Accelerating quantum Monte Carlo simulations on many-core processors via OpenMP nested threading

Akila Gothandaraman

PETER J. REYNOLDS

The Z6 scaling of computational effort in quantum Monte Carlo (QMC) results in part from a type of critical slowing down. This insight leads to an attack based on acceleration algorithms which modify the dynamics in Langevin-based simulations without changing the steady-state. Configuration space is more rapidly sampled and steps are inherently more decorrelated. A second approach uses a hybrid Metropolis–Green function Monte Carlo method which factors the core and valence wave function, and treats the former variationally with accurate trial functions, and the latter exactly in the presence of the variationally evoling core. This allows separation of the relevant core and valence time scales, and has sped up quantum simulations by over three orders of magnitude.

Armin Ahmadzadeh, Hamid Sarbazi‐Azad

Jeongnim Kim, K. Esler, Jeremy McMinis +4 more

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.

Yutaka Okabe, Macoto Kikuchi

Jincheng Zhang

Quantum-Accelerated Monte Carlo Simulation of Complex Systems Quantum-accelerated Monte Carlo simulation represents a significant advancement in computational science, offering the potential to dramatically reduce the time required to simulate complex systems across diverse fields. This paper investigates the design and implementation of a quantum algorithm specifically tailored for accelerating Monte Carlo simulations, leveraging quantum entanglement to enhance efficiency and accuracy. We detail the core mechanism, outlining the strategic use of quantum algorithms to navigate the expansive solution space of these simulations. The paper explores the potential benefits and challenges associated with this approach, highlighting the transformative impact it could have on scientific discovery and technological innovation.