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Currently, several sharedmemory based parallel matrix solutions can provide a scalable
and high performance matrix operations, but matrix resources can not be scalable in the term
of complexity. The '''Hama''' approach proposes the use of 2dimensional Row and Column(Qualifier)
space and multidimensional Columnfamilies of Hbase, which is able to store large sparse and
various type of matrices (e.g. Triangular Matrix, 3D Matrix, and etc.). In addition, autopartitioned
sparsity substructure will be efficiently managed and serviced by Hbase. Row and Column operations
can be done in lineartime, where several algorithms such as structured Gaussian elimination
and iterative methods run in O(~the number of nonzero elements in the matrix~ / ~number
of mappers (processors/cores)~) time on Hadoop Map/Reduce.
=== Initial Contributors ===
 * [:udanax:Edward Yoon] (R&D center, NHN corp.)
 * Chanwit Kaewkasi (Ph.D candidate, University of Manchester)
+ * Edward Yoon ([mailto:edward@udanax.org edward AT SPAMFREE udanax DOT org])
+ * Chanwit Kaewkasi ([mailto:chanwit@gmail.com chanwit AT SPAMFREE gmail DOT com])
=== Initial Source ===
* http://code.google.com/p/hama/source/checkout
=== Dependencies ===
