Abstract
With advances in algorithms and growing computing powers, quantum Monte Carlo
(QMC) methods have become a leading contender for high accuracy calculations
for the electronic structure of realistic systems. The performance gain on
recent HPC systems is largely driven by increasing parallelism: the number of
compute cores of a SMP and the number of SMPs have been going up, as the
Top500 list attests. However, the available memory as well as the
communication and memory bandwidth per element has not kept pace with the increasing
parallelism. This severely limits the applicability of QMC and the problem size
it can handle. OpenMP/MPI hybrid programming provides applications with simple
but effective solutions to overcome efficiency and scalability bottlenecks on
large-scale clusters based on multi/many-core SMPs. We discuss the design and
implementation of hybrid methods in QMCPACK and analyze its performance on
current HPC platforms characterized by various memory and communication
hierarchies.