Quantum Monte Carlo for large chemical systems: Implementing efficient strategies for petascale platforms and beyond

TL;DR

This paper presents advanced strategies for implementing large-scale Quantum Monte Carlo simulations on petascale and future exascale platforms, focusing on efficiency, memory management, and load balancing.

Contribution

It introduces a novel algorithm for Slater matrix calculations based on atomic Gaussian basis functions and a universal framework for scalable QMC simulations.

Findings

Successfully ran QMC=Chem on 80,000 cores at petascale

Achieved high computational efficiency on large peptide systems

Demonstrated feasibility for exascale-level QMC simulations

Abstract

Various strategies to implement efficiently QMC simulations for large chemical systems are presented. These include: i.) the introduction of an efficient algorithm to calculate the computationally expensive Slater matrices. This novel scheme is based on the use of the highly localized character of atomic Gaussian basis functions (not the molecular orbitals as usually done), ii.) the possibility of keeping the memory footprint minimal, iii.) the important enhancement of single-core performance when efficient optimization tools are employed, and iv.) the definition of a universal, dynamic, fault-tolerant, and load-balanced computational framework adapted to all kinds of computational platforms (massively parallel machines, clusters, or distributed grids). These strategies have been implemented in the QMC=Chem code developed at Toulouse and illustrated with numerical applications on small peptides of increasing sizes (158, 434, 1056 and 1731 electrons). Using 10k-80k computing cores of the Curie machine (GENCI-TGCC-CEA, France) QMC=Chem has been shown to be capable of running at the petascale level, thus demonstrating that for this machine a large part of the peak performance can be achieved. Implementation of large-scale QMC simulations for future exascale platforms with a comparable level of efficiency is expected to be feasible.