[ https://issues.apache.org/jira/browse/HADOOP2878?page=com.atlassian.jira.plugin.system.issuetabpanels:alltabpanel
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Edward Yoon updated HADOOP2878:

Description:
*Introduction*
Hama will develop a highperformance and largescale parallel matrix computational package
based on Hadoop Map/Reduce. It will be useful for a massively 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.
Hama approach proposes the use of 3dimensional Row and Column (Qualifier), Time space and
multidimensional Columnfamilies of Hbase (BigTable Clone), which is able to store large sparse
and various type of matrices (e.g. Triangular Matrix, 3D Matrix, and etc.). 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.
So, it has a strong relationship with the hadoop project, and it would be great if the "hama"
can become a contrib project of the hadoop
*Current Status*
In its current state, the 'hama' is buggy and needs filling out, but generalized matrix interface
and basic linear algebra operations was implemented within a large prototype system. In the
future, We need new parallel algorithms based on Map/Reduce for performance of heavy decompositions
and factorizations. It also needs tools to compose an arbitrary matrix only with certain data
filtered from hbase array structure.
It would be great if we can collaboration with the hadoop members.
*Members*
We have a master's (or Ph.D) degrees in the mathematics and computer science.
 Edward Yoon (edward AT udanax DOT org)
 Chanwit Kaewkasi (chanwit AT gmail DOT com)
 Min Cha (minslovey AT gmail DOT com)
 Antonio Suh (bluesvm AT gmail DOT com)
At least, I and Min Cha will be involved fulltime with this work.
was:
*Introduction*
Hama will develop a highperformance and largescale parallel matrix computational package
based on Hadoop Map/Reduce. It will be useful for a massively 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.
Hama approach proposes the use of 3dimensional Row and Column (Qualifier), Time space and
multidimensional Columnfamilies of Hbase (BigTable Clone), which is able to store large sparse
and various type of matrices (e.g. Triangular Matrix, 3D Matrix, and etc.). 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.
So, it has a strong relationship with the hadoop project, and it would be great if the "hama"
can become a contrib project of the hadoop
*Current Status*
In its current state, the 'hama' is buggy and needs filling out, but generalized matrix interface
and basic linear algebra operations was implemented within a large prototype system. In the
future, We need new parallel algorithms based on Map/Reduce for performance of heavy decompositions
and factorizations. It also needs tools to compose an arbitrary matrix only with certain data
filtered from hbase array structure.
It would be great if we can collaboration with the hadoop members.
*Members*
The initial set of committers includes folks from the Hadoop & Hbase communities, and
We have a master's (or Ph.D) degrees in the mathematics and computer science.
 Edward Yoon (edward AT udanax DOT org)
 Chanwit Kaewkasi (chanwit AT gmail DOT com)
 Min Cha (minslovey AT gmail DOT com)
At least, I and Min Cha will be involved fulltime with this work.
Antonio Suh was joined to this project. He is my fellow worker.
> Hama code contribution
> 
>
> Key: HADOOP2878
> URL: https://issues.apache.org/jira/browse/HADOOP2878
> Project: Hadoop Core
> Issue Type: New Feature
> Environment: All environment
> Reporter: Edward Yoon
> Assignee: Edward Yoon
> Priority: Minor
> Attachments: hama.tar.gz
>
>
> *Introduction*
> Hama will develop a highperformance and largescale parallel matrix computational package
based on Hadoop Map/Reduce. It will be useful for a massively 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.
> Hama approach proposes the use of 3dimensional Row and Column (Qualifier), Time space
and multidimensional Columnfamilies of Hbase (BigTable Clone), which is able to store large
sparse and various type of matrices (e.g. Triangular Matrix, 3D Matrix, and etc.). 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.
> So, it has a strong relationship with the hadoop project, and it would be great if the
"hama" can become a contrib project of the hadoop
> *Current Status*
> In its current state, the 'hama' is buggy and needs filling out, but generalized matrix
interface and basic linear algebra operations was implemented within a large prototype system.
In the future, We need new parallel algorithms based on Map/Reduce for performance of heavy
decompositions and factorizations. It also needs tools to compose an arbitrary matrix only
with certain data filtered from hbase array structure.
> It would be great if we can collaboration with the hadoop members.
> *Members*
> We have a master's (or Ph.D) degrees in the mathematics and computer science.
>  Edward Yoon (edward AT udanax DOT org)
>  Chanwit Kaewkasi (chanwit AT gmail DOT com)
>  Min Cha (minslovey AT gmail DOT com)
>  Antonio Suh (bluesvm AT gmail DOT com)
> At least, I and Min Cha will be involved fulltime with this work.

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