We provide an updated version of the program hex-ecs originally presented in Comput. Phys. Commun.

185 (2014) 2903–2912. The original version used an iterative method preconditioned by the incomplete

LU factorization (ILU), which – though very stable and predictable – requires a large amount of

working memory. In the new version we implemented a ‘‘separated electrons’’ (or ‘‘Kronecker product

approximation’’, KPA) preconditioner as suggested by Bar-On et al., Appl. Num. Math. 33 (2000) 95–104.

This preconditioner has much lower memory requirements, though in return it requires more iterations

to reach converged results. By careful choice between ILU and KPA preconditioners one is able to extend

the computational feasibility to larger calculations.

Secondly, we added the option to run the KPA preconditioner on an OpenCL device (e.g. GPU). GPUs

have generally better memory access times, which speeds up particularly the sparse matrix multiplication.

185 (2014) 2903–2912. The original version used an iterative method preconditioned by the incomplete

LU factorization (ILU), which – though very stable and predictable – requires a large amount of

working memory. In the new version we implemented a ‘‘separated electrons’’ (or ‘‘Kronecker product

approximation’’, KPA) preconditioner as suggested by Bar-On et al., Appl. Num. Math. 33 (2000) 95–104.

This preconditioner has much lower memory requirements, though in return it requires more iterations

to reach converged results. By careful choice between ILU and KPA preconditioners one is able to extend

the computational feasibility to larger calculations.

Secondly, we added the option to run the KPA preconditioner on an OpenCL device (e.g. GPU). GPUs

have generally better memory access times, which speeds up particularly the sparse matrix multiplication.

typ: | article |
---|---|

journal: | Computer Physics Communications |

volume: | 204 |

pages: | 216-2017 |

year: | 2016 |

pacs: | http://dx.doi.org/10.1016/j.cpc.2016.03.020 |