Michael Heath - US grants

The primary goal of the research is to create more efficient
propagators for atomistic computer simulations
and thus to make possible more ambitious scientific calculations.
New deterministic and stochastic algorithms for both dynamics and sampling
are to be constructed using such techniques as operator splitting, modified
energy functions that compensate for finite steps,
and optimization of method parameters, together with physical insight.
Promising algorithms are tested and compared using mathematical analyses
and computer experiments. Tools for mathematical analysis include the
concept of effective accuracy, the method of modified equations, linear
analysis, and KAM theory. Computer experiments are performed on model
problems chosen to reveal unambiguously the properties of interest.
Faster algorithms are to be implemented in molecular modeling software
being developed for widespread use in a couple of projects at the
University of Illinois Beckman Institute. Most of the work is
sufficiently general that is transfers to other types of problems such
as occur in astrophysics, wave phenomena, and mechanical engineering.
And many of the techniques can be abstracted and applied to generic problems.


Computer simulations of atomic detail are heavily employed in physics,
chemistry, materials science, and structural biology.
These calculations require the generation of sequences of atomic
configurations either for the purpose of modeling actual motion or for the
purpose of calculating averaged values and structures
from a wide range of representative samples. The computing time ranges
from hours to months, so it can benefit tremendously from faster algorithms.
It is the objective of this research project to do this:
to create much more efficient propagators for dynamics
and sampling that reliably achieve acceptable levels of accuracy.
The construction of such algorithms employs ideas from mathematics
and computer science together with physical insight.
Promising algorithms are tested and compared using
mathematical analyses and computer experiments.
The successful ones are implemented in molecular modeling software
being developed for widespread use in a couple of projects at the University
of Illinois Beckman Institute. These advances in methodology are also to be
disseminated in articles targeted to practitioners.
Many of the techniques will apply not only to molecular simulations
but also to simulations in astrophysics, structural mechanics, and fluids.
Potentially, the availability of accelerated propagation algorithms
will lead to a variety of scientific results that otherwise would not be
obtained. The performance of the research will be
a valuable interdisciplinary experience for a graduate student.

DMS-0310446
John C. Hart, Michael T. Heath, Xiangmin Jiao, and John M. Sullivan

This collaborative project funded under DMS-0310642 and
DMS-0310354 is a CARGO full team award made under solicitation
http://www.nsf.gov/pubs/2002/nsf02155/nsf02155.htm.

Level sets represent a moving interface surface implicitly, which
naturally accommodates singularities and topology changes, but at the
expense of a wasteful volumetric Eulerian representation that provides no
control over the topology of the level set. Our project will develop new
methods for tracking interfaces using a more efficient Lagrangian surface
mesh. Through careful prediction of instabilities and topology changes,
we will address the numerical, geometric, and topological difficulties
that have plagued previous Lagrangian approaches to interface propagation.
The result will be faster and more compact simulations of the motion of
interface surfaces, as well as the ability to detect and control changes
in their topology.

Moving boundary or interface surfaces are an integral part of a wide
variety of physical phenomena, such as solidification, extrusion,
multiphase flow, and fluid-solid interaction. Numerical simulations of
such phenomena require reliable and efficient methods for propagating and
tracking such moving surfaces. The focus of our project is to develop
new methods that provide greater control over surface propagation while
also attaining greater computational efficiency than existing methods.
One specific application we will consider is the simulation of combustion
in solid propellant rocket motors, which is the primary focus of UIUC's
Center for Simulation of Advanced Rockets (CSAR). This work will result
in more efficient and more accurate virtual prototyping of rocket motor
designs, leading to safer and more efficient solid rocket boosters,
such as those powering the U.S. Space Shuttle.

The world is populated with enormous amounts of data from a wide variety of sources. There is a compelling human need to represent, analyze, query, manage, understand, and respond to such data for knowledge extraction and decision making. In collaboration with Yahoo! and Hewlett-Packard, we are creating an experimental testbed, the Cloud Computing Testbed (CCT), at the University of Illinois (UIUC) for data-intensive applications using distributed "cloud" computational resources to enable researchers to address this need by processing data at various levels of the system stack, from network, operating system, virtual machines, and distributed applications to the Web. The exploratory nature of CCT results from its focus on systems and networking related research issues within a data-intensive cloud computing environment. Other existing or proposed data processing clusters are focused on user-level applications for which a stable and thus fairly rigid environment must be maintained, whereas the proposed research with the CCT will go deep into the system software stack to explore new and better ways to provide system-level support for data-intensive computing. The UIUC research efforts cover a breadth of research areas including networking, operating systems, virtual machines, distributed systems, data-mining, Web search, network measurements, and multimedia. Access to the CCT is also being made available to external CISE researchers by way of an application process administered by UIUC.

The CCT will provide the academic community with the opportunity to do research in data-intensive computing spanning multiple research areas (OS, virtual machines, distributed systems, datamining, the Web, and online social networks), and in particular to explore powerful systems and networking research topics in a data-intensive environment. It will give the academic community access to resources that would otherwise be unavailable due to cost. The CCT is providing opportunities for multi-disciplinary research on large-scale, distributed computing projects. It is accelerating research for Internet-scale computing and will drive innovation for future systems.

This project develops a testbed for experimentation with energy saving in data centers via holistic management of both the computing and cooling subsystems. It instruments a large computing cluster at the University of Illinois that reproduces representative dynamics of energy consumption in data centers. Understanding heat and energy dynamics in large computing systems requires detailed sensing and control on both the computing and cooling side. The goal is to produce models and algorithms that significantly contribute to energy optimization research leading to reductions in carbon footprint and operating costs of contemporary computing clusters. The testbed developed in this project focuses primarily on understanding and improving software-controlled mechanisms for energy optimization in systems that exhibit non-trivial coupled thermal and computational dynamics, called thermo-computational systems, towards better energy management of data centers. When complete, it is likely to become the first and largest open testbed geared for enabling high quality research on large thermo-computational systems. The project is motivated by the increasing energy cost of data centers, which is estimated at more than $4.5 billion annually and is expected to grow at a rate of 12% in the absence of intervention. According to the EPA, most of this cost is avoidable. If successful, the project will therefore contribute significantly to both the economy and the environment by resulting in savings in both energy cost and carbon footprint.