Department of Computer Engineering
MS Thesis Presentation
Increasing Data Reuse in Parallel Sparse Matrix-Vector and Matrix-Transpose-Vector Multiply on Shared-Memory Architectures
Mustafa Ozan Karsavuran
(Supervisor: Prof. Dr. Cevdet Aykanat)
Computer Engineering Department
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 AATx operations due to irregular sparsity pattern of the matrix. We propose two parallelization strategies for Sparse AATx. 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 sparse matrices. Experimental results show that proposed methods achieve higher performance improvement than the state-of-the-art methods in the literature.
DATE: 05 September, 2014, Friday @ 11:00