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Currently, several shared-memory 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 2-dimensional Row and Column(Qualifier) space and multi-dimensional 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, auto-partitioned sparsity sub-structure will be efficiently managed and serviced by Hbase. Row and Column operations can be done in linear-time, where several algorithms such as structured Gaussian elimination and iterative methods run in O(~-the number of non-zero 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 ===