The industry shift to emerging forms of parallel and distributed computing (PDC), including multi-core CPUs, cloud computing, and general-purpose use of GPUs, have naturally led to increased presence of PDC elements undergraduate Computer Science curriculum recommendations, such as the new and substantial "PD" knowledge area in the ACM/IEEE CS2013 recommendations. How can undergraduate students grasp the extensive and complex range of PDC principles and practices, and apply that knowledge in problem solving, while PDC technologies continue to evolve rapidly? Parallel design patterns occur at all computational levels, ranging from low-level concurrent execution patterns (such as message passing or thread pool patterns) to high-level software design patterns suitable for organizing entire systems or their components (such as model-view-control or pipe and filter patterns). The ubiquity of these patterns in all forms of parallel and distributed computation makes parallel patterns relevant and illuminating at all undergraduate levels, and knowledge of those patterns can guide effective problem solving for parallel programs. This panel presents four viewpoints representing various approaches for teaching parallel patterns to CS undergraduates at various academic levels, including short "patternlets" examples, patterns in domain applications and parallel problem solving, and two tools, Seeds and Paraguin, for teaching parallel design patterns to undergraduates.

Parallel programming has been taught for many years but has recently taken on greater significance with introduction of multicore and many-core processors. Still, most teaching has followed a traditional path of focusing on learning low-level tools such as MPI. This workshop introduces a new educational approach based upon first selecting a computational pattern and then using higher-level tools to create parallel executable code. A pattern programming framework has been developed to create a distributed application that avoids the need to use low-level routines. The framework self-deploys on distributed computers, clusters, and individual multicore computers. Several patterns have been implemented including workpool, pipeline, synchronous and iterative all-to-all, and stencil. The first pattern covered is the widely applicable workpool pattern and other patterns will be introduced. The workshop will describe how the approach can be used in the undergraduate curriculum and will provide hands-on experiences to participants. In our approach, students focus upon computational strategies. Programming is much easier and less likely to be flawed. Each workshop participant will receive a flash drive with all software and documentation pre-loaded for use in the workshop and afterwards in their classes. Laptop required.

In this paper, we describe an approach for teaching parallel and distributed computing at the undergraduate level using computational patterns. The goal is to promote higher-level structured design for parallel programming and make parallel programming easier and more scalable. A pattern programming framework has been developed to create a distributed application that avoids the need to write code in low level message--passing APIs such as MPI. Several patterns have been implemented including workpool, pipeline, synchronous and iterative all-to-all, and stencil. We have redesigned a regular senior undergraduate parallel programming course to begin with a pattern strategy using our framework and provide a detailed syllabus around patterns.

In 2003, MIT Technology Review listed Grid computing as one of 'Ten Emerging Technologies That Will Change the World' [5]. Five years later, is Grid computing ready for the undergraduate classroom? In this panel, a group of educators share their experiences in teaching Grid computing over the past several years and in various settings, and discuss how the subject materials should be developed for the future. Key points under discussion include the place in the undergraduate curriculum, the role of programming exercises, bottom-up versus top-down approaches, and the necessary Grid computing platform. This panel will be of interest to those who teach the subject, and those who wish to introduce Grid computing into their programs. It will also interest those who do not want to offer a full Grid computing course but may wish to introduce Grid computing into existing distributed systems, networking, or parallel programming courses.

Early undergraduate Grid computing courses generally took a bottom-up approach to Grid computing education starting with network protocols, client-server concepts, creating Web and Grid services, and then progressing through the underlying Grid computing middleware, security mechanisms and job submission all using a Linux command-line interface. We describe a new approach to teaching Grid computing beginning with a production-style Grid portal, registration process, and job submission, and then leading into infrastructure details. We incorporate seven assignments, several of which require students to install Grid computing software on their own computer or lab computers rather than using centralized servers. Students complete a "capstone" mini-project. Typical projects included creating a JSR 168 portlet user interface for an application. We describe our experiences using this new course structure.

This paper describes the experiences of teaching a new undergraduate grid computing course to students across the State of North Carolina in Fall 2004. This course is specifically focused at the undergraduate level and has a large number of geographically distributed participating sites. New materials have been developed for undergraduate students. We have developed a coherent set of grid computing programming assignments. All the materials developed are available on-line. This course also had internationally known guest speakers to provide state-of-the art presentations. Apart from developing grid computing materials, a number of issues arose in using the grid computing software and coordinating the activities, which are described here.

Grid computing is now mature enough and important enough to be studied as a full course at the undergraduate level for upper-level computer science majors. We have developed such a course, including a set of lecture slides, assignments, and assignment handouts specifically targeted for this audience. The sequence of assignments is a key part of the course. Some of the assignments are modifications of pre-existing work and others are completely new. We describe the key decisions we made about the course organization and content and describe the assignments. An important feature of the course is that it was geographically distributed with copies of the grid software installed at three campuses. Those campuses plus three others were receiving sites and included students and faculty associated with nine universities.

This paper describes a course on distributed shared memory (DSM) programming that uses a cluster of workstations and freely available software. DSM progamming offers a unique challenge to the instructor as it is not yet well established. The ways of obtaining a DSM environment are still actively researched and many different approaches are being explored. Progammers using a DSM system require an understanding of the complex underlying methods used to create the illusion of shared memory. Intriguingly, DSM can also offer a mixture of both message passing and shared memory programming techniques and especially so if the cluster being used is a cluster of symmetric multiprocessors (SMPs). Our courses teach this new environment to advanced undergraduates or graduates. A detailed list of topics and techniques that a student needs to know for DSM programming are described. A home page has been developed providing online instructional materials.

In the above-titled paper by F. El Guibaly (see ibid., vol.38, p.161-71, (1989)), the (normalized) access time for various arbitration protocols was theoretically characterized. The commenter uses simulations to investigate single-bus multiprocessor arbitration protocols. Differences are found between the theoretical results and the simulation results. Theoretical formulae given by El Guibaly are corrected and modified to take into account that resubmitted requests increase the request rate. Closer agreement between theoretical and simulation results is then obtained.>

Multiprocessor interconnection networks are proposed which have the characteristic that each processor or memory module can connect to a group of its near neighbors and adjacent directly connected groups include some of the same processors or memories. These overlapping connectivity networks are attractive especially for a very large number of processors which cannot be provided with full connectivity but need to operate by communication between processors. Applications for overlapping connectivity networks include neural computers and dataflow computers. Bandwidth formulas are derived using a probabilistic approach, including when intermediate processors are used in the interconnection path. A general cell design is presented capable of representing various overlapping connectivity networks.>

Processor-memory multiprocessor interconnection networks called overlapping connectivity networks are investigated in this paper. Overlapping connectivity networks provide each processor with direct paths to a group of local memory modules and the groups of memory modules directly available to each processor overlap. The bandwidth of rhombic crossbar overlapping connectivity networks is compared to that of the full crossbar switch, multiple bus and multistage Delta networks under various ranges of requests using simulation. Multistage interconnection networks with overlapping connectivity are also presented with comparative results. We call these networks Sigma networks. Overall we show that overlapping connectivity networks can have superior performance compared with existing processor-memory interconnection networks. The networks also scale upwards well and are amenable to VLSI construction.