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We used the CUDAMat library [33] to perform matrix operations on a GPU for speeding up the training procedures.
Thus, the present study considers the architectural characteristics that affect the performance of matrix operations on real multicore processors.
Our proposed parallel algorithm employs these techniques to reduce communication overhead during matrix operations on the star graph.
This is achieved by applying QL decomposition followed by elementary matrix operations on H in a manner analogous to the modified QL decomposition algorithm based on the Gram-Schmidt orthogonalization procedure, thus avoiding the need for expensive matrix inversion operations.
In this work, an alternative way to calculate the aPC is suggested, which provides the optimal polynomials, Gaussian quadrature collocation points and weights from the moments using only a handful of matrix operations on the Hankel matrix of moments.
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In addition, recent studies of matrix operations rely on parallel processing to reduce any calculation delays.
Retrieval aspects, such as content, structure and semantics, are expressed by matrices defined in these spaces and by matrix operations applied on them.
In this paper, we consider symmetric approaches and we focus on Matrix Operation for Randomization and Encryption (MORE) approach to build a new algorithm overcoming the drawbacks of MORE.
On the basis of these analyses, the tuning method has been concluded and a computer program THURT Tsing Hua University RFQ Tune) has been composed to perform all the calculations based on matrix operations.
Like the method by Chen et al. (2012), the new method is based on matrix operations and is invariant to linear transformations.
The method is iterative and relies only on constant matrix operations such as multiplications, null-space calculations and equivalence transformations, and thereby straightforward to implement.
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