Performance issues of scientific programming in Ada 95
Proceedings of the conference on TRI-Ada '97
Performance optimizations and bounds for sparse matrix-vector multiply
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
IEEE Transactions on Computers
The Journal of Supercomputing
Accelerating sparse matrix computations via data compression
Proceedings of the 20th annual international conference on Supercomputing
Data distribution schemes of sparse arrays on distributed memory multicomputers
The Journal of Supercomputing
Optimizing sparse matrix-vector multiplication using index and value compression
Proceedings of the 5th conference on Computing frontiers
Pattern-based sparse matrix representation for memory-efficient SMVM kernels
Proceedings of the 23rd international conference on Supercomputing
Performance evaluation of the sparse matrix-vector multiplication on modern architectures
The Journal of Supercomputing
Parallel blocked sparse matrix-vector multiplication with dynamic parameter selection method
ICCS'03 Proceedings of the 2003 international conference on Computational science: PartIII
HiPC'08 Proceedings of the 15th international conference on High performance computing
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
EaCRS: an extendible array based compression scheme for high dimensional data
Proceedings of the Second Symposium on Information and Communication Technology
Comparing Different Sparse Matrix Storage Structures as Index Structure for Arabic Text Collection
International Journal of Information Retrieval Research
Fast Recommendation on Bibliographic Networks
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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We analyze single-node performance of sparsematrix-vector multiplication by investigating issues ofdata locality and fine-grained parallelism. We examine the data-locality characteristics of the compressed-sparse-row representation and consider improvementsin locality through matrix permutation. Motivatedby potential improvements in fine-grained parallelism,we evaluate modified sparse-matrix representations.The results lead to general conclusions about improving single-node performance of sparse matrix-vectormultiplication in parallel libraries of sparse iterativesolvers.