Robert Collier

Using video game development to engage undergraduate students of assembly language programming

It is widely accepted that the instruction of programming in assembly language is often a challenging and frustrating experience, both to educators and undergraduate students. Although little can be done to simplify the curriculum, it is absolutely crucial that frustration not compel students to abandon the subject. Our use of game development in a second-year course affords a unique opportunity to present this complex subject, without omission, in such a way as to create an experience that most students find entertaining. The results of a class survey indicated that 65% of participants agree or strongly agree that the experience was enjoyable (with only 11% in disagreement). We conclude that this ensures a sufficiently engaging experience that offsets the tedium inherent to the subject. The consensus of most students was that the complexity of video game design does not detract from their enjoyment of the course and contrarily has a positive impact on their learning overall. This position is supported by additional survey results.

A formal and empirical analysis of recombination for genetic algorithm-based approaches to the FPGA placement problem

To reduce the compilation times for Field Programmable Gate Arrays, genetic algorithms have been proposed for performing placement. However, the quality of solutions produced by these methods, so far, has been inferior to that produced by other search methods. In this paper, we show how traditional recombination operators, employed by the genetic algorithm when performing placement, fail to produce offspring solutions that are confined to the solution subspace defined by the parent solutions. This violates a fundamental principle that should govern the behavior of the recombination operator. We explore this flaw in detail, and propose a novel recombination operator that yields very statistically significant performance improvements, when tested with standard benchmarks.

GPU Approach to FPGA placement based on star

While simulated-annealing is currently the most widely used method for performing FPGA placement, it does not scale to very large designs. Modern many-core architectures (including GPUs) offer a promising alternative to traditional multi-core processors for improving runtime performance. In this work, we propose a GPU-accelerated simulated-annealing variant for FPGA placement. Our approach uses the Star+ wirelength model along with a novel method of efficiently generating large sets of independent swap operations, providing a high level of parallelism. Speedups from 5.4-89.2× (median 20.2×) were achieved over a single-core CPU-only implementation.

Depictions of genotypic space for evaluating the suitability of different recombination operators

When the genetic algorithm recombines two parent genotypes, the differences between them define a genotypic subspace, and any offspring produced should be confined to this subspace. Although this might seem insignificant, those recombination (or crossover) operators that violate this principle can direct a search away from the region (in genotypic space) that contains the two parent genotypes. This is contrary to the task for which the recombination operator was originally developed and can be detrimental, so this paper introduces a visualization that can be used to detect violations of this principle. The methodology also inspired the development of a different approach to recombining permutations, and a brief case study shows that an alternative recombination operator that does not violate this principle can be used to achieve a performance improvement over previous attempts to optimize Field-Programmable Gate-Array placements using a genetic algorithm. We believe that this technique will be invaluable for developing additional recombination operators.

Approaches to multidimensional scaling for adaptive landscape visualization

The adaptive landscape has become a standard approach for genetic algorithm visualization, and the representation of the higher dimensional chromosome space onto a two-dimensional plane suitable for the construction of an adaptive landscape requires an accurate measurement of the distance between chromosomes. Although the shortcomings of traditional approaches to adaptive landscape construction are by no means unknown to the research community, the intuitions afforded by this visualization have kept it in widespread usage. Since the multidimensional scaling required for the creation of a representative landscape is often disregarded to avoid the computational overhead required, this paper demonstrated that distance measures are available that remain representative of the genetic operators of the genetic algorithm while being suitable for multidimensional scaling techniques. This paper also demonstrated that in spite of the complications expected when the distance between chromosomes is measured with respect to both a unary mutation operation and a binary recombination operation simultaneously, it is possible to construct adaptive landscapes that depict features indicative of the effects of both genetic operators.

Improving Student Content Retention using a Classroom Response System.

