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== Introduction ==
 '''Hama''' is a parallel matrix computational package based on Hadoop Map/Reduce. It will
be useful for a massively largescale ''Numerical Analysis'' and ''Data Mining'', which need
the intensive computation power of matrix inversion, e.g. linear regression, PCA, SVM and
etc. It will be also useful for many scientific applications, e.g. physics computations, linear
algebra, computational fluid dynamics, statistics, graphic rendering and many more.
+ '''Hama''' is a parallel matrix computational package based on Hadoop Map/Reduce. In Korean,
Hama is pronunciation of Hippo (하마). It will be useful for a massively largescale ''Numerical
Analysis'' and ''Data Mining'', which need the intensive computation power of matrix inversion,
e.g. linear regression, PCA, SVM and etc. It will be also useful for many scientific applications,
e.g. physics computations, linear algebra, computational fluid dynamics, statistics, graphic
rendering and many more.
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. Triangle 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 ===
