Previous research argues that liberal arts computer science programs are defining new directions for curricula based on distinctive features of their institutional and/or departmental identity. These innovations, and the approach to curricular design that inform them, can inspire curricular innovation at large universities, community colleges, and K-12 institutions. This panel illustrates an approach to curriculum design based on institutional and/or departmental identity with specific examples from four programs. It will guide audience members to consider distinctive features of their program context and how that can influence their curricula.

Across the US, plummeting CS enrollments and disappointing student retention have caused serious concern in recent years. Yet, in the Spring of 2007, the Berea College faculty unanimously adopted a CIS major in response to the rise in enrollment in CS0 and retention into CS1. This paper details how using a funnel approach to attract students into the discipline via multiple CS0-level courses resulted in the exception to the trend.

The availability of inexpensive "off the shelf" machines increases the likelihood that parallel programs run on heterogeneous clusters of machines. These programs are increasingly likely to be out of core, meaning that portions of their datasets must be stored on disk during program execution. This results in significant, per-iteration, I/O cost. This paper describes an execution model, called MHETA, which is the key component to finding an effective data distribution on heterogeneous clusters. MHETA takes into account computation, communication, and I/O costs of iterative scientific applications. MHETA uses automatically extracted information from a single iteration to predict the execution time of the remaining iterations. Results show that MHETA predicts with on average 98% accuracy the execution time of several scientific benchmarks (with and without prefetching) and one full-scale scientific program that utilize pipelined and other communication. MHETA is thus an effective tool when searching for the most effective distribution on a heterogeneous cluster.

Distributing data is a fundamental problem in implementing efficient distributed-memory parallel programs. The problem becomes more difficult in environments where the participating nodes are not dedicated to a parallel application. We are investigating the data distribution problem in non dedicated environments in the context of explicit message-passing programs. To address this problem, we have designed and implemented an extension to MPI called Dynamic MPI (Dyn-MPI). The key component of Dyn-MPI is its run-time system, which efficiently and automatically redistributes data on the fly when there are changes in the application or the underlying environment. Dyn-MPI supports efficient memory allocation, precise measurement of system load and computation time, and node removal. Performance results show that programs that use Dyn-MPI execute efficiently in non dedicated environments, including up to almost a three-fold improvement compared to programs that do not redistribute data and a 25% improvement over standard adaptive load balancing techniques.