59 research outputs found
Load Balancing Regular Meshes on SMPS with MPI
Domain decomposition for regular meshes on parallel computers has
traditionally been performed by attempting to exactly partition the work among the available processors (now cores). However, these
strategies often do not consider the inherent system noise which can hinder MPI application scalability to emerging peta-scale machines
with 10000+ nodes. In this work, we suggest a solution that uses a tunable hybrid static/dynamic scheduling strategy that can be incorporated into current MPI implementations of mesh codes. By applying this strategy to a 3D jacobi algorithm, we achieve performance gains
of at least 16% for 64 SMP nodes
eLearning lecturer workload: Working smarter or working harder?
Lecturers who move into the online learning environment often discover that the workload involved not only changes, but can be overwhelming as they cope with using digital technologies. Questions arise, given the dissatisfaction of lecturers with lowering morale and increasing workload, whether future expansion of this teaching component in tertiary institutions is sustainable. The challenge facing lecturers now, and in the future, is about learning workload management strategies which effectively manage the workload they encounter in the online learning environment. This paper describes a case study (which is a work-in-progress) examining the perceptions of online workload cf. face-to-face teaching of lecturers who are experienced in e-teaching. As well, it identifies strategies the lecturers have developed or adopted to manage this element of their workload
An investigation into students' perceptions of group assignments
The collection of student feedback is a central strategy to monitor the effectiveness of teaching and learning at educational institutions (Meyer, 2010). This paper analyses the feedback and findings from a recent questionnaire survey of students' experience and perceptions of group work at the University of Bedfordshire at both undergraduate and postgraduate levels. The main objective of this study is to raise practical issues that teachers need to consider in designing and carrying out group assessments. This is aimed at overcoming the drawbacks, while amplifying the benefits, of group work, and improving students' engagement and performance in this type of assessment
Investigation of degradation and upgradation models for flexible unit systems: a systematic literature review
Research on flexible unit systems (FUS) with the context of descriptive, predictive, and prescriptive analysis have remarkably progressed in recent times, being now reinforced in the current Industry 4.0 era with the increased focus on integration of distributed and digitalized systems. In the existing literature, most of the work focused on the individual contributions of the above mentioned three analyses. Moreover, the current literature is unclear with respect to the integration of degradation and upgradation models for FUS. In this paper, a systematic literature review on degradation, residual life distribution, workload adjustment strategy, upgradation, and predictive maintenance as major performance measures to investigate the performance of the FUS has been considered. In order to identify the key issues and research gaps in the existing literature, the 59 most relevant papers from 2009 to 2020 have been sorted and analyzed. Finally, we identify promising research opportunities that could expand the scope and depth of FUS.The project is funded by the Department of Science and Technology, Science & Engineering
Research Board (DST-SERB), Statutory Body Established through an Act of Parliament: SERB Act
2008, Government of India with Sanction Order No ECR/2016/001808, and also by FCTâFundação
para a CiĂȘncia e Tecnologia through the R&D Units Project Scope: UIDB/00319/2020
Effects of organizational structure on performance : experimental results
Cover title.Includes bibliographical references (p. 9).Research supported by the Office of Naval Research. N00014-84-K-0519 Research supported by the Basic Research Group of the Joint Directors of Laboratory through the Office of Naval Research. N00014-85-K-0782by Victoria Y. Jin, Alexander H. Levis
Alignment-free Genomic Analysis via a Big Data Spark Platform
Motivation: Alignment-free distance and similarity functions (AF functions,
for short) are a well established alternative to two and multiple sequence
alignments for many genomic, metagenomic and epigenomic tasks. Due to
data-intensive applications, the computation of AF functions is a Big Data
problem, with the recent Literature indicating that the development of fast and
scalable algorithms computing AF functions is a high-priority task. Somewhat
surprisingly, despite the increasing popularity of Big Data technologies in
Computational Biology, the development of a Big Data platform for those tasks
has not been pursued, possibly due to its complexity. Results: We fill this
important gap by introducing FADE, the first extensible, efficient and scalable
Spark platform for Alignment-free genomic analysis. It supports natively
eighteen of the best performing AF functions coming out of a recent hallmark
benchmarking study. FADE development and potential impact comprises novel
aspects of interest. Namely, (a) a considerable effort of distributed
algorithms, the most tangible result being a much faster execution time of
reference methods like MASH and FSWM; (b) a software design that makes FADE
user-friendly and easily extendable by Spark non-specialists; (c) its ability
to support data- and compute-intensive tasks. About this, we provide a novel
and much needed analysis of how informative and robust AF functions are, in
terms of the statistical significance of their output. Our findings naturally
extend the ones of the highly regarded benchmarking study, since the functions
that can really be used are reduced to a handful of the eighteen included in
FADE
Overload depending on driving experience and situation complexity: which strategies faced with a pedestrian crossing?
The purpose of this study was to identify the influence of situation complexity and driving experience on subjective workload and driving performance, and the less costly and the most effective strategies faced with a hazard pedestrian crossing. Four groups of young drivers (15 traditionally trained novices, 12 early-trained novices, 15 with three years of experience and 15 with a minimum of five years of experience) were randomly assigned to three situations (simple, moderately complex and very complex) including unexpected pedestrian crossings, in a driving simulator. The subjective workload was collected by the NASA-TLX questionnaire after each situation. The main results confirmed that the situation complexity and the lack of experience increased the subjective workload. Moreover, the subjective workload, the avoidance strategies and the reaction times influenced the number of collisions depending on situation complexity and driving experience. These results must be taken into account to target the prevention actions
Collaborative Networks, Decision Systems, Web Applications and Services for Supporting Engineering and Production Management
This book focused on fundamental and applied research on collaborative and intelligent networks and decision systems and services for supporting engineering and production management, along with other kinds of problems and services. The development and application of innovative collaborative approaches and systems are of primer importance currently, in Industry 4.0. Special attention is given to flexible and cyber-physical systems, and advanced design, manufacturing and management, based on artificial intelligence approaches and practices, among others, including social systems and services
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