5 papers
Data-Driven Spectral Prediction for Accelerating Large-Scale Electronic Structure Calculations
Abhiram Badrinarayanan, Davor Davidovic, Edoardo Di Napoli +4
Simulating large molecular systems comprising thousands of atoms requires highly scalable methodologies. While modern Density Functional Theory (DFT) codes exhibit linear scaling,…
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.…
ChASE -- A Distributed Hybrid CPU-GPU Eigensolver for Large-scale Hermitian Eigenvalue Problems
Xinzhe Wu, Davor Davidovic, Sebastian Achilles +1
As modern massively parallel clusters are getting larger with beefier compute nodes, traditional parallel eigensolvers, such as direct solvers, struggle keeping the pace with the h…
A Parallel Direct Eigensolver for Sequences of Hermitian Eigenvalue Problems with No Tridiagonalization
Shengguo Li, Xinzhe Wu, Jose E. Roman +3
In this paper, a Parallel Direct Eigensolver for Sequences of Hermitian Eigenvalue Problems with no tridiagonalization is proposed, denoted by \texttt{PDESHEP}, and it combines dir…