Combinatorial algorithms for integrated circuit layout
Combinatorial algorithms for integrated circuit layout
A high performance algorithm using pre-processing for the sparse matrix-vector multiplication
Proceedings of the 1992 ACM/IEEE conference on Supercomputing
Characterizing the behavior of sparse algorithms on caches
Proceedings of the 1992 ACM/IEEE conference on Supercomputing
An Approximate Minimum Degree Ordering Algorithm
SIAM Journal on Matrix Analysis and Applications
Improving the memory-system performance of sparse-matrix vector multiplication
IBM Journal of Research and Development
Hypergraph-Partitioning-Based Decomposition for Parallel Sparse-Matrix Vector Multiplication
IEEE Transactions on Parallel and Distributed Systems
Improving performance of sparse matrix-vector multiplication
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
A Fine-Grain Hypergraph Model for 2D Decomposition of Sparse Matrices
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Reducing the bandwidth of sparse symmetric matrices
ACM '69 Proceedings of the 1969 24th national conference
On Improving the Performance of Sparse Matrix-Vector Multiplication
HIPC '97 Proceedings of the Fourth International Conference on High-Performance Computing
Automatic multimedia cross-modal correlation discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Research Paper Recommender Systems: A Random-Walk Based Approach
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Cache-Oblivious Sparse Matrix-Vector Multiplication by Using Sparse Matrix Partitioning Methods
SIAM Journal on Scientific Computing
Increasing data reuse of sparse algebra codes on simultaneous multithreading architectures
Concurrency and Computation: Practice & Experience
A Unified Framework for Link Recommendation Using Random Walks
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Personalized PageRank vectors for tag recommendations: inside FolkRank
Proceedings of the fifth ACM conference on Recommender systems
Reduced-Bandwidth Multithreaded Algorithms for Sparse Matrix-Vector Multiplication
IPDPS '11 Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium
Two-dimensional cache-oblivious sparse matrix-vector multiplication
Parallel Computing
TheAdvisor: a webservice for academic recommendation
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
Towards a personalized, scalable, and exploratory academic recommendation service
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Graphs and matrices are widely used in algorithms for social network analyses. Since the number of interactions is much less than the possible number of interactions, the graphs and matrices used in the analyses are usually sparse. In this paper, we propose an efficient implementation of a sparse-matrix computation which arises in our publicly available citation recommendation service called the advisor. The recommendation algorithm uses a sparse matrix generated from the citation graph. We observed that the nonzero pattern of this matrix is highly irregular and the computation suffers from high number of cache misses. We propose techniques for storing the matrix in memory efficiently and reducing the number of cache misses. Experimental results show that our techniques are highly efficient on reducing the query processing time which is highly crucial for a web service.