Toward High Performance Computing Education

High Performance Computing (HPC) is the ability to process data and perform complex calculations at extremely high speeds. Current HPC platforms can achieve calculations on the order of quadrillions of calculations per second with quintillions on the horizon. The past three decades witnessed a vast increase in the use of HPC across different scientific, engineering and business communities, for example, sequencing the genome, predicting climate changes, designing modern aerodynamics, or establishing customer preferences. Although HPC has been well incorporated into science curricula such as bioinformatics, the same cannot be said for most computing programs. This working group will explore how HPC can make inroads into computer science education, from the undergraduate to postgraduate levels. The group will address research questions designed to investigate topics such as identifying and handling barriers that inhibit the adoption of HPC in educational environments, how to incorporate HPC into various curricula, and how HPC can be leveraged to enhance applied critical thinking and problem solving skills. Four deliverables include: (1) a catalog of core HPC educational concepts, (2) HPC curricula for contemporary computing needs, such as in artificial intelligence, cyberanalytics, data science and engineering, or internet of things, (3) possible infrastructures for implementing HPC coursework, and (4) HPC-related feedback to the CC2020 project

A knowledge perspective

Costa-Mendes, R., Cruz-Jesus, F., Oliveira, T., & Castelli, M. (2021). Machine learning bias in predicting high school grades: A knowledge perspective. Emerging Science Journal, 5(5), 576-597. https://doi.org/10.28991/esj-2021-01298This study focuses on the machine learning bias when predicting teacher grades. The experimental phase consists of predicting the student grades of 11th and 12thgrade Portuguese high school grades and computing the bias and variance decomposition. In the base implementation, only the academic achievement critical factors are considered. In the second implementation, the preceding year’s grade is appended as an input variable. The machine learning algorithms in use are random forest, support vector machine, and extreme boosting machine. The reasons behind the poor performance of the machine learning algorithms are either the input space poor preciseness or the lack of a sound record of student performance. We introduce the new concept of knowledge bias and a new predictive model classification. Precision education would reduce bias by providing low-bias intensive-knowledge models. To avoid bias, it is not necessary to add knowledge to the input space. Low-bias extensive-knowledge models are achievable simply by appending the student’s earlier performance record to the model. The low-bias intensive-knowledge learning models promoted by precision education are suited to designing new policies and actions toward academic attainments. If the aim is solely prediction, deciding for a low bias knowledge-extensive model can be appropriate and correct.publishersversionpublishe

A Competency-based Approach toward Curricular Guidelines for Information Technology Education

The Association for Computing Machinery and the IEEE Computer Society have launched a new report titled, Curriculum Guidelines for Baccalaureate Degree Programs in Information Technology (IT2017). This paper discusses significant aspects of the IT2017 report and focuses on competency-driven learning rather than delivery of knowledge in information technology (IT) programs. It also highlights an IT curricular framework that meets the growing demands of a changing technological world in the next decade. Specifically, the paper outlines ways by which baccalaureate IT programs might implement the IT curricular framework and prepare students with knowledge, skills, and dispositions to equip graduates with competencies that matter in the workplace. The paper suggests that a focus on competencies allows academic departments to forge collaborations with employers and engage students in professional practice experiences. It also shows how professionals and educators might use the report in reviewing, updating, and creating baccalaureate IT degree programs worldwide

Toward High-Performance Computing and Big Data Analytics Convergence: The Case of Spark-DIY

Convergence between high-performance computing (HPC) and big data analytics (BDA) is currently an established research area that has spawned new opportunities for unifying the platform layer and data abstractions in these ecosystems. This work presents an architectural model that enables the interoperability of established BDA and HPC execution models, reflecting the key design features that interest both the HPC and BDA communities, and including an abstract data collection and operational model that generates a unified interface for hybrid applications. This architecture can be implemented in different ways depending on the process- and data-centric platforms of choice and the mechanisms put in place to effectively meet the requirements of the architecture. The Spark-DIY platform is introduced in the paper as a prototype implementation of the architecture proposed. It preserves the interfaces and execution environment of the popular BDA platform Apache Spark, making it compatible with any Spark-based application and tool, while providing efficient communication and kernel execution via DIY, a powerful communication pattern library built on top of MPI. Later, Spark-DIY is analyzed in terms of performance by building a representative use case from the hydrogeology domain, EnKF-HGS. This application is a clear example of how current HPC simulations are evolving toward hybrid HPC-BDA applications, integrating HPC simulations within a BDA environment.This work was supported in part by the Spanish Ministry of Economy, Industry and Competitiveness under Grant TIN2016-79637-P(toward Unification of HPC and Big Data Paradigms), in part by the Spanish Ministry of Education under Grant FPU15/00422 TrainingProgram for Academic and Teaching Staff Grant, in part by the Advanced Scientific Computing Research, Office of Science, U.S.Department of Energy, under Contract DE-AC02-06CH11357, and in part by the DOE with under Agreement DE-DC000122495,Program Manager Laura Biven

Female Under-Representation in Computing Education and Industry - A Survey of Issues and Interventions

This survey paper examines the issue of female under-representation in computing education and industry, which has been shown from empirical studies to be a problem for over two decades. While various measures and intervention strategies have been implemented to increase the interest of girls in computing education and industry, the level of success has been discouraging. The primary contribution of this paper is to provide an analysis of the extensive research work in this area. It outlines the progressive decline in female representation in computing education. It also presents the key arguments that attempt to explain the decline and intervention strategies. We conclude that there is a need to further explore strategies that will encourage young female learners to interact more with computer educational games

Development and Evaluation of the Nebraska Assessment of Computing Knowledge

One way to increase the quality of computing education research is to increase the quality of the measurement tools that are available to researchers, especially measures of students’ knowledge and skills. This paper represents a step toward increasing the number of available thoroughly-evaluated tests that can be used in computing education research by evaluating the psychometric properties of a multiple-choice test designed to differentiate undergraduate students in terms of their mastery of foundational computing concepts. Classical test theory and item response theory analyses are reported and indicate that the test is a reliable, psychometrically-sound instrument suitable for research with undergraduate students. Limitations and the importance of using standardized measures of learning in education research are discussed

Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

Computer literacy in secondary education: The performance and engagement of girls

This research study examines performance and engagement in computer literacy of boys and girls (N = 873). Performance and engagement in computer literacy are established with CAST. Computer Alfabetisme Schalen Twente, a Dutch version of the Minnesota Computer Literacy Awareness Assessment. The results of the study show that girls perform lower and are less engaged in computer literacy than boys. Research on sex differences in mathematics and science education shows that three factors are important for the design of action programs for girls, viz. the expectation and behaviour of significant others, the perception of the usefulness of the subject for a future career and a positive attitude towards the subject. This study shows that these factors seem to be relevant for computer literacy too. It has been found that a positive attitude towards mathematics and physics is positively related to a positive attitude towards computer literacy. An examination of the relation between performance in computer literacy and attitude towards mathematics and physics shows no differences in performance between boys and girls with a negative attitude towards mathematics and physics. For boys and girls with a positive attitude towards mathematics and physics however a difference in performance in computer literacy has been found in favour of boy