Intranet
Several applications are considered in EPEEC: AVBP (Cerfacs, a numerical simulation framework for the study of fluid dynamics and combustion problems), DIOGENeS (Inria, a numerical simulation framework for the study of nanoscale light-matter interaction problems), OSIRIS (INESC-ID, a numerical simulation framework for the study of plasma physics problems), Quantum ESPRESSO (Cineca, a set of numerical tools for the study of electronic properties of materials) and SMURFF (IMEC, a Bayesian matrix factorization framework for building recommender systems with applications to life sciences). The present news focuses on the Quantum Espresso application.
A material is a substance, usually a solid, that can have different applications. Our society exploits materials widely, from the clothes we wear to the vehicles in which we travel. The applicability of a material for a particular use, for example as a semiconductor in electronics or a metal alloy in an engineering structure, depends on its properties. These in turn are related to the structure of the material and the aim of materials science is to understand these structure-property relationships. With this understanding, it may be possible, for example, to improve the characteristics of current materials, such as superconductors to make them operate at higher temperatures, or design completely new ones.
New insights into material behaviour can be obtained by computer simulation with the result that the time required to design new materials can be greatly reduced. Although simulation methods such as molecular dynamics, Monte Carlo, etc. have been developed for many years, high quality simulations of materials at the atomic level have been available only relatively recently. The reason is that in order to obtain useful information, the electronic structure of a material needs to be modelled and this requires calculations based on quantum mechanics, rather than on classical mechanics, which is computationally less expensive. However, thanks to developments in a computational quantum mechanical modelling method called density functional theory (DFT), and increasing computational power, electronic structure calculations based on DFT are now common and play an important role in materials science.
Quantum ESPRESSO is an integrated suite of Open Source computer codes for electronic-structure calculations and materials modelling at the nanoscale. It is based on DFT, plane waves, and pseudopotentials. Quantum ESPRESSO has evolved into a distribution of independent and interoperable codes in the spirit of an Open Source project. The Quantum ESPRESSO distribution consists of a “historical” core set of components, and a set of plug-ins that perform more advanced tasks, plus a number of third-party packages designed to be interoperable with the core components. Researchers active in the field of electronic structure calculations are encouraged to participate in the project by contributing their own codes or by implementing their own ideas into existing codes. Quantum ESPRESSO is an open initiative in collaboration with many groups worldwide and coordinated by the Quantum ESPRESSO Foundation. Present members of the latter include Scuola Internazionale Superiore di Studi Avanzati (SISSA), the Abdus Salam International Centre for Theoretical Physics (ICTP), the CINECA National Supercomputing Center, the Ecole Polytechnique Fédérale de Lausanne (EPFL), the University of North Texas (UNT) and University of Oxford.
The application code in Quantum ESPRESSO has been extensively optimised at many levels but still presents inefficiencies, which limit the complexities of the materials that can be studied and the accuracies of the calculations. In particular, the Fast Fourier Transform (FFT) algorithm, used widely in the application, represents a bottleneck when used with large numbers of processors because of the communication costs.
In EPEEC, we are working to remove or at least reduce these bottlenecks, to enlarge the range of materials that can be simulated, and to do so with higher accuracies. Progress on reducing the inefficiencies in the parallel computations by using an alternative to the MPI model has been demonstrated with a consequent shorter time-to-solution, particularly on massively parallel architectures.
