Geoffrey C. Fox

Bio: Geoffrey C. Fox is an academic researcher from Indiana University. The author has contributed to research in topics: Web service & Grid computing. The author has an hindex of 66, co-authored 882 publications receiving 25499 citations. Previous affiliations of Geoffrey C. Fox include University of Virginia & University of Maryland, College Park.

Grid Computing: Making the Global Infrastructure a Reality

Abstract: From the Publisher: Grid computing is applying the resources of many computers in a network to a single problem at the same time Grid computing appears to be a promising trend for three reasons: (1) Its ability to make more cost-effective use of a given amount of computer resources, (2) As a way to solve problems that can't be approached without an enormous amount of computing power (3) Because it suggests that the resources of many computers can be cooperatively and perhaps synergistically harnessed and managed as a collaboration toward a common objective. A number of corporations, professional groups, university consortiums, and other groups have developed or are developing frameworks and software for managing grid computing projects. The European Community (EU) is sponsoring a project for a grid for high-energy physics, earth observation, and biology applications. In the United States, the National Technology Grid is prototyping a computational grid for infrastructure and an access grid for people. Sun Microsystems offers Grid Engine software. Described as a distributed resource management tool, Grid Engine allows engineers at companies like Sony and Synopsys to pool the computer cycles on up to 80 workstations at a time. "the Grid" is a very hot topic generating broad interest from research and industry (e.g. IBM, Platform, Avaki, Entropia, Sun, HP) Grid architecture enables very popular e-Science projects like the Genome project which demand global interaction and networking In recent surveys over 500f Chief Information Officers are expected to use Grid technology this year Grid Computing: Features contributions from the major players in the field Covers all aspects of grid technology from motivation to applications Provides an extensive state-of-the-art guide in grid computing This is essential reading for researchers in Computing and Engineering, physicists, statisticians, engineers and mathematicians and IT policy makers.

Application Overview for the Book: Grid Computing – Making the Global Infrastructure a Reality

Abstract: From the Publisher: Grid computing is applying the resources of many computers in a network to a single problem at the same time Grid computing appears to be a promising trend for three reasons: (1) Its ability to make more cost-effective use of a given amount of computer resources, (2) As a way to solve problems that can't be approached without an enormous amount of computing power (3) Because it suggests that the resources of many computers can be cooperatively and perhaps synergistically harnessed and managed as a collaboration toward a common objective. A number of corporations, professional groups, university consortiums, and other groups have developed or are developing frameworks and software for managing grid computing projects. The European Community (EU) is sponsoring a project for a grid for high-energy physics, earth observation, and biology applications. In the United States, the National Technology Grid is prototyping a computational grid for infrastructure and an access grid for people. Sun Microsystems offers Grid Engine software. Described as a distributed resource management tool, Grid Engine allows engineers at companies like Sony and Synopsys to pool the computer cycles on up to 80 workstations at a time. "the Grid" is a very hot topic generating broad interest from research and industry (e.g. IBM, Platform, Avaki, Entropia, Sun, HP) Grid architecture enables very popular e-Science projects like the Genome project which demand global interaction and networking In recent surveys over 500f Chief Information Officers are expected to use Grid technology this year Grid Computing: Features contributions from the major players in the field Covers all aspects of grid technology from motivation to applications Provides an extensive state-of-the-art guide in grid computing This is essential reading for researchers in Computing and Engineering, physicists, statisticians, engineers and mathematicians and IT policy makers.

Twister: a runtime for iterative MapReduce

Abstract: MapReduce programming model has simplified the implementation of many data parallel applications. The simplicity of the programming model and the quality of services provided by many implementations of MapReduce attract a lot of enthusiasm among distributed computing communities. From the years of experience in applying MapReduce to various scientific applications we identified a set of extensions to the programming model and improvements to its architecture that will expand the applicability of MapReduce to more classes of applications. In this paper, we present the programming model and the architecture of Twister an enhanced MapReduce runtime that supports iterative MapReduce computations efficiently. We also show performance comparisons of Twister with other similar runtimes such as Hadoop and DryadLINQ for large scale data parallel applications.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Data clustering: a review

Abstract: Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.