Solving path problems on the GPU
Parallel Computing
Parallel SimRank computation on large graphs with iterative aggregation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Regularized latent semantic indexing
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
The Combinatorial BLAS: design, implementation, and applications
International Journal of High Performance Computing Applications
The i/o complexity of sparse matrix dense matrix multiplication
LATIN'10 Proceedings of the 9th Latin American conference on Theoretical Informatics
Space-round tradeoffs for MapReduce computations
Proceedings of the 26th ACM international conference on Supercomputing
Regularized Latent Semantic Indexing: A New Approach to Large-Scale Topic Modeling
ACM Transactions on Information Systems (TOIS)
Performance evaluation of sparse matrix products in UPC
The Journal of Supercomputing
Communication optimal parallel multiplication of sparse random matrices
Proceedings of the twenty-fifth annual ACM symposium on Parallelism in algorithms and architectures
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We identify the challenges that are special to parallel sparse matrix-matrix multiplication (PSpGEMM). We show that sparse algorithms are not as scalable as their dense counterparts, because in general, there are not enough non-trivial arithmetic operations to hide the communication costs as well as the sparsity overheads. We analyze the scalability of 1D and 2D algorithms for PSpGEMM. While the 1D algorithm is a variant of existing implementations, 2D algorithms presented are completely novel. Most of these algorithms are based on the previous research on parallel dense matrix multiplication. We also provide results from preliminary experiments with 2D algorithms.