We propose an accelerated density matrix purification scheme with error control. The method makes use of the scale-and-fold acceleration technique and screening submatrix products in block-sparse matrix-matrix multiplies to reduce computational cost. An bound a parameter sweep are combined select threshold value for screening, such that can be controlled. evaluate performance comparison without...

Gyrate atrophy (GA) is a rare, progressive metabolic choroid and retinal degeneration that results from a deficiency of the pyridoxal phosphate-dependent mitochondrial matrix enzyme ornithine aminotransferase. Here, we report the case of a 40-yearold woman who presented with a gradual decline in visual acuity since puberty, along with a history of high myopia and cataract surgery. She was admit...

In this paper, we present our work on developing a new matrix format and a new sparse matrix-vector multiplication algorithm. The matrix format is HEC, which is a hybrid format. This matrix format is efficient for sparse matrix-vector multiplication and is friendly to preconditioner. Numerical experiments show that our sparse matrix-vector multiplication algorithm is efficient on

Sparse matrices are a core component in many numerical simulations, and their efficiency is essential to achieving high performance. Dynamic sparse-matrix allocation (insertion) can benefit a number of problems such as sparse-matrix factorization, sparse-matrix-matrix addition, static analysis (e.g., points-to analysis), computing transitive closure, and other graph algorithms. Existing sparse-...

The multiplication of a sparse matrix with dense vector (SpMV) is key component in many numerical schemes and its performance known to be severely limited by main memory access. Several require the polynomial which typically implemented as sequence SpMVs. This results low ignores potential increase arithmetic intensity reusing data from cache. In this work we use recursive algebraic coloring en...

We consider designing a sparse sensing matrix which contains few non-zero entries per row for compressive sensing (CS) systems. By unifying the previous approaches for optimizing sensing matrices based on minimizing the mutual coherence, we propose a general framework for designing a sparse sensing matrix that minimizes the mutual coherence of the equivalent dictionary and is robust to sparse r...