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From Apache Wiki <wikidi...@apache.org>
Subject [Incubator Wiki] Trivial Update of "HamaProposal" by udanax
Date Mon, 21 Apr 2008 03:58:16 GMT
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The following page has been changed by udanax:
http://wiki.apache.org/incubator/HamaProposal

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  Hama will develop a parallel matrix computational package, which provides an library of
matrix operations for the large-scale processing development environment and Map/Reduce framework
for the large-scale 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 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.  
+ 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. And, Hadoop HDFS Files and Map/Reduce can only used by 1d blocked algorithm.
 
  == Rationale ==
  
- Hama approach proposes the use of 3-dimensional Row and Column (Qualifier), Time space and
multi-dimensional 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 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'' or ''iterative methods'', run in O(the number
of non-zero elements in the matrix / number of mappers) time on Hadoop Map/Reduce.
+ Hama approach proposes the use of 3-dimensional Row and Column (Qualifier), Time space and
multi-dimensional 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 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'' or ''iterative methods'',
run in O(the number of non-zero elements in the matrix / number of mappers) time on Hadoop
Map/Reduce.
  
  == Current Status ==
  

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