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Hama will develop a parallel matrix computational package, which provides an library of
matrix operations for the largescale processing development environment and Map/Reduce framework
for the 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.
== Background ==
 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. And, Hadoop HDFS Files and Map/Reduce can only used by 1d blocked algorithm.
+ 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. And, Hadoop HDFS Files and Map/Reduce can only used by 1D blocked algorithm.
== 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.) and utilize the 2D blocked algorithm. 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.

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