Hypergraph partitioning based models and methods for exploiting cache locality in sparse matrix-vector multiplication
SIAM Journal on Scientific Computing
MetadataShow full item record
Please cite this item using this persistent URLhttp://hdl.handle.net/11693/20764
Sparse matrix-vector multiplication (SpMxV) is a kernel operation widely used in iterative linear solvers. The same sparse matrix is multiplied by a dense vector repeatedly in these solvers. Matrices with irregular sparsity patterns make it difficult to utilize cache locality effectively in SpMxV computations. In this work, we investigate single-and multiple-SpMxV frameworks for exploiting cache locality in SpMxV computations. For the single-SpMxV framework, we propose two cache-size-aware row/column reordering methods based on one-dimensional (1D) and twodimensional (2D) top-down sparse matrix partitioning. We utilize the column-net hypergraph model for the 1D method and enhance the row-column-net hypergraph model for the 2D method. The primary aim in both of the proposed methods is to maximize the exploitation of temporal locality in accessing input vector entries. The multiple-SpMxV framework depends on splitting a given matrix into a sum of multiple nonzero-disjoint matrices. We propose a cache-size-aware splitting method based on 2D top-down sparse matrix partitioning by utilizing the row-column-net hypergraph model. The aim in this proposed method is to maximize the exploitation of temporal locality in accessing both input-and output-vector entries. We evaluate the validity of our models and methods on a wide range of sparse matrices using both cache-miss simulations and actual runs by using OSKI. Experimental results show that proposed methods and models outperform state-of-the-art schemes. © 2013 Society for Industrial and Applied Mathematics.
- Research Paper 7144
Showing items related by title, author, creator and subject.
Akbudak K.; Aykanat C. (IEEE Computer Society, 2017)Exploiting spatial and temporal localities is investigated for efficient row-by-row parallelization of general sparse matrix-matrix multiplication (SpGEMM) operation of the form C=A,B on many-core architectures. Hypergraph ...
Encapsulating multiple communication-cost metrics in partitioning sparse rectangular matrices for parallel matrix-vector multiplies Uçar, B.; Aykanat, C. (2004)This paper addresses the problem of one-dimensional partitioning of structurally unsymmetric square and rectangular sparse matrices for parallel matrix-vector and matrix-transpose-vector multiplies. The objective is to ...
Uçar, B.; Çatalyürek, U.V.; Aykanat, C. (2010)We present the PaToH MATLAB Matrix Partitioning Interface. The interface provides support for hypergraph-based sparse matrix partitioning methods which are used for efficient parallelization of sparse matrix-vector ...