Sanibel Symposium

Advances in high performance computing (HPC) have provided a way to treat large, computationally demanding tasks using thousands of processors. With the development of more powerful HPC architectures, the need to create efficient and scalable code has grown more important. Electronic structure calculations are valuable in understanding in the experimental observations and are routinely used for new materials predictions. For, the electronic structure calculations, the memory and computation time are proportional to the number of atoms. Memory requirements for these calculations scale as N², where N is the number of atoms. While the recent advances in HPC offer platforms with large numbers of cores, the limited amount of memory available on a given node and poor scalability of the electronic structure code hinder their efficient usage of these platforms.

This work will present development of a mesh-splitting approach to overcome these memory bottlenecks. These developments, which are implemented in the NRLMOL electronic structure code, also make use of sparse matrix storage and MPI-3 shared memory allocations to store large arrays. Scalability measurements of the implementation in terms of strong scaling and weak scaling as well as memory requirements will be presented. Using these developments, we have been able to perform ground state density functional calculations using up to 67,000 basis functions. The developed code was also used to perform the excited state calculations of a light harvesting triad using a large number of explicit solvent molecules.