The most typical uses of a classroom response system are to improve student engagement and to provide opportunities for immediate feedback. For our introductory course in computer science we sought to investigate whether the content and format typically associated with a classroom response system could be adapted from a feedback tool into an approach for improving content retention. We devised an experiment wherein different sections would be presented with complementary sets of questions presented either immediately after the corresponding material (i.e., for feedback) or at the beginning of the following lecture, with the express purpose of reminding and reinforcing material (i.e., to improve content retention). In every case, the participants that encountered an item in the following lecture exhibited relatively better performance on the corresponding items of the final exam. Thus our evidence supports the hypothesis that, with no significant additional investment of preparation or lecture time (beyond that associated with all classroom response systems), questions can be presented in such a way as to engage students while simultaneously improving content retention.

Advancing genetic algorithm approaches to field programmable gate array placement with enhanced recombination operators

Abstract Since their inception, field programmable gate arrays have seen an enormous growth in usage because they can dramatically reduce design and manufacturing costs. However, the time required for placement (a key step in the design) is dominating the compilation process. In this paper, we take some initial theoretical steps towards developing an efficient genetic algorithm for solving the placement problem by developing suitable recombination operators for performing placement. According to Holland, when the genetic algorithm recombines two parent genotypes, the differences between them define a genotypic subspace, and any offspring produced should be confined to this subspace. Those recombination operators that violate this principle can direct a search away from the region containing the parent genotypes and this is contrary to the intended task for recombination. This is often detrimental to search performance. This paper contributes the development of an intuitive visualization technique that can be used to easily detect violations of the previous principle. The efficacy of the proposed methodology is demonstrated and it is demonstrated that many standard recombination operators violate this principle. The methodology is then used to guide the development of novel operators that exhibit substantial (and statistically significant) improvements in performance over standard recombination operators.

An impulse-c hardware accelerator for packet classification based on fine/coarse grain optimization

Current software-based packet classification algorithms exhibit relatively poor performance, prompting many researchers to concentrate on novel frameworks and architectures that employ both hardware and software components. The Packet Classification with Incremental Update (PCIU) algorithm, Ahmed et al. (2010), is a novel and efficient packet classification algorithm with a unique incremental update capability that demonstrated excellent results and was shown to be scalable formany different tasks and clients. While a pure software implementation can generate powerful results on a server machine, an embedded solution may be more desirable for some applications and clients. Embedded, specialized hardware accelerator based solutions are typically much more efficient in speed, cost, and size than solutions that are implemented on general-purpose processor systems. This paper seeks to explore the design space of translating the PCIU algorithm into hardware by utilizing several optimization techniques, ranging from fine grain to coarse grain and parallel coarse grain approaches. The paper presents a detailed implementation of a hardware accelerator of the PCIU based on an Electronic System Level (ESL) approach. Results obtained indicate that the hardware accelerator achieves on average 27x speedup over a state-of-the-art Xeon processor.

The problems with counting ancestors in a simple genetic algorithm

The ease with which the genetic algorithm can generate good solutions to challenging optimization problems has resulted in a tendency for researchers to overlook the easily gathered and largely untapped raw data present in the ancestral relationships that guide a population to convergence. This article introduces the notion of a lineage tree structure and associated ancestor count measure that reveals unexpected regularities when studying instances of a simple genetic algorithm applied to three disparate problems. Consequently, a series of explanatory models was constructed to identify the components of the underlying evolutionary mechanism that may be responsible for the commonalities observed. These components (exponential growth of member parentage and inbreeding caused by the appearance of duplicates and shared ancestries) place constraints on the number of ancestors that a solution may have; an insight that may prove valuable for future analysis of the behavior of the genetic algorithm.

Lessons learned and recommended strategies for game development components in a computer literacy course

The challenges that instructors face attempting to motivate novice programming students are amplified when the students are not pursuing degrees or careers in computer science. For the programming module of our course for non-computer science majors we assigned a video game programming deliverable that we expected would engage students and enhance their experiences. After extensive analyses of the survey responses of 245 enrolled students we were surprised to learn that, although the majority believed the game programming experience enhanced their learning overall, another majority reported that the project itself was not enjoyable. Through qualitative analysis we have identified several key areas that seem to have detracted from the overall level of enjoyment, and in this paper we follow this investigation with discussion surrounding how these issues could be remedied in the future. These recommended strategies will bolster student enjoyment and motivation in future offerings and we believe this discussion will prove very useful to other instructors planning to employ game programming components.