Sparse Matrices Research Papers - Academia.edu (original) (raw)

A number of parallel formulations of dense matrix multiplication algorithm have been developed. For arbitrarily large number of processors, any of these algorithms or their variants can provide near linear speedup for sufficiently large... more

A number of parallel formulations of dense matrix multiplication algorithm have been developed. For arbitrarily large number of processors, any of these algorithms or their variants can provide near linear speedup for sufficiently large matrix sizes and none of the algorithms can be clearly claimed to be superior than the others. In this paper we analyze the performance and scalability of a number of parallel formulations of the matrix multiplication algorithm and predict the conditions under which each formulation is better than the others.

We describe a new algorithm for Gaussian Elimination suitable for general (unsymmetric and possibly singular) sparse matrices, of any entry type, which has a natural parallel and distributed-memory formulation but degrades gracefully to... more

We describe a new algorithm for Gaussian Elimination suitable for general (unsymmetric and possibly singular) sparse matrices, of any entry type, which has a natural parallel and distributed-memory formulation but degrades gracefully to sequential execution. We present a sample MPI implementation of a program computing the rank of a sparse integer matrix using the proposed algorithm. Some preliminary performance measurements are presented and discussed, and the performance of the algorithm is compared to corresponding state-of-the-art algorithms for floating-point and integer matrices.

On multicore architectures, the ratio of peak memory bandwidth to peak floating-point performance (byte:flop ratio) is decreasing as core counts increase, further limiting the performance of bandwidth limited applications. Multiplying a... more

On multicore architectures, the ratio of peak memory bandwidth to peak floating-point performance (byte:flop ratio) is decreasing as core counts increase, further limiting the performance of bandwidth limited applications. Multiplying a sparse matrix (as well as its transpose in the unsymmetric case) with a dense vector is the core of sparse iterative methods. In this paper, we present a new multithreaded algorithm for the symmetric case which potentially cuts the bandwidth requirements in half while exposing lots of parallelism in practice. We also give a new data structure transformation, called bit masked register blocks, which promises significant reductions on bandwidth requirements by reducing the number of indexing elements without introducing additional fill-in zeros. Our work shows how to incorporate this transformation into existing parallel algorithms (both symmetric and unsymmetric) without limiting their parallel scalability. Experimental results indicate that the combi...

TopoToolbox contains a set of Matlab functions that provide utilities for relief analysis in a non-Geographical Information System (GIS) environment. The tools have been developed to support the work flow in combined spatial and... more

TopoToolbox contains a set of Matlab functions that provide utilities for relief analysis in a non-Geographical Information System (GIS) environment. The tools have been developed to support the work flow in combined spatial and non-spatial numerical analysis. They offer flexible and user-friendly software for hydrological and geomorphological research that involves digital elevation model analysis and focuses on material fluxes and spatial variability of water, sediment, chemicals and nutrients. The objective of this paper is to give an introduction to the linear algebraic concept behind the software that employs sparse matrix computations for digital elevation model analysis. Moreover, we outline the functionality of the toolbox. The source codes are freely available in Matlab language on the authors' webpage (physiogeo.unibas.ch/topotoolbox).

The simulation of large digital and mixed-signal integrated circuits is one of the challenges of the electronics design automation industry. In this work, a fast linear solver, KLU, is implemented into NGSPICE circuit simulator and its... more

The simulation of large digital and mixed-signal integrated circuits is one of the challenges of the electronics design automation industry. In this work, a fast linear solver, KLU, is implemented into NGSPICE circuit simulator and its performances have been verified on standard netlists.

In this paper, wavelets and fuzzy support vector machines are used to automated detect and classify power quality (PQ) disturbances. Electric power quality is an aspect of power engineering that has been with us since the inception of... more

In this paper, wavelets and fuzzy support vector machines are used to automated detect and classify power quality (PQ) disturbances. Electric power quality is an aspect of power engineering that has been with us since the inception of power systems. The types of concerned disturbances include voltage sags, swells, interruptions, switching transients, impulses, flickers, harmonics, and notches. Fourier transform and wavelet analysis are utilized to denoise the digital signals, to decompose the signals and then to obtain eight common features for the sampling PQ disturbance signals. A fuzzy support vector machines is designed and trained by 8-dimension feature space points for making a decision regarding the type of the disturbance. Simulation cases illustrate the effectiveness.

The design of a differential-mode EMC input filter for a three-phase AC-DC-AC very sparse matrix converter intended for electrical machine drive applications is discussed in this paper. A review of the steps to be performed in the course... more

The design of a differential-mode EMC input filter for a three-phase AC-DC-AC very sparse matrix converter intended for electrical machine drive applications is discussed in this paper. A review of the steps to be performed in the course of the filter design is presented and a detailed mathematical model of the EMI test receiver for quasipeak measurement of conducted emissions

Spectral unmixing is an important tool in hyperspectral data analysis for estimating endmembers and abundance fractions in a mixed pixel. This paper examines the applicability of a recently developed algorithm called graph regularized... more

Spectral unmixing is an important tool in hyperspectral data analysis for estimating endmembers and abundance fractions in a mixed pixel. This paper examines the applicability of a recently developed algorithm called graph regularized nonnegative matrix factorization (GNMF) for this aim. The proposed approach exploits the intrinsic geometrical structure of the data besides considering positivity and full additivity constraints. Simulated data based on the measured spectral signatures, is used for evaluating the proposed algorithm. Results in terms of abundance angle distance (AAD) and spectral angle distance (SAD) show that this method can effectively unmix hyperspectral data.

Wheeling is the transmission of electrical energy from a buyer to a seller through a transmission network owned by a third party. This paper provides a theoretically sound, yet practical to implement, basis for setting wheeling rates.... more

Wheeling is the transmission of electrical energy from a buyer to a seller through a transmission network owned by a third party. This paper provides a theoretically sound, yet practical to implement, basis for setting wheeling rates. These wheeling rates are based on marginal costs (determined by losses and effects of line flow and voltage magnitude constraints) adjusted up or down as necessary to account for embedded capital costs (i.e. revenue reconciliation). Simple numerical examples are provided to illustrate interesting phenomena such as negative wheeling rates which yield positive net revenue. Comparisons with present day wheeling rates show that major differences exist. The wheeling rates of this paper yield a "no lose" situation for the buying, selling and wheeling utilities.

In this paper we propose an incremental item-based collaborative filtering algorithm. It works with binary ratings (sometimes also called implicit ratings), as it is typically the case in a Web environment. Our method is capable of... more

In this paper we propose an incremental item-based collaborative filtering algorithm. It works with binary ratings (sometimes also called implicit ratings), as it is typically the case in a Web environment. Our method is capable of incorporating new information in parallel with performing recommendation. New sessions and new users are used to update the similarity matrix as they appear. The proposed algorithm is compared with a non-incremental one, as well as with an incremental user-based approach, based on an existing explicit rating recommender. The use of techniques for working with sparse matrices on these algorithms is also evaluated. All versions, implemented in R, are evaluated on 5 datasets with various number of users and/or items. We observed that: Recall tends to improve when we continuously add information to the recommender model; the time spent for recommendation does not degrade; the time for updating the similarity matrix (necessary to the recommendation) is relatively low and motivates the use of the item-based incremental approach. Moreover we study how the number of items and users affects the user based and the item based approaches.