Paylaşılan bellek mimarisinde gerçekleştirilen paralel seyrek matris-vektör ve devrik-matris-vektör çarpımında veri yeniden kullanımını arttırmak

Sparse matrix-vector and matrix-transpose-vector multiplications (Sparse AATx) are the kernel operations used in iterative solvers. Sparsity pattern of the input matrix A, as well as its transpose, remains the same throughout the iterations. CPU cache could not be used properly during these Sparse AA T x operations due to irregular sparsity pattern of the matrix. We propose two parallelization strategies for Sparse AA T x. Our methods partition A matrix in order to exploit cache locality for matrix nonzeros and vector entries. We conduct experiments on the recently-released Intel Xeon Phi coprocessor involving large variety of sparsematrices. Experimental results show that proposed methods achieve higher performance improvement than the state-of-the-art methods in the literature.