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Compressive sensing (CS) provides a new perspective for data reduction without compromising performance when the signal of interest is sparse or has intrinsically low-dimensional structure. The theoretical foundation for most of the existing studies on CS is based on the stable embedding (i.e., a distance-preserving property) of vectors that are sparse or in a union of subspaces via random measurement...
It is an important task to tune performance for sparse matrix vector multiplication (SpMV), but it is also a difficult task because of its irregularity. In this paper, we propose a cache blocking method to improve the performance of SpMV on the emerging GPU architecture. The sparse matrix is partitioned into many sub-blocks, which are stored in CSR format. With the blocking method, the corresponding...
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