Highly parallel computation

Highly parallel computing architectures are the only means to achieve the computation rates demanded by advanced scientific problems. A decade of research has demonstrated the feasibility of such machines and current research focuses on which architectures designated as multiple instruction multiple datastream (MIMD) and single instruction multiple datastream (SIMD) have produced the best results to date; neither shows a decisive advantage for most near-homogeneous scientific problems. For scientific problems with many dissimilar parts, more speculative architectures such as neural networks or data flow may be needed

Modeling a Secured Digital Image Encryption Scheme Using a Three Moduli Set

This paper proposes a new digital image coding scheme that uses a three moduli set with a common factor. The proposed scheme is specific to a particular three moduli set {2n+2, 2n+1,2n}. The design of the scheme is based on the residue to binary converter which achieves in terms of area and critical path delay as compared to the state of the art. This scheme offers high-speed processing because in the reverse converter the computation of the multiplicative inverse is eliminated, and it achieves low-power VLSI implementation for image processing such as digital image transform and digital image filtering

Cluster Computing in the Classroom: Topics, Guidelines, and Experiences

With the progress of research on cluster computing, more and more universities have begun to offer various courses covering cluster computing. A wide variety of content can be taught in these courses. Because of this, a difficulty that arises is the selection of appropriate course material. The selection is complicated by the fact that some content in cluster computing is also covered by other courses such as operating systems, networking, or computer architecture. In addition, the background of students enrolled in cluster computing courses varies. These aspects of cluster computing make the development of good course material difficult. Combining our experiences in teaching cluster computing in several universities in the USA and Australia and conducting tutorials at many international conferences all over the world, we present prospective topics in cluster computing along with a wide variety of information sources (books, software, and materials on the web) from which instructors can choose. The course material described includes system architecture, parallel programming, algorithms, and applications. Instructors are advised to choose selected units in each of the topical areas and develop their own syllabus to meet course objectives. For example, a full course can be taught on system architecture for core computer science students. Or, a course on parallel programming could contain a brief coverage of system architecture and then devote the majority of time to programming methods. Other combinations are also possible. We share our experiences in teaching cluster computing and the topics we have chosen depending on course objectives

Suffolk University Academic Catalog, School of Management--supplement, 1981-1982

This catalog is a supplement that supercedes information contained in 1981-1982 SBS cataloghttps://dc.suffolk.edu/cassbs-catalogs/1072/thumbnail.jp

Computer Science 2019 APR Self-Study & Documents

UNM Computer Science APR self-study report and review team report for Spring 2019, fulfilling requirements of the Higher Learning Commission