In the last few years, various communication compression techniques have emerged as an indispensable tool helping to alleviate the communication bottleneck in distributed learning. However, despite the fact biased com...
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The Globus project is a multi-institutional research effort that seeks to enable the construction of computational grids providing pervasive, dependable, and consistent access to high-performance computational resourc...
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The Globus project is a multi-institutional research effort that seeks to enable the construction of computational grids providing pervasive, dependable, and consistent access to high-performance computational resources, despite geographical distribution of both resources and users. Computational grid technology is being viewed as a critical element of future high-performance computing environments that will enable entirely new classes of computation-oriented applications, much as the World Wide Web fostered the development of new classes of information-oriented applications. In this paper, we report on the status of the Globus project as of early 1998. We describe the progress that has been achieved to date in the development of the Globus toolkit, a set of core services for constructing grid tools and applications. We also discuss the Globus Ubiquitous Supercomputing Testbed Organization (GUSTO) that we have constructed to enable large-scale evaluation of Globus technologies, and we review early experiences with the development of large-scale grid applications on the GUSTO testbed.
The goal of this work is the development of a highly parallel approach to computing the electron density in nanostructures. In the context of orbital-free density functional theory, a model reduction approach leads to...
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It is generally believed that the queueing behavior of an arrival process is most sensitive to its first and second order statistics. In this paper, we show that this is not generally true. We consider the queueing be...
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It is generally believed that the queueing behavior of an arrival process is most sensitive to its first and second order statistics. In this paper, we show that this is not generally true. We consider the queueing behavior of two classes of sources. The first class is the superposition of multiple discrete autoregressive sources of order 1 (referred to as DAR(1) sources), and the second class is a single DAR(1) source having the same first and second order statistics as the superposed source. In particular, we show that in the homogeneous case, the mean queue lengths of the two models are identical, but the queue length variances of the two models are significantly different depending on the correlation of individual sources.
Motivated by applications in robotics, we formulate the problem of minimizing the total angle cost of a TSP tour for a set of points in Euclidean space, where the angle cost of a tour is the sum of the direction chang...
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Motivated by applications in robotics, we formulate the problem of minimizing the total angle cost of a TSP tour for a set of points in Euclidean space, where the angle cost of a tour is the sum of the direction changes at the points. We establish the NP-hardness of both this problem and its relaxation to the cycle cover problem. We then consider the issue of designing approximation algorithms for these problems and show that both problems can be approximated to within a ratio of O(log n) in polynomial time. We also consider the problem of simultaneously approximating both the angle and the length measure for a TSP tour. In studying the resulting tradeoff, we choose to focus on the sum of the two performance ratios and provide tight bounds on the sum. Finally, we consider the external value of the angle measure and obtain essentially tight bounds for it. In this paper we restrict our attention to the planar setting, but all our results are easily extended to higher dimensions.
The classical convergence analysis of SGD is carried out under the assumption that the norm of the stochastic gradient is uniformly bounded. While this might hold for some loss functions, it is violated for cases wher...
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