2013
DOI: 10.1002/cpe.3129
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Improving the scalability of a symmetric iterative eigensolver for multi‐core platforms

Abstract: SUMMARYWe describe an efficient and scalable symmetric iterative eigensolver developed for distributed memory multi‐core platforms. We achieve over 80% parallel efficiency by major reductions in communication overheads for the sparse matrix‐vector multiplication and basis orthogonalization tasks. We show that the scalability of the solver is significantly improved compared to an earlier version, after we carefully reorganize the computational tasks and map them to processing units in a way that exploits the ne… Show more

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Cited by 93 publications
(129 citation statements)
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“…Odd values of N max then correspond to states with "unnatural" parity. These calculations are performed with the code MFDn [14][15][16], a hybrid MPI/OpenMP configuration interaction code for ab initio nuclear structure calculations. The paper is organized as follows.…”
Section: Ab Initio No Core Full Configuration Approachmentioning
confidence: 99%
“…Odd values of N max then correspond to states with "unnatural" parity. These calculations are performed with the code MFDn [14][15][16], a hybrid MPI/OpenMP configuration interaction code for ab initio nuclear structure calculations. The paper is organized as follows.…”
Section: Ab Initio No Core Full Configuration Approachmentioning
confidence: 99%
“…It gives a good description of most narrow states in light nuclei up to about A = 12 [15,16] without additional three-nucleon forces. For our calculations we use the code MFDn [4,5,6,7] which has been demonstrated to scale to over 200,000 cores, and we consider basis spaces with dimensions up to 3.3 billion basis states, and nearly 4 trillion nonzero matrix elements.…”
Section: Recent Results For Be Isotopes With Jisp16mentioning
confidence: 99%
“…Improved algorithms to construct this matrix and to determine its lowest eigenstates, as well as efficient use of increasing computational resources are critical for these successes [3,4,5,6,7,8].…”
Section: No-core Configuration Interaction Approachmentioning
confidence: 99%
“…Sparse matrix vector and and sparse matrix transpose vector products are key kernels in the iterative eigensolver. The sparse matrix is stored in a compressed sparse block coordinate (CSB COO) [1,2] format which allows efficient linear algebra operations on the large sparse matrix. The sparse matrix elements and corresponding indices account for 64 GB of the memory and the input/output vectors account for up to 16 GB depending on the specific problem.…”
Section: Mfdnmentioning
confidence: 99%