Memory performance of and-parallel prolog on shared-memory architectures

The goal of the RAP-WAM AND-parallel Prolog abstract architecture is to provide inference speeds significantly beyond those of sequential systems, while supporting Prolog semantics and preserving sequential performance and storage efficiency. This paper presents simulation results supporting these claims with special emphasis on memory performance on a two-level sharedmemory multiprocessor organization. Several solutions to the cache coherency problem are analyzed. It is shown that RAP-WAM offers good locality and storage efficiency and that it can effectively take advantage of broadcast caches. It is argued that speeds in excess of 2 ML IPS on real applications exhibiting medium parallelism can be attained with current technology

Parallel processing and expert systems

Whether it be monitoring the thermal subsystem of Space Station Freedom, or controlling the navigation of the autonomous rover on Mars, NASA missions in the 1990s cannot enjoy an increased level of autonomy without the efficient implementation of expert systems. Merely increasing the computational speed of uniprocessors may not be able to guarantee that real-time demands are met for larger systems. Speedup via parallel processing must be pursued alongside the optimization of sequential implementations. Prototypes of parallel expert systems have been built at universities and industrial laboratories in the U.S. and Japan. The state-of-the-art research in progress related to parallel execution of expert systems is surveyed. The survey discusses multiprocessors for expert systems, parallel languages for symbolic computations, and mapping expert systems to multiprocessors. Results to date indicate that the parallelism achieved for these systems is small. The main reasons are (1) the body of knowledge applicable in any given situation and the amount of computation executed by each rule firing are small, (2) dividing the problem solving process into relatively independent partitions is difficult, and (3) implementation decisions that enable expert systems to be incrementally refined hamper compile-time optimization. In order to obtain greater speedups, data parallelism and application parallelism must be exploited

Parallel processing and expert systems

Whether it be monitoring the thermal subsystem of Space Station Freedom, or controlling the navigation of the autonomous rover on Mars, NASA missions in the 90's cannot enjoy an increased level of autonomy without the efficient use of expert systems. Merely increasing the computational speed of uniprocessors may not be able to guarantee that real time demands are met for large expert systems. Speed-up via parallel processing must be pursued alongside the optimization of sequential implementations. Prototypes of parallel expert systems have been built at universities and industrial labs in the U.S. and Japan. The state-of-the-art research in progress related to parallel execution of expert systems was surveyed. The survey is divided into three major sections: (1) multiprocessors for parallel expert systems; (2) parallel languages for symbolic computations; and (3) measurements of parallelism of expert system. Results to date indicate that the parallelism achieved for these systems is small. In order to obtain greater speed-ups, data parallelism and application parallelism must be exploited

The distributed computer system

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University

A study of the very high order natural user language (with AI capabilities) for the NASA space station common module

The requirements are identified for a very high order natural language to be used by crew members on board the Space Station. The hardware facilities, databases, realtime processes, and software support are discussed. The operations and capabilities that will be required in both normal (routine) and abnormal (nonroutine) situations are evaluated. A structure and syntax for an interface (front-end) language to satisfy the above requirements are recommended

Being everything to everyone: the lived experiences of first-generation college students and how colleges can better support them

Over the course of the past few decades, first-generation college students have been analyzed from many angles. With research ranging from quantitative reviews of lower graduation and retention rates, as well as higher attrition rates (Engle & Tinto, 2008; Inman & Mayes, 1999; Terenzini et al., 1996; Tinto, 1975), to qualitative case studies focusing on the psychological aspects of preparation, parental support, and identity formation (Lara, 1992; London, 1989; Rendón, 1992; Rodriguez, 1975 1982; Skinner & Richardson, 1988; Weis, 1985), this population has been well documented across a spectrum of research methodologies. More recently, scholarly attention has shifted toward a more individualized approach, focusing on smaller cohorts within the larger first-generation college student population (Collier & Morgan, 2008; Covarrubias et al., 2019; McCoy, 2014; Phinney & Haas, 2003). The goal of this three-article dissertation is to highlight and prioritize first-generation college students’ voices and narratives by emphasizing their lived experiences, as well as reviewing the support services currently available to them. This goal is addressed using three distinct, yet interconnected articles all utilizing different research methodologies. The first article, a phenomenological case study, addressed the experiences of six female first-generation college student caregivers (Orbe, 2004; Pyne & Means, 2013; Covarrubias et al., 2019) at a large, prestigious, research-driven institution in the Northeast. The second study, a singular, narrative case study, utilized the construct of intersectionality (Crenshaw, 1994; Pyne & Means, 2013) to examine the experiences of a female, first-generation college student caregiver of color as she navigated the higher education system. The last article, a comparative case study, examined the available first-gen support programming at three institutions in the same metropolitan area. This final study also included administrator perspectives about what is required to implement and execute first-generation college student support initiatives. The major implications of this dissertation project include the following: a strong recommendation for increased intersectionality in all first-gen support programming; a discovery of the causational relationship of being a first-gen caregiver and the added difficulty that multi-layered identity creates; a demonstration of the need to motivate and utilize collected student data in order to inform first-gen program creation; and a recognition of the stressors placed on certain campus stakeholders and the need for enhanced cross-campus collaboration to improve first-generation college student support. Future research and specific recommendations for the field of higher education are discussed

Analysis of Big Data Processing Using HDM Framework

MapReduce and Spark have been introduced to ease the task of developing big data programs and applications. However, the jobs in these frameworks are roughly defined and packaged as executable jars without any functionality being exposed or described. This means that deployed jobs are not natively composable and reusable for subsequent development. Besides, it also hampers the ability for applying optimizations on the data flow of job sequences and pipelines. The Hierarchically Distributed Data Matrix (HDM) which is a functional, strongly-typed data representation for writing composable big data applications. Along with HDM, a runtime framework is provided to support the execution, integration and management of HDM applications on distributed infrastructures. Based on the functional data dependency graph of HDM, multiple optimizations are applied to improve the performance of executing HDM jobs. The experimental results show that our optimizations can achieve improvements between 10% to 30% of the Job-Completion-Time and clustering time for different types of applications when compared

A Distributed Economics-based Infrastructure for Utility Computing

Existing attempts at utility computing revolve around two approaches. The first consists of proprietary solutions involving renting time on dedicated utility computing machines. The second requires the use of heavy, monolithic applications that are difficult to deploy, maintain, and use. We propose a distributed, community-oriented approach to utility computing. Our approach provides an infrastructure built on Web Services in which modular components are combined to create a seemingly simple, yet powerful system. The community-oriented nature generates an economic environment which results in fair transactions between consumers and providers of computing cycles while simultaneously encouraging improvements in the infrastructure of the computational grid itself.Comment: 8 pages, 1 figur