Αραιοί πίνακες (Greek Wikipedia)

Analysis of information sources in references of the Wikipedia article "Αραιοί πίνακες" in Greek language version.

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anl.gov

Argonne National Laboratory Deploys Cerebras CS-1, the World's Fastest Artificial Intelligence Computer | Argonne National Laboratory. Δελτίο τύπου.

archive.org

Saad, Yousef (2003). Iterative methods for sparse linear systems. SIAM.

berkeley.edu

people.eecs.berkeley.edu

Buluç, Aydın; Fineman, Jeremy T.; Frigo, Matteo; Gilbert, John R.; Leiserson, Charles E. (2009). «Parallel sparse matrix-vector and matrix-transpose-vector multiplication using compressed sparse blocks». ACM Symp. on Parallelism in Algorithms and Architectures. https://people.eecs.berkeley.edu/~aydin/csb2009.pdf.

businesswire.com

«Cerebras Systems Unveils the Industry's First Trillion Transistor Chip». www.businesswire.com (στα Αγγλικά). 19 Αυγούστου 2019. Ανακτήθηκε στις 2 Δεκεμβρίου 2019. The WSE contains 400,000 AI-optimized compute cores. Called SLAC™ for Sparse Linear Algebra Cores, the compute cores are flexible, programmable, and optimized for the sparse linear algebra that underpins all neural network computation

doi.org

dx.doi.org

Yan, Di; Wu, Tao; Liu, Ying; Gao, Yang (2017). «2017 IEEE 17th International Conference on Communication Technology (ICCT)». IEEE, pp. 1880–1883. doi:10.1109/icct.2017.8359956. ISBN 978-1-5090-3944-9. «The computation kernel of DNN is large sparse-dense matrix multiplication. In the field of numerical analysis, a sparse matrix is a matrix populated primarily with zeros as elements of the table. By contrast, if the number of non-zero elements in a matrix is relatively large, then it is commonly considered a dense matrix. The fraction of zero elements (non-zero elements) in a matrix is called the sparsity (density). Operations using standard dense-matrix structures and algorithms are relatively slow and consume large amounts of memory when applied to large sparse matrices.»
Bank, Randolph E.; Douglas, Craig C. (1993), «Sparse Matrix Multiplication Package (SMMP)», Advances in Computational Mathematics 1: 127–137, doi:10.1007/BF02070824, http://www.mgnet.org/~douglas/Preprints/pub0034.pdf, ανακτήθηκε στις 2024-01-25

dtic.mil

apps.dtic.mil

Eisenstat, S. C.· Gursky, M. C.· Schultz, M. H.· Sherman, A. H. (Απριλίου 1977). «Yale Sparse Matrix Package» (PDF) (στα English). Αρχειοθετήθηκε (PDF) από το πρωτότυπο στις 6 Απριλίου 2019. Ανακτήθηκε στις 6 Απριλίου 2019. CS1 maint: Μη αναγνωρίσιμη γλώσσα (link)

mgnet.org

Bank, Randolph E.; Douglas, Craig C. (1993), «Sparse Matrix Multiplication Package (SMMP)», Advances in Computational Mathematics 1: 127–137, doi:10.1007/BF02070824, http://www.mgnet.org/~douglas/Preprints/pub0034.pdf, ανακτήθηκε στις 2024-01-25

scipy.org

docs.scipy.org

web.archive.org

Eisenstat, S. C.· Gursky, M. C.· Schultz, M. H.· Sherman, A. H. (Απριλίου 1977). «Yale Sparse Matrix Package» (PDF) (στα English). Αρχειοθετήθηκε (PDF) από το πρωτότυπο στις 6 Απριλίου 2019. Ανακτήθηκε στις 6 Απριλίου 2019. CS1 maint: Μη αναγνωρίσιμη γλώσσα (link)

wikipedia.org

en.wikipedia.org

Buluç, Aydın; Fineman, Jeremy T.; Frigo, Matteo; Gilbert, John R.; Leiserson, Charles E. (2009). «Parallel sparse matrix-vector and matrix-transpose-vector multiplication using compressed sparse blocks». ACM Symp. on Parallelism in Algorithms and Architectures. https://people.eecs.berkeley.edu/~aydin/csb2009.pdf.