Pervasive Parallel And Distributed Computing In A Liberal Arts College Curriculum

We present a model for incorporating parallel and distributed computing (PDC) throughout an undergraduate CS curriculum. Our curriculum is designed to introduce students early to parallel and distributed computing topics and to expose students to these topics repeatedly in the context of a wide variety of CS courses. The key to our approach is the development of a required intermediate-level course that serves as a introduction to computer systems and parallel computing. It serves as a requirement for every CS major and minor and is a prerequisite to upper-level courses that expand on parallel and distributed computing topics in different contexts. With the addition of this new course, we are able to easily make room in upper-level courses to add and expand parallel and distributed computing topics. The goal of our curricular design is to ensure that every graduating CS major has exposure to parallel and distributed computing, with both a breadth and depth of coverage. Our curriculum is particularly designed for the constraints of a small liberal arts college, however, much of its ideas and its design are applicable to any undergraduate CS curriculum

Teaching Concurrent Software Design: A Case Study Using Android

In this article, we explore various parallel and distributed computing topics from a user-centric software engineering perspective. Specifically, in the context of mobile application development, we study the basic building blocks of interactive applications in the form of events, timers, and asynchronous activities, along with related software modeling, architecture, and design topics.Comment: Submitted to CDER NSF/IEEE-TCPP Curriculum Initiative on Parallel and Distributed Computing - Core Topics for Undergraduate

Teaching Parallel Computing to Freshmen

Parallelism is the future of computing and computer science and should therefore be at the heart of the CS curriculum. Instead of continuing along the evolutionary path by introducing parallel computation “top down” (first in special junior-senior level courses), we are taking a radical approach and introducing parallelism at the earliest possible stages of instruction. Specifically, we are developing a completely new freshman-level course on data structures that integrates parallel computation naturally, and retains the emphasis on laboratory instruction. This will help to steer our curriculum as expeditiously as possible toward parallel computing. Our approach is novel in three distinct and essential ways. First, we will teach parallel computing to freshmen in a course designed from beginning to end to do so. Second, we will motivate the course with examples from scientific computation. Third, we use multimedia and visualization as instructional aids. We have two primary objectives: to begin a reform of our undergraduate curriculum with an laboratory-based freshman course on parallel computation, and to produce tools and methodologies that improve student understanding of the basic principles of parallel computing. Parallelism is the future of computing and computer science and should therefore be at the heart of the CS curriculum. Instead of continuing along the evolutionary path by introducing parallel computation “top down” (first in special junior-senior level courses), we are taking a radical approach and introducing parallelism at the earliest possible stages of instruction. Specifically, we are developing a completely new freshman-level course on data structures that integrates parallel computation naturally, and retains the emphasis on laboratory instruction. This will help to steer our curriculum as expeditiously as possible toward parallel computing. Our approach is novel in three distinct and essential ways. First, we will teach parallel computing to freshmen in a course designed from beginning to end to do so. Second, we will motivate the course with examples from scientific computation. Third, we use multimedia and visualization as instructional aids. We have two primary objectives: to begin a reform of our undergraduate curriculum with an laboratory-based freshman course on parallel computation, and to produce tools and methodologies that improve student understanding of the basic principles of parallel computing

Cross Teaching Parallelism and Ray Tracing: A Project-based Approach to Teaching Applied Parallel Computing

Massively parallel Graphics Processing Unit (GPU) hardware has become increasingly powerful, available and affordable. Software tools have also advanced to the point that programmers can write general purpose parallel programs that take advantage of the large number of compute cores available in the hardware. With literally hundreds of compute cores available on a single device, program performance can increase by orders of magnitude. We believe that introducing students to the concepts of parallel programming for massively parallel hardware is of increasing importance in an undergraduate computer science curriculum. Furthermore, we believe that students learn best when given projects that reflect real problems in computer science. This paper describes the experience of integrating two undergraduate computer science courses to enhance student learning in parallel computing concepts. In this cross teaching experience we structured the integration of the courses such that students studying parallel computing worked with students studying advanced rendering for approximately 30% of the quarter long courses. Working in teams on a joint project, both groups of students were able to see the application of parallelization to an existing software project with both the benefits and complications exposed early in the curriculum of both courses. Motivating projects and performance gains are discussed, as well as student survey data on the effectiveness of the learning outcomes. Both performance and survey data indicate a positive gain from the cross teaching experience

