186 search results - page 16 / 38 » Run-Time Techniques for Parallelizing Sparse Matrix Problems |
AAAI
2010
13 years 9 months ago
2010
Matrix factorization is a fundamental technique in machine learning that is applicable to collaborative filtering, information retrieval and many other areas. In collaborative fil...
SPAA
1998
ACM
13 years 11 months ago
1998
ACM
Sparse LU factorization with partial pivoting is important for many scienti c applications and delivering high performance for this problem is di cult on distributed memory machin...
ISCAS
2008
IEEE
14 years 1 months ago
2008
IEEE
— Non-negative matrix factorization (NMF), i.e. V ≈ WH where both V, W and H are non-negative has become a widely used blind source separation technique due to its part based r...
AMC
2010
13 years 7 months ago
2010
: Parallel iterative methods are powerful tool for solving large system of linear equations (LEs). The existing parallel computing research results are focussed mainly on sparse sy...
IPPS
2009
IEEE
14 years 2 months ago
2009
IEEE
In this paper we explore the impact of the block shape on blocked and vectorized versions of the Sparse Matrix-Vector Multiplication (SpMV) kernel and build upon previous work by ...