NDLI: Singular value decomposition utilizing parallel algorithms on graphical processors

Content Provider	IEEE Xplore Digital Library
Author	Kotas, C. Barhen, J.
Copyright Year	2011
Description	Author affiliation: Center for Engineering Science Advanced Research, Oak Ridge National Laboratory, TN, USA (Kotas, C.; Barhen, J.)
Abstract	One of the current challenges in underwater acoustic array signal processing is the detection of quiet targets in the presence of noise. In order to enable robust detection, one of the key processing steps requires data and replica whitening. This, in turn, involves the eigen-decomposition of the sample spectral matrix, C = 1 over K Σ $^{K}X(k)X^{H}(k)$ where X(k) denotes a single frequency snapshot with an element for each element of the array. By employing the singular value decomposition (SVD) method, the eigenvectors and eigenvalues can be determined directly from the data without computing the sample covariance matrix, reducing the computational requirements for a given level of accuracy (van Trees, Optimum Array Processing). (Recall that the SVD of a complex matrix A involves determining V, Σ, and U such that A = $UΣV^{H}$ where U and V are orthonormal and Σ is a positive, real, diagonal matrix containing the singular values of A. U and V are the eigenvectors of $AA^{H}$ and $A^{H}A,$ respectively, while the singular values are the square roots of the eigenvalues of $AA^{H}.)$ Because it is desirable to be able to compute these quantities in real time, an efficient technique for computing the SVD is vital. In addition, emerging multicore processors like graphical processing units (GPUs) are bringing parallel processing capabilities to an ever increasing number of users. Since the computational tasks involved in array signal processing are well suited for parallelization, it is expected that these computations will be implemented using GPUs as soon as users have the necessary computational tools available to them. Thus, it is important to have an SVD algorithm that is suitable for these processors.
Starting Page	1
Ending Page	7
File Size	729421
Page Count	7
File Format	PDF
ISBN	9781457714276
e-ISBN	9780933957398
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-19
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	MTS
Subject Keyword	Graphics processing unit Jacobian matrices Arrays Instruction sets Vectors Matrix decomposition Libraries GPU singular value decompostion parallel processing
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A parallel method for computing the generalized singular value decomposition

Design and Implementation of Jacobi Algorithms on GPU

Optimizing Sparse Matrix Operations on GPUs Using Merge Path

Fast Sparse Matrix Matrix Product Based on ELLR-T and GPU Computing

Parallel singular value decomposition on heterogeneous multi-core and multi-GPU platforms

Iterative Methods for Sparse Linear Systems on Graphics Processing Unit

Sparse LU factorization for parallel circuit simulation on GPU

Evaluating Polynomials in Several Variables and their Derivatives on a GPU Computing Processor

A Parallel Maximum Subarray Algorithm on GPUs

Singular value decomposition utilizing parallel algorithms on graphical processors

Similar Documents

A parallel method for computing the generalized singular value decomposition

Design and Implementation of Jacobi Algorithms on GPU

Optimizing Sparse Matrix Operations on GPUs Using Merge Path

Fast Sparse Matrix Matrix Product Based on ELLR-T and GPU Computing

Parallel singular value decomposition on heterogeneous multi-core and multi-GPU platforms

Iterative Methods for Sparse Linear Systems on Graphics Processing Unit

Sparse LU factorization for parallel circuit simulation on GPU

Evaluating Polynomials in Several Variables and their Derivatives on a GPU Computing Processor

A Parallel Maximum Subarray Algorithm on GPUs

Singular value decomposition utilizing parallel algorithms on graphical processors