.png){kind=icon}
.png){kind=icon}
Return
HYPERGRAPH PARTITIONING BASED MODELS AND METHODS FOR EXPLOITING CACHE LOCALITY IN SPARSE MATRIX- VECTOR MULTIPLICATION
.png){kind=icon}
OA
AI
DOI：10.1137/100813956.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 two-dimensional (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.
Keywords:
cache locality
sparse matrix
matrix-vector multiplication
matrix reordering
computational hypergraph model
hypergraph partitioning
traveling salesman problem
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.6
Papers:
5.1K
Citations:
1.8W