5 papers
Advancing the distributed Multi-GPU ChASE library through algorithm optimization and NCCL library
Xinzhe Wu, Edoardo Di Napoli
As supercomputers become larger with powerful Graphics Processing Unit (GPU), traditional direct eigensolvers struggle to keep up with the hardware evolution and scale efficiently…
Computing formation enthalpies through an explainable machine learning method: the case of Lanthanide Orthophosphates solid solutions
Edoardo Di Napoli, Xinzhe Wu, Thomas Bornhake +1
In the last decade, the use of Machine and Deep Learning (MDL) methods in Condensed Matter physics has seen a steep increase in the number of problems tackled and methods employed.…
Hybrid CPU-GPU generation of the Hamiltonian and Overlap matrices in FLAPW methods
Diego Fabregat-Traver, Davor Davidović, Markus Höhnerbach +1
In this paper we focus on the integration of high-performance numerical libraries in ab initio codes and the portability of performance and scalability. The target of our work is F…
Parallel adaptive integration in high-performance functional Renormalization Group computations
Julian Lichtenstein, Jan Winkelmann, David Sánchez de la Peña +2
The conceptual framework provided by the functional Renormalization Group (fRG) has become a formidable tool to study correlated electron systems on lattices which, in turn, provid…
An Optimized and Scalable Eigensolver for Sequences of Eigenvalue Problems
Mario Berljafa, Daniel Wortmann, Edoardo Di Napoli
In many scientific applications the solution of non-linear differential equations are obtained through the set-up and solution of a number of successive eigenproblems. These eigenp…