Data Structures and Algorithms for Efficient Solution of Simultaneous Linear Equations from 3-D Ice Sheet Models

Two current software packages for solving large systems of sparse simultaneous l~neare equations are evaluated in terms of their applicability to solving systems of equations generated by the University of Maine Ice Sheet Model. SuperLU, the first package, has been developed by researchers at the University of California at Berkeley and the Lawrence Berkeley National Laboratory. UMFPACK, the second package, has been developed by T. A. Davis of the University of Florida who has ties with the U. C. Berkeley researchers as well as European researchers. Both packages are direct solvers that use LU factorization with forward and backward substitution. The University of Maine Ice Sheet Model uses the finite element method to solve partial differential equations that describe ice thickness, velocity,and temperature throughout glaciers as functions of position and t~me. The finite element method generates systems of linear equations having tens of thousands of variables and one hundred or so non-zero coefficients per equation. Matrices representing these systems of equations may be strictly banded or banded with right and lower borders. In order to efficiently Interface the software packages with the ice sheet model, a modified compressed column data structure and supporting routines were designed and written. The data structure interfaces directly with both software packages and allows the ice sheet model to access matrix coefficients by row and column number in roughly 100 nanoseconds while only storing non-zero entries of the matrix. No a priori knowledge of the matrix\u27s sparsity pattern is required. Both software packages were tested with matrices produced by the model and performance characteristics were measured arid compared with banded Gaussian elimination. When combined with high performance basic linear algebra subprograms (BLAS), the packages are as much as 5 to 7 times faster than banded Gaussian elimination. The BLAS produced by K. Goto of the University of Texas was used. Memory usage by the packages varted from slightly more than banded Gaussian elimination with UMFPACK, to as much as a 40% savings with SuperLU. In addition, the packages provide componentwise backward error measures and estimates of the matrix\u27s condition number. SuperLU is available for parallel computers as well as single processor computers. UMPACK is only for single processor computers. Both packages are also capable of efficiently solving the bordered matrix problem

Incremental file reorganization schemes

Issued as Final project report, Project no. G-36-66

Evaluating the Use of Good Neighbor Agreements for Environmental and Community Protection: Final Report

viii, 133 p. : forms ; 28 cmhttps://scholar.law.colorado.edu/books_reports_studies/1018/thumbnail.jp

Bit-Flip Aware Data Structures for Phase Change Memory

Big, non-volatile, byte-addressable, low-cost, and fast non-volatile memories like Phase Change Memory are appearing in the marketplace. They have the capability to unify both memory and storage and allow us to rethink the present memory hierarchy. An important draw-back to Phase Change Memory is limited write-endurance. In addition, Phase Change Memory shares with other Non-Volatile Random Access Memories an asym- metry in the energy costs of writes and reads. Best use of Non-Volatile Random Access Memories limits the number of times a Non-Volatile Random Access Memory cell changes contents, called a bit-flip. While the future of main memory is still unknown, we should already start to create data structures for them in order to shape the future era. This thesis investigates the creation of bit-flip aware data structures.The thesis first considers general ways in which a data structure can save bit- flips by smart overwrites and by using the exclusive-or of pointers. It then shows how a simple content dependent encoding can reduce bit-flips for web corpora. It then shows how to build hash based dictionary structures for Linear Hashing and Spiral Storage. Finally, the thesis presents Gray counters, close to bit-flip optimal counters that even enable age- based wear leveling with counters managed by the Non-Volatile Random Access Memories themselves instead of by the Operating Systems

Representation and parsing of multiword expressions

This book consists of contributions related to the definition, representation and parsing of MWEs. These reflect current trends in the representation and processing of MWEs. They cover various categories of MWEs such as verbal, adverbial and nominal MWEs, various linguistic frameworks (e.g. tree-based and unification-based grammars), various languages including English, French, Modern Greek, Hebrew, Norwegian), and various applications (namely MWE detection, parsing, automatic translation) using both symbolic and statistical approaches

Current trends

Deep parsing is the fundamental process aiming at the representation of the syntactic structure of phrases and sentences. In the traditional methodology this process is based on lexicons and grammars representing roughly properties of words and interactions of words and structures in sentences. Several linguistic frameworks, such as Headdriven Phrase Structure Grammar (HPSG), Lexical Functional Grammar (LFG), Tree Adjoining Grammar (TAG), Combinatory Categorial Grammar (CCG), etc., offer different structures and combining operations for building grammar rules. These already contain mechanisms for expressing properties of Multiword Expressions (MWE), which, however, need improvement in how they account for idiosyncrasies of MWEs on the one hand and their similarities to regular structures on the other hand. This collaborative book constitutes a survey on various attempts at representing and parsing MWEs in the context of linguistic theories and applications

Enterprise Data Mining & Machine Learning Framework on Cloud Computing for Investment Platforms

Machine Learning and Data Mining are two key components in decision making systems which can provide valuable in-sights quickly into huge data set. Turning raw data into meaningful information and converting it into actionable tasks makes organizations profitable and sustain immense competition. In the past decade we saw an increase in Data Mining algorithms and tools for financial market analysis, consumer products, manufacturing, insurance industry, social networks, scientific discoveries and warehousing. With vast amount of data available for analysis, the traditional tools and techniques are outdated for data analysis and decision support. Organizations are investing considerable amount of resources in the area of Data Mining Frameworks in order to emerge as market leaders. Machine Learning is a natural evolution of Data Mining. The existing Machine Learning techniques rely heavily on the underlying Data Mining techniques in which the Patterns Recognition is an essential component. Building an efficient Data Mining Framework is expensive and usually culminates in multi-year project for the organizations. The organization pay a heavy price for any delay or inefficient Data Mining foundation. In this research, we propose to build a cost effective and efficient Data Mining (DM) and Machine Learning (ML) Framework on cloud computing environment to solve the inherent limitations in the existing design methodologies. The elasticity of the cloud architecture solves the hardware constraint on businesses. Our research is focused on refining and enhancing the current Data Mining frameworks to build an enterprise data mining and machine learning framework. Our initial studies and techniques produced very promising results by reducing the existing build time considerably. Our technique of dividing the DM and ML Frameworks into several individual components (5 sub components) which can be reused at several phases of the final enterprise build is efficient and saves operational costs to the organization. Effective Aggregation using selective cuboids and parallel computations using Azure Cloud Services are few of many proposed techniques in our research. Our research produced a nimble, scalable portable architecture for enterprise wide implementation of DM and ML frameworks

Research Design Meets Market Design: Using Centralized Assignment for Impact Evaluation

A growing number of school districts use centralized assignment mechanisms to allocate school seats in a manner that reflects student preferences and school priorities. Many of these assignment schemes use lotteries to ration seats when schools are oversubscribed. The resulting random assignment opens the door to credible quasi-experimental research designs for the evaluation of school effectiveness. Yet the question of how best to separate the lottery-generated variation integral to such designs from non-random preferences and priorities remains open. This paper develops easily-implemented empirical strategies that fully exploit the random assignment embedded in a wide class of mechanisms, while also revealing why seats are randomized at one school but not another. We use these methods to evaluate charter schools in Denver, one of a growing number of districts that combine charter and traditional public schools in a unified assignment system. The resulting estimates show large achievement gains from charter school attendance. Our approach generates efficiency gains over ad hoc methods, such as those that focus on schools ranked first, while also identifying a more representative average causal effect. We also show how to use centralized assignment mechanisms to identify causal effects in models with multiple school sectors

Data Structures & Algorithm Analysis in C++

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