chapter  21
Development of QMCPACK for Exascale Scientific Computing
ByAnouar Benali, David M. Ceperley, Eduardo D'Azevedo, Mark Dewing, Paul R. C. Kent, Jeongnim Kim, Jaron T. Krogel, Ying Wai Li, Ye Luo, Tyler McDaniel, Miguel A. Morales, Amrita Mathuriya, Luke Shulenburger, Norm M. Tubman
Pages 20

This chapter outlines what drives the choice of algorithms used in quantum Monte Carlo (QMC) and QMCPACK. Internally, QMC codes are similar to particle-based classical molecular dynamics codes with the addition of a limited amount of simple dense linear algebra. The scientific possibilities for QMC with exascale computing will lead too many new applications Availability of exascale hardware will naturally alleviate the floating-point limitation on system size, N, but the memory limitations are expected to become more complex. The optimizations were supported and realized through an Intel Parallel Computing Center at Argonne National Laboratory and Sandia National Laboratories and a full article describing the methods can be found in. Unit testing enables projects to scale to a larger number of developers, and provides assurances to developers that changes are not breaking the code. Unit tests also help design by driving refactoring, and can lead to a less tightly-coupled architecture.