The computer science graduate record exam tutorial courseware, 1994

The design and development of Computer Science Graduate Record Examination Tutorial Software will be discussed. The courseware reviews Computer Design, File Structures, Data Structures, and Discrete Math to thoroughly prepare students for the exam. A demonstration of the software is included on diskette

Structured P2P Technologies for Distributed Command and Control

The utility of Peer-to-Peer (P2P) systems extends far beyond traditional file sharing. This paper provides an overview of how P2P systems are capable of providing robust command and control for Distributed Multi-Agent Systems (DMASs). Specifically, this article presents the evolution of P2P architectures to date by discussing supporting technologies and applicability of each generation of P2P systems. It provides a detailed survey of fundamental design approaches found in modern large-scale P2P systems highlighting design considerations for building and deploying scalable P2P applications. The survey includes unstructured P2P systems, content retrieval systems, communications structured P2P systems, flat structured P2P systems and finally Hierarchical Peer-to-Peer (HP2P) overlays. It concludes with a presentation of design tradeoffs and opportunities for future research into P2P overlay systems

Data structures

We discuss data structures and their methods of analysis. In particular, we treat the unweighted and weighted dictionary problem, self-organizing data structures, persistent data structures, the union-find-split problem, priority queues, the nearest common ancestor problem, the selection and merging problem, and dynamization techniques. The methods of analysis are worst, average and amortized case

Praktické datové struktury

V této práci implementujeme datové struktury pro uspořádané a neuspořádané slovníky a měříme jejich výkon v hlavní paměti pomocí syntetických i praktických experimentů. Náš průzkum zahrnuje jak obvyklé datové struktury (B-stromy, červeno-černé stromy, splay stromy a hashování), tak exotičtější přístupy (k-splay stromy a k-lesy). Powered by TCPDF (www.tcpdf.org)In this thesis, we implement several data structures for ordered and unordered dictionaries and we benchmark their performance in main memory on synthetic and practical workloads. Our survey includes both well-known data structures (B-trees, red-black trees, splay trees and hashing) and more exotic approaches (k-splay trees and k-forests). Powered by TCPDF (www.tcpdf.org)Department of Applied MathematicsKatedra aplikované matematikyMatematicko-fyzikální fakultaFaculty of Mathematics and Physic

Data Structures & Algorithm Analysis in C++

This is the textbook for CSIS 215 at Liberty University.https://digitalcommons.liberty.edu/textbooks/1005/thumbnail.jp

Automating abstraction functions

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 109-119).Data abstraction has been the dominant structuring paradigm for programs for decades. The essence of a data abstraction is the abstraction function, which relates the concrete program representation to its abstract meaning. However, abstraction functions are not generally considered to be a part of the executing program. We propose that making abstraction functions an executable part of the program can enable programmers to write clearer and more concise programs with fewer errors. In particular, we show that the object equality and hashing operations (which programmers are required to write), can often be expressed more clearly and more concisely in terms of the abstract state of the object. Getting these methods right has proven to be difficult for programmers at all skill levels, from novice through expert. In a case study of the standard Java libraries we show that rewriting the code with explicit declarative abstraction functions (and generating equality and hashing methods automatically) removed object-contract compliance faults previously found by Pacheco et al. To make abstraction functions part of the executing program we develop four techniques for the dynamic evaluation of abstraction functions written in a declarative first-order logic with relations and transitive closure. We observe that the abstraction functions programmers write in practice may often be viewed as navigation queries on the heap, and two of our techniques exploit this insight to synthesize executable code from declarative abstraction functions. The performance of our research prototype is within striking distance of hand-written code.by Derek F. Rayside.Ph.D

An Open Guide to Data Structures and Algorithms

This textbook serves as a gentle introduction for undergraduates to theoretical concepts in data structures and algorithms in computer science while providing coverage of practical implementation (coding) issues. The field of computer science (CS) supports a multitude of essential technologies in science, engineering, and communication as a social medium. The varied and interconnected nature of computer technology permeates countless career paths making CS a popular and growing major program. Mastery of the science behind computer science relies on an understanding of the theory of algorithms and data structures. These concepts underlie the fundamental tradeoffs that dictate performance in terms of speed, memory usage, and programming complexity that separate novice programmers from professional practitioners

Efficient similarity computations on parallel machines using data shaping

Similarity computation is a fundamental operation in all forms of data. Big Data is, typically, characterized by attributes such as volume, velocity, variety, veracity, etc. In general, Big Data variety appears as structured, semi-structured or unstructured forms. The volume of Big Data in general, and semi-structured data in particular, is increasing at a phenomenal rate. Big Data phenomenon is posing new set of challenges to similarity computation problems occurring in semi-structured data. Technology and processor architecture trends suggest very strongly that future processors shall have ten\u27s of thousands of cores (hardware threads). Another crucial trend is that ratio between on-chip and off-chip memory to core counts is decreasing. State-of-the-art parallel computing platforms such as General Purpose Graphics Processors (GPUs) and MICs are promising for high performance as well high throughput computing. However, processing semi-structured component of Big Data efficiently using parallel computing systems (e.g. GPUs) is challenging. Reason being most of the emerging platforms (e.g. GPUs) are organized as Single Instruction Multiple Thread/Data machines which are highly structured, where several cores (streaming processors) operate in lock-step manner, or they require a high degree of task-level parallelism. We argue that effective and efficient solutions to key similarity computation problems need to operate in a synergistic manner with the underlying computing hardware. Moreover, semi-structured form input data needs to be shaped or reorganized with the goal to exploit the enormous computing power of \textit{state-of-the-art} highly threaded architectures such as GPUs. For example, shaping input data (via encoding) with minimal data-dependence can facilitate flexible and concurrent computations on high throughput accelerators/co-processors such as GPU, MIC, etc. We consider various instances of traditional and futuristic problems occurring in intersection of semi-structured data and data analytics. Preprocessing is an operation common at initial stages of data processing pipelines. Typically, the preprocessing involves operations such as data extraction, data selection, etc. In context of semi-structured data, twig filtering is used in identifying (and extracting) data of interest. Duplicate detection and record linkage operations are useful in preprocessing tasks such as data cleaning, data fusion, and also useful in data mining, etc., in order to find similar tree objects. Likewise, tree edit is a fundamental metric used in context of tree problems; and similarity computation between trees another key problem in context of Big Data. This dissertation makes a case for platform-centric data shaping as a potent mechanism to tackle the data- and architecture-borne issues in context of semi-structured data processing on GPU and GPU-like parallel architecture machines. In this dissertation, we propose several data shaping techniques for tree matching problems occurring in semi-structured data. We experiment with real world datasets. The experimental results obtained reveal that the proposed platform-centric data shaping approach is effective for computing similarities between tree objects using GPGPUs. The techniques proposed result in performance gains up to three orders of magnitude, subject to problem and platform

Database architecture evolution: Mammals flourished long before dinosaurs became extinct

The holy grail for database architecture research is to find a solution that is Scalable & Speedy, to run on anything from small ARM processors up to globally distributed compute clusters, Stable & Secure, to service a broad user community, Small & Simple, to be comprehensible to a small team of programmers, Self-managing, to let it run out-of-the-box without hassle. In this paper, we provide a trip report on this quest, covering both past experiences, ongoing research on hardware-conscious algorithms, and novel ways towards self-management specifically focused on column store solutions