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The Sparse Matrix-Vector Multiplication (SpMxV) kernel exhibits poor scaling on shared memory systems, due to the streaming nature of its data access pattern. To decrease memory contention and improve kernel performance we propose two compression schemes: CSR-DU, that targets the reduction of the matrix structural data by applying coarse-grained delta-encoding, and CSR-VI, that targets the reduction of the values using indirect indexing, applicable to matrices with a small number of unique values. Thorough experimental evaluation of the proposed methods and their combination, on two modern shared memory systems, demonstrated that they can significantly improve multithreaded SpMxV performance upon standard and state-of-the-art approaches. © 2010 ACM

Exploiting compression opportunities to improve SpMxV performance on shared memory systems