A set of level 3 basic linear algebra subprograms
ACM Transactions on Mathematical Software (TOMS)
Using MPI: portable parallel programming with the message-passing interface
Using MPI: portable parallel programming with the message-passing interface
ScaLAPACK user's guide
LAPACK Users' guide (third ed.)
LAPACK Users' guide (third ed.)
LAPACK Working Note 94: A User''s Guide to the BLACS v1.0
LAPACK Working Note 94: A User''s Guide to the BLACS v1.0
Discovery of climate indices using clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Non-negative tensor factorization with applications to statistics and computer vision
ICML '05 Proceedings of the 22nd international conference on Machine learning
Projected Gradient Methods for Nonnegative Matrix Factorization
Neural Computation
Hierarchical ALS algorithms for nonnegative matrix and 3D tensor factorization
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
On the convergence of the block nonlinear Gauss-Seidel method under convex constraints
Operations Research Letters
Proceedings of the 21st ACM international conference on Information and knowledge management
ParCube: sparse parallelizable tensor decompositions
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
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Increasingly large datasets acquired by NASA for global climate studies demand larger computation memory and higher CPU speed to mine out useful and revealing information. While boosting the CPU frequency is getting harder, clustering multiple lower performance computers thus becomes increasingly popular. This prompts a trend of parallelizing the existing algorithms and methods by mathematicians and computer scientists. In this paper, we take on the task of parallelizing the Nonnegative Tensor Factorization (NTF) method, with the purposes of distributing large datasets into each cluster node and thus reducing the demand on a single node, blocking and localizing the computation at the maximal degree, and finally minimizing the memory use for storing matrices or tensors by exploiting their structural relationships. Numerical experiments were performed on a NASA global sea surface temperature dataset and result factors were analyzed and discussed.