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» Sparse matrix factorization on massively parallel computers
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90
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AC
2008
Springer
14 years 11 months ago
Distributed Sparse Matrices for Very High Level Languages
Sparse matrices are first class objects in many VHLLs (very high level languages) used for scientific computing. They are a basic building block for various numerical and combinat...
John R. Gilbert, Steve Reinhardt, Viral Shah
IPPS
2008
IEEE
15 years 6 months ago
Accurately measuring collective operations at massive scale
Accurate, reproducible and comparable measurement of collective operations is a complicated task. Although Different measurement schemes are implemented in wellknown benchmarks, m...
Torsten Hoefler, Timo Schneider, Andrew Lumsdaine
ICML
2010
IEEE
15 years 22 days ago
A Simple Algorithm for Nuclear Norm Regularized Problems
Optimization problems with a nuclear norm regularization, such as e.g. low norm matrix factorizations, have seen many applications recently. We propose a new approximation algorit...
Martin Jaggi, Marek Sulovský
86
Voted
ICCV
2005
IEEE
16 years 1 months ago
Learning Non-Negative Sparse Image Codes by Convex Programming
Example-based learning of codes that statistically encode general image classes is of vital importance for computational vision. Recently, non-negative matrix factorization (NMF) ...
Christoph Schnörr, Matthias Heiler
JPDC
2008
135views more  JPDC 2008»
14 years 11 months ago
Parallel block tridiagonalization of real symmetric matrices
Two parallel block tridiagonalization algorithms and implementations for dense real symmetric matrices are presented. Block tridiagonalization is a critical pre-processing step for...
Yihua Bai, Robert C. Ward