Sparse Matrix Computations on Graphics Processing Units Publication Trend The graph below shows the total number of publications each year in Sparse Matrix Computations on Graphics Processing Units.

Sparse matrix computations are pivotal to advancing high-performance scientific applications, particularly as modern numerical simulations and data analyses demand efficient management of large ...

Graphics processing units (GPUs) perform matrix multiplication tasks due to their ability to handle many calculations simultaneously. They break large matrix problems into smaller segments and solve ...

In particular, the Intel MKL DGEMM function for matrix-matrix multiplication is highly tuned for small matrices. To eliminate overhead, Intel MKL provides a compiler flag to guarantee that the fastest ...

Engheta and colleagues have now set their sights on vector–matrix multiplication, which is a vital operation for the artificial neural networks used in some artificial intelligence systems. The team ...

Photonic accelerators have been widely designed to accelerate some specific categories of computing in the optical domain, especially matrix multiplication, to address the growing demand for ...

The matrix multiplication infix operator (*) produces a new matrix by performing matrix multiplication. The first matrix must have the same number of columns as the second matrix has rows.

The result of elementwise multiplication is also known as the Schur or Hadamard product. Element multiplication (using the # operator) should not be confused with matrix multiplication (using the * ...