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http://wiki.apache.org/hadoop/Hama
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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. Triangle 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 ===
- * 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 :