Quantum ESPRESSO (QE) is an integrated suite of Open-Source computer codes for electronic-structure calculations and materials modeling at the nanoscale. It is based on density-functional theory, plane waves, and pseudopotentials. Quantum ESPRESSO stands for opEn Source Package for Research in Electronic Structure, Simulation, and Optimization. QE is evolving towards a distribution of independent and inter-operable codes in the spirit of an open-source project. 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.
A new build of the GPU-accelerated Quantum ESPRESSO version 5.0.1 (build2) is available for download. This new build includes few improvements:
- - update of all CUDA kernels in order to get 10~15% more performance running new TESLA K10/K20 card
- - phiGEMM 1.9.9
- - MAGMA now compile and works on CRAY XK6 systems (using PGI + ACML 4.4.0)
- - the recently introduced ELPA library (by adding manually __ELPA_PHIGEMM it is possible to couple ELPA and phiGEMM) very useful for large parallel gamma point calculations
We describe the different activities performed to enable the Quantum ESPRESSO user community to challenge frontiers of science running extreme computing simulation on European Tier-0 system of current and next generation. There main sections are described: 1) the improvement of parallelization efficiency on two DTF-based applications: Nuclear Magnetic Resonance (NMR) and EXact-eXchange (EXX) calculation; 2) introduction of innovative van der Waals interaction at the ab-initio level; 3) porting of PWscf code to hybrid system equipped with NVIDIA GPU technology.
The high complexity of supercomputing infrastructure expose scientific community to largely improve legacy code to efficiently perform large-scale simulation. This concept becomes even more strength if considering the introduction of accelerators on top of general purpose CPU systems. On the other hand the constant upgrade of complex scientific code requires to both developers and scientists a huge effort, large amount of computational resources and remarkable expertise in high performance computing.