Sentence examples for matrix multiplication kernels from inspiring English sources

In this paper we present GiMMiK a generator of bespoke matrix multiplication kernels for the CUDA and OpenCL platforms.

Using GiMMiK as the matrix multiplication kernel provider allows us to achieve a speedup of up to 1.70 (2.19) for a simulation of an unsteady flow over a cylinder executed with PyFR in double (single) precision on the Tesla K40c.

Our study targets algorithmic-based fault-tolerant versions of the dense matrix-vector and matrix -matrix) matrix -matrix kernels (gemultiplicationespectively), using the BLIS frameworkernelsell as an implementation of the LU factorization with partial pivotingemvilt on top of gemm.

We incrementally build efficient kernels, for matrix multiplication first, then triangular system solving, on top of which a recursive PLUQ decomposition algorithm is built.

Modern CPU architectures and computational optimization methods allow for very fast matrix multiplication, thus those types of kernel matrices can be computed much faster than others.

The matrix multiplication was repeated multiple times until the spectrum matches the observed spectrum.

The q-step random walk kernels K q − step  =  K q, can be easily obtained by matrix multiplication from the one-step random walk kernel matrix (20), where q represents the number of random walk steps in the underlying graph [53].

Some types of kernel, however, can be computed using matrix multiplication.

The performance of GiMMiK's kernels is particularly apparent in a block-by-panel type of matrix multiplication, where the block matrix is typically small (e.g. dimensions of 96×64).

The discrete convolution can be written as matrix multiplication B = KX, where K is a convolution matrix constructed from the kernel k.

Projective transformations, which are invertible linear changes of homogeneous coordinates, are given by matrix multiplication.