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Some Computer Science Issues In Ubiquitous Computing
Some computer science Issues in March 23, 1993 to appear in CACM, July 1993 different about the computer science in ubiquitous computing. various subdisciplines of computer science hardware components (e.g.
http://nano.xerox.com/hypertext/weiser/UbiCACM.html

Extractions: March 23, 1993 [to appear in CACM, July 1993] Ubiquitous computing is the method of enhancing computer use by making many computers available throughout the physical environment, but making them effectively invisible to the user. Since we started this work at Xerox PARC in 1988, a number of researchers around the world have begun to work in the ubiquitous computing framework. This paper explains what is new and different about the computer science in ubiquitous computing. It starts with a brief overview of ubiquitous computing, and then elaborates through a series of examples drawn from various subdisciplines of computer science: hardware components (e.g. chips), network protocols, interaction substrates (e.g. software for screens and pens), applications, privacy, and computational methods. Ubiquitous computing offers a framework for new and exciting research across the spectrum of computer science. The idea of ubiquitous computing first arose from contemplating the place of today's computer in actual activities of everyday life. In particular, anthropological studies of work life [Suchman 1985, Lave 1991] teach us that people primarily work in a world of shared situations and unexamined technological skills. However the computer today is isolated and isolating from the overall situation, and fails to get out of the way of the work. In other words, rather than being a tool through which we work, and so which disappears from our awareness, the computer too often remains the focus of attention. And this is true throughout the domain of personal computing as currently implemented and discussed for the future, whether one thinks of PC's, palmtops, or dynabooks. The characterization of the future computer as the "intimate computer" [Kay 1991], or "rather like a human assistant" [Tesler 1991] makes this attention to the machine itself particularly apparent.
University Of Maryland Computer Science Technical Reports
computer science laboratory offers technical reports on such topics as mining the Web for parallel strands. Department of computer science of the University Advanced computer Studies, Department of computer science, University computer Studies, Department of computer science, University of
http://www.cs.umd.edu/TRs/TR.html

Extractions: To see a listing without abstracts You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format. However, this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the Department of Computer Science of the University of Maryland at College Park under terms that include this permission. All other rights are reserved by the author(s). CS-TR-4170 CS-TR-4169 CS-TR-4168 CS-TR-4165 CS-TR-4164 Performance and Analysis of Saddle Point Preconditioners for the. Howard C. Elman. David J. Silvester. Andrew J. Wathen. July 2000. We examine the convergence characteristics of iterative methods based on a new preconditioning operator for solving the linear systems arising from discretization and linearization of the steady-state Navier-Stokes equations. With a combination of analytic and empirical results, we study the effects of fundamental parameters on convergence. We demonstrate that the preconditioned problem has an eigenvalue distribution consisting of a tightly clustered set together with a small number of outliers. The structure of these distributions is independent of the discretization mesh size, but the cardinality of the set of outliers increases slowly as the viscosity becomes smaller. These characteristics are directly correlated with the convergence properties of iterative solvers. (Also cross-refernced as UMIACS-TR-2000-54) University of Maryland Institute for Advanced Computer Studies, Department of Computer Science, University of Maryland
Computer Science
study of computer science may choose computer science 4 or The major in computer science is intended for work in computer science are computer science 5, computer science 15 or
http://www.dartmouth.edu/~reg/courses/cosc.html

Extractions: Registrar Home Page ORC Table of Contents Go directly to... African and African American Studies Asian and Middle Eastern Studies Asian and Middle Eastern Languages and Literatures Anthropology Art History Biochemistry Biological Sciences Chemistry Classics College Courses Comparative Literature Computer Science Earth Sciences Economics Education English Engineering Sciences Environmental Studies Program Film and Television Studies French and Italian Languages and Literatures Genetics Geography German Studies Government History Humanities Human Biology Jewish Studies Latin American, Latino and Caribbean Studies Linguistics and Cognitive Science Mathematics and Social Sciences Mathematics Microbiology and Immunology Music Native American Studies Program Public Policy Minor Pharmacology and Toxicology Philosophy Physiology Physics and Astronomy Psychological and Brain Sciences Religion Russian Language and Literature Studio Art Science Sociology Social Science Spanish and Portuguese Languages and Literatures Speech Theater Women's and Gender Studies Program Computer Science Chair: Robert L. Drysdale III
IIT - Computer Science Course Descriptions
s CS100 Introduction to Professions An introduction to science and engineering as a profession. Examines the problemsolving process used in engineering and science. program in computer science, computer Information Systems, or computer program in computer science, computer Information Systems, or computercomputer science Course
http://www.cs.iit.edu/courses/all_courses.html

