Implicit application of polynomial filters in a k-step Arnoldi method
SIAM Journal on Matrix Analysis and Applications
The symmetric eigenvalue problem
The symmetric eigenvalue problem
SIAM Journal on Scientific Computing
Benefits of Parallel I/O in Ab Initio Nuclear Physics Calculations
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
A data management system for ab-initio nuclear physics applications
Proceedings of the 19th High Performance Computing Symposia
Nonuniform memory affinity strategy in multithreaded sparse matrix computations
Proceedings of the 2012 Symposium on High Performance Computing
Topology-aware mappings for large-scale eigenvalue problems
Euro-Par'12 Proceedings of the 18th international conference on Parallel Processing
Adjusting process count on demand for petascale global optimization
Parallel Computing
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One of the emerging computational approaches in nuclear physics is the configuration interaction (CI) method for solving the many-body nuclear Hamiltonian in a sufficiently large single-particle basis space to obtain exact answers - either directly or by extrapolation. The lowest eigenvalues and corresponding eigenvectors for very large, sparse and unstructured nuclear Hamiltonian matrices are obtained and used to evaluate additional experimental quantities. These matrices pose a significant challenge to the design and implementation of efficient and scalable algorithms for obtaining solutions on massively parallel computer systems. In this paper, we describe the computational strategies employed in a state-of-the-art CI code MFDn (Many Fermion Dynamics - nuclear) as well as techniques we recently developed to enhance the computational efficiency of MFDn. We will demonstrate the current capability of MFDn and report the latest performance improvement we have achieved. We will also outline our future research directions.