# Getting Started

**Hama** is 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.

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) and Time 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.) and utilize
the 2D blocked algorithm. 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)
time on Hadoop Map/Reduce.