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Manuscript Title: Large-Scale Linear Systems Solver using Secondary Storage: Self-energy in hybrid nanostructures
Authors: J.M. Badia, J.L. Movilla, J.I. Climente, M. Castillo, M. Marqués, R. Mayo, E.S. Quintana-Ortí, J. Planelles
Program title: HDSS - (Huge Dense System Solver)
Catalogue identifier: AEHU_v1_0
Distribution format: tar.gz
Journal reference: Comput. Phys. Commun. 182(2011)533
Programming language: Fortran90, C.
Computer: parallel architectures: multiprocessors, computer clusters.
Operating system: Linux/Unix.
Has the code been vectorised or parallelized?: Yes. 4 processors used in the sample tests; tested from 1 to 288 processors.
RAM: 2Gb for the sample tests; tested for up to 80Gb.
Keywords: LU decomposition, Out-of-core, Dielectric confinement, Self-energy.
PACS: 02.60.-x, 73.21.-b, 73.22.-f, 77.22.Ch, 78.67.-n.
Classification: 7.3.

External routines: MPI, BLAS, PLAPACK, POOCLAPACK. PLAPACK and POOCLAPACK are included in the distribution file.

Nature of problem:
Huge scale dense systems of linear equations, Ax = B, beyond standard LAPACK capabilities. Application to calculations of self-energy potential in dielectrically mismatched semiconductor quantum dots.

Solution method:
The linear systems are solved by means of parallelized routines based on the LU factorization, using efficient secondary storage algorithms when the available main memory is insufficient. The self-energy solver relies on an induced charge computation method. The differential equation is discretized to yield linear systems of equations, which we then solve by calling the HDSS library.

Restrictions:
Simple precision. For the self-energy solver, axially symmetric systems must be considered.

Running time:
About 32 minutes to solve a system with approximately 100,000 equations and more than 6,000 right-hand side vectors using a four-node commodity cluster with a total of 32 Intel cores.