Extractions: An introduction to science and engineering as a profession. Examines the problem-solving process used in engineering and science. Emphasizes the interdisciplinary and international nature of problem-solving and the need to evaluate solutions in terms of a variety of constraints: computational, financial, and social. (1-2-2) CS105 Introduction to Computer Programming I Introduction to the use of formal mathematical structures to represent problems and computational processes. Topics covered include Boolean algebra, first-order logic, recursive structures, graphs, and abstract language models. Prerequisite: CS 106 or CS 200. (3-0-3) CS331 Data Structures and Algorithms Implementation and application of the essential data structures used in computer science. Analysis of basic sorting and searching algorithms and their relationship to these data structures. Particular emphasis is given to the use of object-oriented design and data abstraction in the creation and application of data structures. Prerequisite: CS 106 or CS 200. (2-2-3) CS350 Computer Organization and Assembly Language Programming
MTU Department Of Computer Science
Rong Ge Department of computer science Michigan Technological University. Softwarepipelining is an excellent method for improving the parallelism in loops by
http://www.cs.mtu.edu/new/html/abstracts/rong_ge_proposal.html

Extractions: Michigan Technological University ABSTRACT Since the advent of ILP (Instruction-level Parallelism) processors, people have been trying to exploit the parallelism that exists at the operation level. Software pipelining is an excellent method for improving the parallelism in loops by overlapping operations from various loop iterations. However, software pipelining also brings a large register demand. Since the operations from different iterations are scheduled together, a value could need multiple copies to keep it live long enough to reach its last reference. In the case that the lifetime of a register is longer than the length of software pipelined loop body, multiple copies of this register are needed. The increase in register demand could be tremendous when a long schedule is wrapped and fitted into a small software pipelined body. If the register demand exceeds the number of registers available on the machine, some values have to be stored back to memory to make room for others. A better register pressure prediction scheme must be developed in order to take fully advantage of the benefit of software pipelining. Decreasing execution time is the goal of compiler optimization. To achieve this goal, memory operations are big concerns, especially in loops, because array operations appear in a loop very often. Failing to recognize memory reuse will cause a great number of memory accesses, thereby extending the loop's execution time. Scalar replacement, by replacing array variables with scalars, can effectively reduce the memory resource demand in innermost loop. However, for keeping the right semantics, an array variable could be replaced by more than one scalar variable. This can tremendously increase the register demand. So correctly estimating register pressure to avoid being over aggressive with scalar replacement becomes very important.
MTU Department Of Computer Science
Rong Ge Department of computer science Michigan Technological University. Softwarepipelining is an excellent compiler loop optimization technique to achieve
http://www.cs.mtu.edu/new/html/abstracts/rong_ge_defense.html

Extractions: Michigan Technological University ABSTRACT Since the advent of instruction level parallel processors, compiler techniques have worked together with multiple-instruction-issue hardware to exploit instruction level parallelism(ILP) in programs. Software pipelining is an excellent compiler loop optimization technique to achieve ILP by overlapping operations from various loop iterations. However, software pipelining incurs a large register demand and the number of registers required is hard to predict before actually software pipelining the code. One reason for difficulty in predicting register pressure is that we cannot determine in which cycle a register is defined or used before getting a real schedule. Another factor preventing the accurate prediction is determining the effects of high resource constraints that can stretch the lifetime of registers, thereby increasing register pressure. This research develops an algorithm to compute an approximate schedule that allows a more accurate prediction of register pressure than previous techniques. This approach creates a more accurate prediction because the approximate schedule is close to the real schedule except when the resource constraints become the main concern. In addition computing the approximate schedule is much less expensive than computing a real schedule. Based upon the approximate schedule, it is not hard to compute the number of live registers for each cycle. The maximum value is the predicted register pressure of a software pipelined loop. To handle tight resource constraints, two heuristic algorithms are developed to predict possible register demand based upon the average resource requirement.
Citations: Constrained Software Pipelining - Jones (ResearchIndex)
RB Jones. Constrained Software pipelining. Master's thesis, Department ofComputer science, Utah State University, Logan, UT, September 1991.
http://citeseer.nj.nec.com/context/692025/499252

Extractions: This paper is cited in the following contexts: M.R. O'Neill, V.H. Allan, N. Flann, and H. Chen - Department Of Computer (Correct) ....shown progressing horizontally. By delaying operation 3 for one cycle, a schedule of length 2 is produced. 4 Empirical Study The GA was applied to a set of DDGs, derived from a diversity of iterative code fagments. Each example has different resource constraints. These examples were taken from and a random DDG generator. The GA was compared to two heuristic methods: The Petri method using the all s chromosome (with no additional delays) and Lam s algorithm [Lam88] The Pertri net al..gorithm is compared to Lam s algorithm on 84 randomly chosen exam8 Table 2: Summary Statistics for ....

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