Exploiting compression opportunities to improve SpMxV performance on shared memory systems
ACM Transactions on Architecture and Code Optimization (TACO)
CSX: an extended compression format for spmv on shared memory systems
Proceedings of the 16th ACM symposium on Principles and practice of parallel programming
Exploiting dense substructures for fast sparse matrix vector multiplication
International Journal of High Performance Computing Applications
Sparse matrix-vector multiply on the HICAMP architecture
Proceedings of the 26th ACM international conference on Supercomputing
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The Sparse Matrix-Vector Multiplication kernel exhibits limited potential for taking advantage of modern shared memory architectures due to its large memory bandwidth requirements. To decrease memory contention and improve the performance of the kernel we propose two compression schemes. The first, called CSR-DU, targets the reduction of the matrix structural data by applying coarse grain delta encoding for the column indices. The second scheme, called CSR-VI, targets the reduction of the numerical values using indirect indexing and can only be applied to matrices which contain a small number of unique values. Evaluation of both methods on a rich matrix set showed that they can significantly improve the performance of the multithreaded version of the kernel and achieve good scalability for large matrices.