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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.
== Rationale ==
 Hama approach proposes the use of 3dimensional Row and Column (Qualifier), Time space and
multidimensional Columnfamilies of [http://hadoop.apache.org/hbase Hbase], which is able
to store large sparse and various type of matrices (e.g. Triangular Matrix, 3D Matrix, and
etc.). its 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'' or ''iterative methods'', run in O(the number
of nonzero elements in the matrix/ number of mappers) time on Hadoop Map/Reduce.
+ Hama approach proposes the use of 3dimensional Row and Column (Qualifier), Time space and
multidimensional Columnfamilies of [http://hadoop.apache.org/hbase Hbase], which is able
to store large sparse and various type of matrices (e.g. Triangular Matrix, 3D Matrix, and
etc.). its 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'' or ''iterative methods'', run in O(the number
of nonzero elements in the matrix / number of mappers) time on Hadoop Map/Reduce.
== Current Status ==

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