251,122 research outputs found
Sensitivity analysis for large-scale problems
The development of efficient techniques for calculating sensitivity derivatives is studied. The objective is to present a computational procedure for calculating sensitivity derivatives as part of performing structural reanalysis for large-scale problems. The scope is limited to framed type structures. Both linear static analysis and free-vibration eigenvalue problems are considered
Optimising Sparse Matrix Vector multiplication for large scale FEM problems on FPGA
Sparse Matrix Vector multiplication (SpMV) is an important kernel in many scientific applications. In this work we propose an architecture and an automated customisation method to detect and optimise the architecture for block diagonal sparse matrices. We evaluate the proposed approach in the context of the spectral/hp Finite Element Method, using the local matrix assembly approach. This problem leads to a large sparse system of linear equations with block diagonal matrix which is typically solved using an iterative method such as the Preconditioned Conjugate Gradient. The efficiency of the proposed architecture combined with the effectiveness of the proposed customisation method reduces BRAM resource utilisation by as much as 10 times, while achieving identical throughput with existing state of the art designs and requiring minimal development effort from the end user. In the context of the Finite Element Method, our approach enables the solution of larger problems than previously possible, enabling the applicability of FPGAs to more interesting HPC problems
Advances and open problems on the control of large scale systems
Bibliography: leaves 10-12.ONR Contract N00014-76-C-0345 and ERDA Contract E-(49-18)-2087.by Michael Athans
On very large scale assignment problems
"May 1993."Includes bibliographical references (p. 24-25).Yusin Lee [and] James B. Orlin
Large-scale linear regression: Development of high-performance routines
In statistics, series of ordinary least squares problems (OLS) are used to
study the linear correlation among sets of variables of interest; in many
studies, the number of such variables is at least in the millions, and the
corresponding datasets occupy terabytes of disk space. As the availability of
large-scale datasets increases regularly, so does the challenge in dealing with
them. Indeed, traditional solvers---which rely on the use of black-box"
routines optimized for one single OLS---are highly inefficient and fail to
provide a viable solution for big-data analyses. As a case study, in this paper
we consider a linear regression consisting of two-dimensional grids of related
OLS problems that arise in the context of genome-wide association analyses, and
give a careful walkthrough for the development of {\sc ols-grid}, a
high-performance routine for shared-memory architectures; analogous steps are
relevant for tailoring OLS solvers to other applications. In particular, we
first illustrate the design of efficient algorithms that exploit the structure
of the OLS problems and eliminate redundant computations; then, we show how to
effectively deal with datasets that do not fit in main memory; finally, we
discuss how to cast the computation in terms of efficient kernels and how to
achieve scalability. Importantly, each design decision along the way is
justified by simple performance models. {\sc ols-grid} enables the solution of
correlated OLS problems operating on terabytes of data in a matter of
hours
Linear Programming for Large-Scale Markov Decision Problems
We consider the problem of controlling a Markov decision process (MDP) with a
large state space, so as to minimize average cost. Since it is intractable to
compete with the optimal policy for large scale problems, we pursue the more
modest goal of competing with a low-dimensional family of policies. We use the
dual linear programming formulation of the MDP average cost problem, in which
the variable is a stationary distribution over state-action pairs, and we
consider a neighborhood of a low-dimensional subset of the set of stationary
distributions (defined in terms of state-action features) as the comparison
class. We propose two techniques, one based on stochastic convex optimization,
and one based on constraint sampling. In both cases, we give bounds that show
that the performance of our algorithms approaches the best achievable by any
policy in the comparison class. Most importantly, these results depend on the
size of the comparison class, but not on the size of the state space.
Preliminary experiments show the effectiveness of the proposed algorithms in a
queuing application.Comment: 27 pages, 3 figure
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