Incorporating the NSF/TCPP Curriculum Recommendations in a Liberal Arts Setting

This paper examines the integration of the NSF/TCPP Core Curriculum Recommendations in a liberal arts undergraduate setting. We examine how parallel and distributed computing concepts can be incorporated across the breadth of the undergraduate curriculum. As a model of such an integration, changes are proposed to Data Structures and Design and Analysis of Algorithms. These changes were implemented in Design and Analysis of Algorithms and the results were compared to previous iterations of that course taught by the same instructor. The student feedback received shows that the introduction of these topics made the course more engaging and conveyed an adequate introduction to this material

Integrating Open Source GIS Software in Undergraduate Curriculum, Research, and Outreach - Recent Experiences at Salisbury University

The Department of Geography and Geosciences at Salisbury University has a long tradition of teaching geographic information science. Until recently, most of the courses and research activities focused on commercial software offerings. However, the Department recently integrated Free and Open Source Software for GIS (FOSSG) into its curriculum, research, and outreach. Curriculum changes included introducing students to FOSSG in traditional GIS courses using QGIS, and creating two entirely new courses in Enterprise GIS and GIS Programming using PostGIS, GDAL, and Spatial Lite. Through a competitive National Science Foundation (NSF) Research Experience for Undergraduates grant (REU), students participated in cutting edge research projects in parallel processing with Hadoop and spatial Hadoop for cluster computing, and CUDA for GPGPU calculation on embarrassingly parallel processes for raster data. Finally, undergraduate interns working in the Department\u27s Eastern Shore Regional GIS Cooperative (ESRGC) developed geodashboards using node.js, PostGIS, and Leaflet, while a special topics course developed a GIS based iphone and Android application used by 4,000 participants in the annual Sea Gull Century bike ride using GeoJSON, Leaflet, and javascript. In addition to highlighting the successes of these activities, this paper will discuss the process we used to make the necessary changes in our curriculum, secure the necessary funding for external projects, and the training approach we used to get our computer science students proficient in programming with FOSSG tools

Modeling Women's Elective Choices in Computing

Evidence-based strategies suggest ways to reduce the gender gap in computing. For example, elective classes are valuable in enabling students to choose in which directions to expand their computing knowledge in areas aligned with their interests. The availability of electives of interest may also make computing programs of study more meaningful to women. However, research on which elective computing topics are more appealing to women is often class or institution specific. In this study, we investigate differences in enrollment within undergraduate-level elective classes in computing to study differences between women and men. The study combined data from nine institutions from both Western Europe and North America and included 272 different classes with 49,710 student enrollments. These classes were encoded using ACM curriculum guidelines and combined with the enrollment data to build a hierarchical statistical model of factors affecting student choice. Our model shows which elective topics are less popular with all students (including fundamentals of programming languages and parallel and distributed computing), and which elective topics are more popular with women students (including mathematical and statistical foundations, human computer interaction and society, ethics, and professionalism). Understanding which classes appeal to different students can help departments gain insight of student choices and develop programs accordingly. Additionally, these choices can also help departments explore whether some students are less likely to choose certain classes than others, indicating potential barriers to participation in computing

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

Representative Names of Computing Degree Programs Worldwide

Through the auspices of ACM and with support from the IEEE Computer Society, a task group charged to prepare the IT2017 report conducted an online international survey of computing faculty members about their undergraduate degree programs in computing. The purpose of this survey was to clarify the breadth of and disparities in nomenclature used by diverse communities in the computing field, where a word or phrase can mean different things in different computing communities. This paper examines the English-language words and phrases used to name the computing programs of almost six hundred survey respondents, and the countries in which those names are used. Over eight hundred program names analysed in this paper reveal six program names that together account for more than half of all program names. The paper goes on to consider possible correspondence between reported program names and the five areas of computing identified by the ACM. Names such as computer science and information technology appear to dominate, but with different meanings, while the names of other computing disciplines show clear geographic preferences. Convergence towards a very small number of highly representative program names in computing education worldwide might be deceptive. The paper calls for further examination and international collaborations to align program names with program curriculum content