 |
|
Success Stories
|
Sparse Matrices & Large Graphs
Large-scale computation on graphs are fundamental to the
representation, manipulation, exploration, analysis, and
visualization of data and interrelationships in many parts
of information retrieval, computational biology, web search,
information analytics, and knowledge discovery. Sparse
matrix computations allow structured representation of
irregular data structures and decompositions, and irregular
access patterns in parallel applications.
Sparse matrices frequently have dimensions that are in
the millions or more, and so many non-zeros that they
cannot fit on one workstation. Furthermore, even when
the sparse matrices are themselves not too large, the
fill-in caused by intermediate operations (e.g., LU factorization)
makes it necessary to distribute the factors over several
processors. |
|
 |
|
Star-P provides a simple way to express graphs as sparse
matrices and provides the infrastructure to manipulate
large graphs in the same way as in MATLAB®. Graph algorithm
users can use Star-P to explore their graphs, e.g.,
looking at partitionings or connected components. Analysts
can prototype algorithms in Star-P and/or plug in their
existing MPI, OpenMP, or UPC libraries into Star-P as
a test-bed for creating test cases, exploring the output
and comparing with other algorithms. Star-P provides
an interactive problem-solving environment for rapid
development of graph-theoretic applications, and executes
those applications with scalable performance on very
large (multi-terabyte) memory systems.
View
Larger Image >
|
 Summary
& Metrics
- Explore large graphs using familiar MATLAB® environment
- Work interactively and explore patterns and connections
within enormous data sets
- Scale to hundreds of processors and multi-terabyte
data sets
 An
Interactive Environment
to Manipulate Large Graphs >
PDF download of Paper
by Gilbert, Shah & Reinhardt
|
|
Back to Success Stories >
|
 |
| |
|
 |
|
|
|
Star-P Support
Blog
Email
Page
Star-P Brochure (PDF - 3MB)
