Alumni Project

Supernova Science Center (SNSC)

Stan Woosley, UCSC
Adam Burrows, University of Arizona
Chris Fryer, LANL
Rob Hoffman, LLNL (plus 21 others)

Summary

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.

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).

          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 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.
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.

Figure 3. Application Programmable Visual-ization Toolkit.
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/

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