School of Electrical Engineering and
Computer Science

Washington State University

We are in an age
when massive digital data continues to be collected at an extraordinarily rapid
rate and with high and growing complexity (and concomitant uncertainty), when
the data and the actors behind it are increasingly interconnected, and when architectures
of computing platforms continue to rapidly change. These circumstances (data
richness and complexity; interconnectedness; computing platform advances)
collectively afford us with an unprecedented potential for discovery and
innovation, but they also pose tremendous challenges for the design and
implementation of algorithms that are up to the task of the required data
analysis. Fast, robust and scalable algorithms are acutely needed for the
purpose of analyzing massive datasets and extracting knowledge and insight from
the data. For many problems, such algorithms do not exist, and when they exist,
they are a poor match to the task and need fundamental re-examination.

This project,
named FASCADA, targets a segment in this grand challenge that spans a very wide
spectrum. FASCADAÕs overarching goal is to explore the interplay between *graph* and *matrix *algorithms in order to develop methods for data analytics
that perform at scale on contemporary platforms, with a primary focus on data
that are expressed in terms of networks. Its specific aims are organized under
four intertwining areas:

1.
*Enabling Scalable Data Analytics*.

Devise
novel Òproblem-partitioningÓ methods that are useful for solving, at scale,
optimization problems underlying a large class of machine learning algorithms.

2.
*Network Analysis*.

Develop
fast algorithms for discovering -- and analyzing -- dense sub-graphs in
real-world networks arising from diverse domains.

3.
*High Performance Computing.*

Develop
effective paradigms for the parallelization of inherently sequential graph
algorithms targeting many-core architectures.

4.
*Algorithmic Differentiation (AD). *

Advance
AD as a technology by designing better graph-based algorithms for Hessian
computation, and use AD in emerging applications, including quantifying
uncertainty.

Two new
innovative courses, an undergraduate course on Data Science and a graduate
course on Network Science, will be developed and taught as part of the FASCADA
project. Students at both the graduate and undergraduate level will be engaged
in the research, and they will be trained in computational modeling, algorithm
design, and software development. The project will also reach out to
underrepresented minority groups in computing sciences and engineering, to
increase their participation, via two existing mechanisms in the Pacific
Northwest (the Louis Stokes Alliance for Minority Participation (LSAMP)
initiative and a partnership with Heritage University).

An overview of the Research and Education
plan in FASCADA.

A common thread
that runs through all four of the research Aims of FASCADA is a focus on graph
problems and their solution. Graph algorithms have long played a crucial role
not only in computer science, but also in scientific computing and the
engineering disciplines. Indeed, the area of *combinatorial scientific computing* is founded on the core idea of
effectively applying graph and other combinatorial algorithms in scientific
computing, where the computations predominantly involve matrices. This
perspective has led to much progress in the development of a) efficient methods
that exploit structural properties of problems and b) tools that enable
applications make better use of high performance computing. In the other
direction, there is a range of examples in which matrix algorithms are the
engines behind network (graph) computations. Examples include: Google's
PageRank algorithm, random walks on graphs, graph partitioning, socio-metric
analysis, and many more.

The work to be
carried out in FASCADA seeks to traverse *both*
of these directions (*matrix* to *graph* and *graph* to *matrix)* in the
context of the targeted problems. It strives to advance fundamental knowledge
at the intersection of a range of areas, including data science, computational
science and engineering, computational mathematics, and high performance
computing. Algorithmic progress made through the project will be realized in
software implementations, will be integrated with existing software tools when
applicable, and will be made available to the wider community as open-source
software. Research results will be widely disseminated to the scientific
community through publications.