Enabling Computational Technologies for Terascale Scientific Simulations
We develop scalable algorithms and object-oriented code frameworks for terascale scientific simulations on massively parallel processors (MPPs). Our research in multigrid-based linear solvers and adaptive mesh refinement enables Laboratory programs to use MPPs to explore important physical phenomena. For example, our research aids stockpile stewardship by making practical detailed 3D simulations of radiation transport. The need to solve large linear systems arises in many applications, including radiation transport, structural dynamics, combustion, and flow in porous media. These systems result from discretizations of partial differential equations on computational meshes. Our first research objective is to develop multigrid preconditioned iterative methods for such problems and to demonstrate their scalability on MPPs. Scalability describes how total computational work grows with problem size; it measures how effectively additional resources can help solve increasingly larger problems. Many factors contribute to scalability: computer architecture, parallel implementation, and choice of algorithm. Scalable algorithms have been shown to decrease simulation times by several orders of magnitude.
- Research Organization:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE Office of Defense Programs (DP) (US)
- DOE Contract Number:
- W-7405-Eng-48
- OSTI ID:
- 802085
- Report Number(s):
- UCRL-ID-140219; TRN: US200223%%721
- Resource Relation:
- Other Information: PBD: 24 Aug 2000
- Country of Publication:
- United States
- Language:
- English
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