Bilkent University
Department of Computer Engineering


Reordering Methods for Exploiting Spatial and Temporal Localities in Parallel Sparse Matrix-Vector Multiplication


Nabil AbuBaker
MS Student
(Supervisor: Prof. Dr. Cevdet Aykanat)
Computer Engineering Department
Bilkent University

Sparse Matrix-Vector multiplication (SpMV) is a very important kernel operation for many scientific applications. For irregular sparse matrices, the SpMV operation suffers from poor cache performance due to the irregular accesses of the input vector entries. In this work, we propose row and column reordering methods based on Graph partitioning (GP) and Hypergraph partitioning (HP) in order to exploit spatial and temporal localities in accessing input vector entries by clustering rows/columns with similar sparsity patterns close to each other. The proposed methods exploit spatial and temporal localities separately (using either rows or columns of the matrix in a GP or HP method), simultaneously (using both rows and column) and in a two-phase manner (using either rows or columns in each phase). We evaluate the validity of the proposed models on a 60-core Xeon Phi co-processor for a large set of sparse matrices arising from different applications. The performance results confirm the validity and the effectiveness of the proposed methods and models.


DATE: 8 August 2016, Monday @ 15:00