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

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

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

A model-driven approach to teaching concurrency

We present an undergraduate course on concurrent programming where formal models are used in different stages of the learning process. The main practical difference with other approaches lies in the fact that the ability to develop correct concurrent software relies on a systematic transformation of formal models of inter-process interaction (so called shared resources), rather than on the specific constructs of some programming language. Using a resource-centric rather than a language-centric approach has some benefits for both teachers and students. Besides the obvious advantage of being independent of the programming language, the models help in the early validation of concurrent software design, provide students and teachers with a lingua franca that greatly simplifies communication at the classroom and during supervision, and help in the automatic generation of tests for the practical assignments. This method has been in use, with slight variations, for some 15 years, surviving changes in the programming language and course length. In this article, we describe the components and structure of the current incarnation of the course?which uses Java as target language?and some tools used to support our method. We provide a detailed description of the different outcomes that the model-driven approach delivers (validation of the initial design, automatic generation of tests, and mechanical generation of code) from a teaching perspective. A critical discussion on the perceived advantages and risks of our approach follows, including some proposals on how these risks can be minimized. We include a statistical analysis to show that our method has a positive impact in the student ability to understand concurrency and to generate correct code

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

A systematic review of the literature on methods and technologies for teaching parallel and distributed computing in universities

There is a growing demand for software developers who have experience writing parallel programs rather than just "parallelizing" sequential systems as computer hardware gets more and more parallel. In order to develop the skills of future software engineers, it is crucial to teach pupils parallelism in elementary computer science courses. We searched the Scopus database for articles on "teaching parallel and distributed computing" and "parallel programming," published in English between 2008 and 2019. 26 papers were included in the study after quality review. As a result, a lab course using the C++ programming language and MPI library serves as the primary teaching tool for parallel and distributed computing

A Grid Portal for an Undergraduate Parallel Programming Course

[Abstract] This paper describes an experience of designing and implementing a portal to support transparent remote access to supercomputing facilities to students enrolled in an undergraduate parallel programming course. As these facilities are heterogeneous, are located at different sites, and belong to different institutions, grid computing technologies have been used to overcome these issues. The result is a grid portal based on a modular and easily extensible software architecture that provides a uniform and user-friendly interface for students to work on their programming laboratory assignments.Universidade da Coruña; UDC-TIC03-057Xunta de Galicia; PGIDIT02TIC00103CTXunta de Galicia; PGIDIT04TIC105004PREuropean Commission; IST-2001-3224

A systematic review of the literature on methods and technologies for teaching parallel and distributed computing in universities

There is a growing demand for software developers who have experience writing parallel programs rather than just" parallelizing" sequential systems as computer hardware gets more and more parallel. In order to develop the skills of future software engineers, it is crucial to teach pupils parallelism in elementary computer science courses. We searched the Scopus database for articles on" teaching parallel and distributed computing" and" parallel programming," published in English between 2008 and 2019. 26 papers were included in the study after quality review. As a result, a lab course using the C++ programming language and MPI library serves as the primary teaching tool for parallel and distributed computing

A learning experience toward the understanding of abstraction-level interactions in parallel applications

In the curriculum of a Computer Engineering program, concepts like parallelism, concurrency, consistency, or atomicity are usually addressed in separate courses due to their thoroughness and extension. Isolating such concepts in courses helps students not only to focus on specific aspects, but also to experience the reality of working with modern computer systems, where those concepts are often detached in different abstraction levels. However, due to such an isolation, it exists a risk of inducing to the students an absence of interactions between these concepts, and, by extension, between the different abstraction levels of a system. This paper proposes a learning experience showcasing the interactions between abstraction levels addressed in laboratory sessions of different courses. The driving example is a parallel ray tracer. In the different courses, students implement and assemble components of this application from the algorithmic level of the tracer to the assembly instructions required to guarantee atomicity. Each lab focuses on a single abstraction level, but shows students the interactions with the rest of the levels. Technical results and student learning outcomes through the analysis of surveys validate the proposed experience and confirm the students learning improvement with a more integrated view of the system