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A prototype approach to leverage the bandwidth and computing power of GPUs for these matrix kernel operation is demonstrated resulting in an overall performance of over 110 GFlops/s on the desktop for large matrices and over 38 GFlops/s for sparse matrices arising in real applications.
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It requires matrix and kernel operations at the same time than multi-node communication management.
The third level corresponds to a kernel of the algorithm; each kernel operation is implemented on a single SPE within its local store as a sequence of micro-kernel operations.
The kernel operation yielding the maximum response for a given pixel determined the gradient direction.
The Kernel Logistic Regression (KLR) method [18] performed slightly better than BMRF, but this method involves an expensive matrix exponentiation operation, that is needed to compute the diffusion kernel.
In this paper, we empirically study the usefulness of several simple performance measures that are inexpensive to compute (in the sense that they do not require expensive matrix operations involving the kernel matrix).
A solution consists of expressing the matrix operations in the kernel space ϕ(d i ) T ϕ(d j ) as dot products in the input space K(d i, d j ), so-called kernel trick [ 37].
The matrix-vector multiplication operation is the kernel of most numerical algorithms.Typically, matrix-vector multiplication algorithms exploit the sparsity of a matrix, either to reduce the time taken or the memory used.
Then the covariance matrix operation is performed.
Emission files were prepared from the Sparse Matrix Operating Kernel for Emissions (SMOKE) model.
The recently proposed Laplacian twin support vector machine (Lap-TSVM) is an excellent nonparallel-based kernel tool for semi-supervised classification problems, where its optimal decision hyperplane is determined by solving two quadratic programming problems (QPPs) with matrix inversion operations.
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