New developments in SIESTA for high-performance materials simulations
22 September 2020 - 22 September 2020
Online, Italy
Registration is now open!
For over two-decades, SIESTA has enabled the treatment of large systems with first-principles electronic-structure methods, bringing new opportunities to many disciplines. At the core of SIESTA's efficiency is the use of a basis set of strictly-localized atomic orbitals. The reduced cardinality of the basis and the sparsity of the Hamiltonian means that systems composed of dozens to hundreds of atoms can be treated with modest hardware, and that the programme can employ novel algorithms to extend its applicability to even larger systems. Within the MaX project, which is preparing materials-simulation codes for the upcoming extreme-scale HPC systems, this baseline efficiency has been extended alongside the domain of applicability of SIESTA with the addition of new features. These extensions will be the focus of this webinar.
In this webinar we will highlight the performance enhancements and new features recently added to SIESTA within the MaX project, one of its flagship codes. The traditional high efficiency of the programme, stemming from its use of a finite-support atomic-orbital basis set, has been extended to leading-edge HPC systems by new solver options while its built-in subsystem for electronic transport (TranSIESTA) has undergone a major redesign for increased performance and functionality.
Visit the official webinar page to register and see the exciting agenda prepared for its attendees.
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About MaX
MaX - Materials design at the Exascale has received funding from the European Union's Horizon 2020 under Grant Agreement n. 824143. Led by CNR (Italy), the MaX consortium partners include SISSA (Italy), ICN2 (Spain), JUELICH (Germany), CEA (France), EPFL (Switzerland), Universiteit Gent (Belgium), CINECA (Italy), BSC (Spain), ETHZurich (Switzerland), E4 (Italy), ARM (United Kingdom), ICTP (Italy), Trust-IT (Italy).
www.max-centre.eu I www.linkedin.com/company/max-centre I @max_center2
For over two-decades, SIESTA has enabled the treatment of large systems with first-principles electronic-structure methods, bringing new opportunities to many disciplines. At the core of SIESTA's efficiency is the use of a basis set of strictly-localized atomic orbitals. The reduced cardinality of the basis and the sparsity of the Hamiltonian means that systems composed of dozens to hundreds of atoms can be treated with modest hardware, and that the programme can employ novel algorithms to extend its applicability to even larger systems. Within the MaX project, which is preparing materials-simulation codes for the upcoming extreme-scale HPC systems, this baseline efficiency has been extended alongside the domain of applicability of SIESTA with the addition of new features. These extensions will be the focus of this webinar.
In this webinar we will highlight the performance enhancements and new features recently added to SIESTA within the MaX project, one of its flagship codes. The traditional high efficiency of the programme, stemming from its use of a finite-support atomic-orbital basis set, has been extended to leading-edge HPC systems by new solver options while its built-in subsystem for electronic transport (TranSIESTA) has undergone a major redesign for increased performance and functionality.
Visit the official webinar page to register and see the exciting agenda prepared for its attendees.
---
About MaX
MaX - Materials design at the Exascale has received funding from the European Union's Horizon 2020 under Grant Agreement n. 824143. Led by CNR (Italy), the MaX consortium partners include SISSA (Italy), ICN2 (Spain), JUELICH (Germany), CEA (France), EPFL (Switzerland), Universiteit Gent (Belgium), CINECA (Italy), BSC (Spain), ETHZurich (Switzerland), E4 (Italy), ARM (United Kingdom), ICTP (Italy), Trust-IT (Italy).
www.max-centre.eu I www.linkedin.com/company/max-centre I @max_center2
Keywords
High-Performance Computing (HPC), Materials design, materials modelling, materials simulation, High Throughput Computing (HTC), materials science, data analytics, computational algorithms, HPC libraries