A manager-worker-based parallelization algorithm for Quantum Monte Carlo (QMC-MW) is presented and compared with the pure iterative parallelization algorithm, which is in common use. The new manager-worker algorithm performs automatic load balancing, allowing it to perform near the theoretical maximal speed even on heterogeneous parallel computers. Furthermore, the new algorithm performs as well as the pure iterative algorithm on homogeneous parallel computers. When combined with the dynamic distributable decorrelation algorithm (DDDA) [Feldmann et al., J Comput Chem 28, 2309 (2007 ], the new manager-worker algorithm allows QMC calculations to be terminated at a prespecified level of convergence rather than upon a prespecified number of steps (the common practice). This allows a guaranteed level of precision at the least cost. Additionally, we show (by both analytic derivation and experimental verification) that standard QMC implementations are not "perfectly parallel" as is often claimed.
A manager-worker-based parallelization algorithm for Quantum Monte Carlo (QMC-MW) is presented at REF on heterogeneous parallel computers and they compared with the pure iterative parallelization algorithm (QMC-PI).
Manager-worker-based model for the parallelization of quantum Monte Carlo on heterogeneous and homogeneous networks.
Journal of computational chemistry
Journal of computational chemistry
Computer Science Medicine