we have developed new algorithms that utilize the programmability of GPUs to evaluate the machinability of free-form surfaces. The method runs in real-time on fairly inexpensive hardware (<$600), and performs well regardless of the surface type. The complexity of the method is dictated by the size of the projected view of the model. The proposed method can be used as a plug-in in a CAD system to evaluate the manufacturability of a part at early design stages. The efficiency and the speed of the proposed method are demonstrated on some complex objects.

Aim The ‘Making the Leap’ interactive online course (MTL) was specifically designed to be delivered during the COVID-19 restrictions. This course aimed to facilitate the difficult transition from CT to ST3 in Plastic Surgery. Method The courses consisted of interactive small-group tutorials, each conducted over 2 days, via Zoom, covering 12 plastic surgery core topics in 8 tutorials. The course was attended by 43 delegates from across the UK and abroad. The tutorials were delivered by 27 plastic surgery consultants. Anonymous feedback was collected from both delegates and faculty. Results The overall feedback from delegates were unanimously positive; 100% of the delegates stated that ‘they would recommend the course to a colleague’. All faculty and delegates agreed the use of video conferencing and small group tutorial to be either ‘very’ or ‘extremely’ suitable for an interactive virtual course like MTL. 98% of delegates thought the course has made them feel more prepared to be a registrar, and 69% of delegates preferred an online interactive course instead of the traditional face-to-face method, for non-practical based courses. Conclusions COVID-19 necessitated delivering ‘Making the Leap” as a Zoom course. However, the feedback clearly demonstrated the need for such a course, and the suitability of the format. We discuss the challenges and learning points from our experience delivering successful interactive courses via Zoom.

Teaching computer-aided building design (CABD) in a project-based learning environment is an idea derived from the broader problem-based learning movement. However, a project-based learning curriculum has rarely been implemented in building design education, let alone in CABD. The idea of teaching CABD through design-based approaches has been increasingly adopted over the past 20 years. However, most of the resulting experiments have been limited to specific courses within larger programmes. This approach has rarely affected a dedicated CABD curriculum. The development and implementation of a feasible project-based learning CABD curriculum is described in this paper. In this programme the emphasis is placed on the building aspects and/or objectives of the design process rather than on the different products of the design phases or even on software categories.

Team performance depends on both individual and collaborative skills. This dependence creates increasing education and training demand while striving to improve teams’ efficiency. Consequentially, training and education systems emerge with new capabilities that are changing the learning landscape. With increasingly disperse and mobile teams it can be very inefficient and costly to provide training and education in a centralized instructor-led classes’ manner. Remote solutions are able to reach far more potential users at any moment, and tend to be satisfactory and possibly preferred in many different training and education areas. Computer Aided Design (CAD) requires high quality graphics for positive impact and high satisfaction. Software tools used for hands-on CAD training exercises depend on dedicated Graphics Processing Unit (GPU) to deal with complex graphics processing needed to visualize virtual models in real-time. It is challenging for the remote training system to offer this high level of experience to remote users. In this paper, synergy of conventional CAD laboratory workstations into cells is proposed in order to create a cost-effective team training remote system. The system utilizes existing capabilities of dedicated GPUs and custom software modules to capture video, perform hardware HEVC encoding, and stream the video at low bitrates and sub-second latency to remote team members.

Diversity is sometimes understood narrowly, by focusing on only one or two categories, such as gender or race. Following the concept of intersectionality, there are far more aspects to diversity that can affect students’ success, in some cases only indirectly or subtly. We present a small self-contained online course for raising awareness of some additional aspects of diversity, without admonishing users or prescribing attitudes.