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http://wiki.apache.org/hadoop/Hama

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 ===
 * Edward Yoon (R&D center, NHN corp.)
+ * [:udanax:Edward Yoon] (R&D center, NHN corp.)
* Chanwit Kaewkasi (Ph.D candidate, University of Manchester)
=== Dependencies ===
* Hadoop (HDFS, Map/Reduce) License: Apache License, 2.0
@@ 26, +26 @@
* Crout Decomposition
* Doolittle Decomposition
=== API & Groovy Support ===
 The '''Hama''' project Utilities Groovy for simplification of computational language with
generalized the matrix Java Interface.
+ The '''Hama''' project utilities Groovy for simplification of computational language with
generalized the matrix Java Interface.
For example, we can perform a parallel matrix multiplication by expressing as follows:
{{{
Java API :
