**Supernova Science Center (SNSC)**

**Challenges and Collaborations**

#### **Stan Woosley, UCSC**

#### **Adam Burrows, University of Arizona**

#### **Chris Fryer, LANL**

#### **Rob Hoffman, LLNL (plus 21 others)**

***Solving the supernova problem(s) poses a challenge in computational
physics that just a few years ago would have been insurmountable. The
solutions require expertise from many fields as well as innovations in
computational strategy. During its first three years, the SNSC has
emphasized the development of codes and the forging of
inter-disciplinary alliances, but increasingly, we will need the help of
computer scientists and the SciDAC software ISICS.***

![](media/image1.wmf)

Both the explosion of a massive star as a "core-collapse" supernova and
of a white dwarf as a "thermonuclear" supernova (*aka* Type Ia) pose
problems in computational astrophysics that have challenged the
community for decades. The former requires at least a two- (and
ultimately three-) dimensional treatment of multi-energy-group neutrinos
coupled to multi-dimensional hydrodynamics. The latter is a problem in
turbulent (nuclear) combustion.

During its first three years, the SNSC has focused on forging the
interdisciplinary collaborations necessary to do cutting edge problems
in a field that couples astro-physics, particle physics, nuclear
physics, turbulence theory, combustion theory, and radiation transport.
We have also worked on developing and modifying the necessary computer
codes to incorporate the physics efficiently and do exploratory
calculations.

Examples of these alliances are the chemical combustion groups at LBNL
(Bell et al.) and at Sandia (Kerstein et al.); the NSF's Joint Institute
for Nuclear Astrophysics (JINA); and radiation transport experts at LANL
and LLNL. With LBNL, we applied codes previously optimized to study
chemical combustion on large parallel computers to novel problems in
nuclear combustion in white dwarf stars (Fig. 1).

         ![](media/image2.png){width="2.165277777777778in"
height="1.8451388888888889in"}

Figure 1. *A burning Rayleigh-Taylor unstable*

*flame front calculated for conditions inside a*

*Type Ia supernova using the LBNL low-Mach- number, adaptive-mesh code.*

Figure 2. *A portion (Z \< 60) of the isotopes included in our nuclear
reaction library -- one of the most extensive in the world.*

With JINA, we are working to develop a standardized library of nuclear
data for application to the study of nucleosynthesis in stars,
supernovae, and X-ray bursts on neutron stars (Fig. 2).

In our third year of operations, the complexity and scale of our
problems has evolved to the point where we can make good use of the
assistance offered by the SciDAC Integrated Software Infrastructure
Centers. This has begun.

Motivated by the need to devise more efficient solvers, the Arizona team
is working with the TOPS ISIC on pre-conditioners and sparse matrix
inversion.

Preliminary solvers using PETSc are being devised for test Jacobians
extracted from static 2D transport calculations by Burrows and Hubeny
(UA). With members of TOPS, we are exploring, in the context of PETSc,
direct, stationary, accelerated, and multi-level accelerated methods.
For the simpler test problems, the astrophysicists already have
efficient solvers (using ALI and Ng acceleration). The short-term goal,
for spring 2004, is test comparisons of matrix inverters. The next stage
will be extending these tests to the full Jacobian. The long-term goal
is an efficient technique that can compete in speed-per-zone with our
SESAME/ALI method in 1D, and gain a factor of ten in speed in our
multi-dimensional codes.

We are also working with computer scientists on our own team in ways
that would not have happened before SciDAC. The Arizona team has
developed a Parallel

Software Design Model that allows the

designer to visually inspect the computation, idle, and communication
times during the design stage and optimize them, if possible. The design
model involves three steps:

1\) program characterization, 2) parallel-ization, and 3)
implementation. This model is now being applied to our main core-
collapse code.

Our team of computer scientists has also developed *autonomic
partitioning strategies,* in which data structures and adaptive load
balancing are generated. These are based upon the GraCE system developed
by the New Jersey State University at Rutgers. Our Autonomic
Partitioning Framework has three components: 1) services for monitoring
Grid resource capabilities, 2) autonomic runtime management, and 3)
performance analysis modules.
![](media/image3.png){width="3.4194444444444443in"
height="2.5597222222222222in"}

Figure 3. *Application Programmable Visual-ization Toolkit.*

Finally, the team is working on strategies that use prediction
functions, (experiment-ally formulated in terms of the current state of
the system) to optimize the performance of astrophysical applications in
distributed and dynamic execution environments.

**For further information on this subject contact:**

Stan Woosley - woosley@ucolick.org

Department of Astrophysics, UCSC

Phone: 831-459-2976

http://www.supersci.org/