&lt;title&gt;Computation Of The Generalized Singular Value Decomposition Using Mesh-Connected Processors&lt;/title&gt;

Brent, Richard P.; Luk, Franklin T.; Loan, Charles Van

doi:10.1117/12.936442

Cited by 57 publications

(102 citation statements)

References 15 publications

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“…PˆFFT engines (N`1 points) Full SVD (serial) P N 3 P N 2 P N 3 1 multipurpose pu. Full SVD (systolic array) [31], [32] P N 3 P N 2 N plog N`P q N 2ˆ2 -by-2 SVD pu.…”

Section: Application Of the Fri Approach: Perkmentioning

confidence: 99%

Fast and Robust Parametric Estimation of Jointly Sparse Channels

Barbotin

Vetterli

2012

IEEE J. Emerg. Sel. Topics Circuits Syst.

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If P antennas measure a K-multipath channel with N uniformly sampled measurements per channel, the algorithm possesses an OpKP N log N q complexity and an OpKP N q memory footprint instead of OpP N 3 q and OpP N 2 q for the direct implementation, making it suitable for K ! N . The sparsity is estimated online based on the PER, and the algorithm therefore has a sense of introspection being able to relinquish sparsity if it is lacking.The estimation performances are tested on field measurements with synthetic AWGN, and the proposed algorithm outperforms non-sparse reconstruction in the medium to low SNR range (ď 0dB), increasing the rate of successful symbol decodings by 1{10 th in average, and 1{3 rd in the best case. The experiments also show that the algorithm does not perform worse than a nonsparse estimation algorithm in non-sparse operating conditions, since it may fall-back to it if the PER criterion does not detect a sufficient level of sparsity.The algorithm is also tested against a method assuming a "discrete" sparsity model as in Compressed Sensing (CS). The conducted test indicates a trade-off between speed and accuracy.

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“…PˆFFT engines (N`1 points) Full SVD (serial) P N 3 P N 2 P N 3 1 multipurpose pu. Full SVD (systolic array) [31], [32] P N 3 P N 2 N plog N`P q N 2ˆ2 -by-2 SVD pu.…”

Section: Application Of the Fri Approach: Perkmentioning

confidence: 99%

Fast and Robust Parametric Estimation of Jointly Sparse Channels

Barbotin

Vetterli

2012

IEEE J. Emerg. Sel. Topics Circuits Syst.

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“…We compute the SVD based on a cyclic Jacobi procedure for mesh processors outlined by Brent et al [Brent et al 1985]. Because this step occurs after the QR decomposition, we ensure the input matrix, R, is square with dimensions n × n. If n is odd, the algorithm pads the matrix with a column and row of zeros to make it even.…”

Section: Calibrationmentioning

confidence: 99%

In-Network Distributed Solar Current Prediction

Basha

Jurdak

Rus

2014

ACM Trans. Sen. Netw.

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Long-term sensor network deployments demand careful power management. While managing power requires understanding the amount of energy harvestable from the local environment, current solar prediction methods rely only on recent local history, which makes them susceptible to high variability. In this paper, we present a model and algorithms for distributed solar current prediction, based on multiple linear regression to predict future solar current based on local, in-situ climatic and solar measurements. These algorithms leverage spatial information from neighbors and adapt to the changing local conditions not captured by global climatic information. We implement these algorithms on our Fleck platform and run a 7-weeklong experiment validating our work. In analyzing our results from this experiment, we determined that computing our model requires an increased energy expenditure of 4.5mJ over simpler models (on the order of 10 −7 % of the harvested energy) to gain a prediction improvement of 39.7%.

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“…The benefits of the systolic software pattern are, of course, limited to those applications for which systolic formulations can be found. Systolic algorithms currently exist for a wide range of dense matrix problems commonly found in scientific computing and digital signal processing [18,2] as well as some sorting and graph problems [21]. The systolic pattern is not a good match for highly irregular applications where communication patterns cannot be predicted in advance.…”

Section: Limitationsmentioning

confidence: 99%

A pattern for efficient parallel computation on multicore processors with scalar operand networks

Hoffmann¹,

Devadas²,

Agarwal³

2010

Proceedings of the 2010 Workshop on Parallel Programming Patterns

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Systolic arrays have long been used to develop custom hardware because they result in designs that are efficient and scalable. Many researchers have explored ways to exploit systolic designs in programmable processors; however, such efforts often result in the simulation of large systolic arrays on a general purpose platforms. While simulation can add flexibility and problem size independence, it comes at a cost of greatly reducing the efficiency of the original systolic approach. This paper presents a pattern for developing parallel programs using systolic designs to execute efficiently (without resorting to simulation) on modern multicore processors featuring scalar operand networks. This pattern provides a compromise solution that can achieve high efficiency and flexibility given appropriate hardware support. Several examples illustrate the application of this pattern to produce parallel implementations of matrix multiplication and convolution.

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<title>Computation Of The Generalized Singular Value Decomposition Using Mesh-Connected Processors</title>

Abstract: This paper concerns the systolic array computation of the generalized singular value decomposition. Numerical algorithms for both one-and two -dimensional systolic architectures are discussed.

Cited by 57 publications

References 15 publications

Fast and Robust Parametric Estimation of Jointly Sparse Channels

Fast and Robust Parametric Estimation of Jointly Sparse Channels

In-Network Distributed Solar Current Prediction

A pattern for efficient parallel computation on multicore processors with scalar operand networks

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