1results about How to "Reduce memory access" patented technology

This application provides a GPU-based sparse matrix-vector multiplication optimization method and apparatus for large-scale computing. The method includes rearranging and reorganizing the sparse matrix to make the distribution of non-zero elements in adjacent columns of the rearranged sparse matrix more similar; dividing the non-zero elements in each row of the sorted sparse matrix into several slices, placing the slices into containers that satisfy constraints, namely, the starting column index of the newly added slice is consecutive to the column indices of other existing slices; each container corresponds to a synchronous execution unit, and several synchronous threads perform the multiplication and accumulation of non-zero elements with the corresponding components of the input vector. Thus, matrix rearrangement makes it more likely that a synchronous execution unit will process data from the same row; by dividing non-zero elements with the same row index and consecutive column indices into slices and placing them into containers, the continuity of data processed by the synchronous execution unit in the column direction reduces non-merged memory accesses and enhances data reuse capabilities.